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Li X, Yang Y, Sun G, Dai W, Jie X, Du Y, Huang R, Zhang J. Promising targets and drugs in rheumatoid arthritis: a module-based and cumulatively scoring approach. Bone Joint Res 2020; 9:501-514. [PMID: 32922758 PMCID: PMC7468554 DOI: 10.1302/2046-3758.98.bjr-2019-0301.r1] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
AIMS Rheumatoid arthritis (RA) is a systematic autoimmune disorder, characterized by synovial inflammation, bone and cartilage destruction, and disease involvement in multiple organs. Although numerous drugs are employed in RA treatment, some respond little and suffer from severe side effects. This study aimed to screen the candidate therapeutic targets and promising drugs in a novel method. METHODS We developed a module-based and cumulatively scoring approach that is a deeper-layer application of weighted gene co-expression network (WGCNA) and connectivity map (CMap) based on the high-throughput datasets. RESULTS Four noteworthy RA-related modules were identified, revealing the immune- and infection-related biological processes and pathways involved in RA. HLA-DMA, HLA-DMB, HLA-DPA1, HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DRB1, BLNK, BTK, CD3D, CD4, IL2RG, INPP5D, LCK, PTPRC, RAC2, SYK, and VAV1 were recognized as the key hub genes with high connectivity in gene regulation networks and gene pathway networks. Moreover, the long noncoding RNAs (lncRNAs) in the RA-related modules, such as FAM30A and NEAT1, were identified as the indispensable interactors with the hub genes. Finally, candidate drugs were screened by developing a cumulatively scoring approach based on the selected modules. Niclosamide and the other compounds of T-type calcium channel blocker, IKK inhibitor, and PKC activator, HIF activator, and proteasome inhibitor, which harbour the similar gene signature with niclosamide, were promising drugs with high specificity and broad coverage for the RA-related modules. CONCLUSION This study provides not only the promising targets and drugs for RA but also a novel methodological insight into the target and drug screening.Cite this article: Bone Joint Res 2020;9(8):501-514.
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Affiliation(s)
- Xingyan Li
- Department of Bone and Joint Surgery, The Third Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yejing Yang
- Department of Bone and Joint Surgery, The Third Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Guili Sun
- Department of Nutriology, The Third Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Wanwu Dai
- Department of Bone and Joint Surgery, The Third Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xuri Jie
- Department of Hematology, The Third Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yongjun Du
- Department of Bone and Joint Surgery, The Third Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Runjie Huang
- Second Clinical College, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiaming Zhang
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Uppu S, Krishna A, Gopalan RP. A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:599-612. [PMID: 28060710 DOI: 10.1109/tcbb.2016.2635125] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this era of genome-wide association studies (GWAS), the quest for understanding the genetic architecture of complex diseases is rapidly increasing more than ever before. The development of high throughput genotyping and next generation sequencing technologies enables genetic epidemiological analysis of large scale data. These advances have led to the identification of a number of single nucleotide polymorphisms (SNPs) responsible for disease susceptibility. The interactions between SNPs associated with complex diseases are increasingly being explored in the current literature. These interaction studies are mathematically challenging and computationally complex. These challenges have been addressed by a number of data mining and machine learning approaches. This paper reviews the current methods and the related software packages to detect the SNP interactions that contribute to diseases. The issues that need to be considered when developing these models are addressed in this review. The paper also reviews the achievements in data simulation to evaluate the performance of these models. Further, it discusses the future of SNP interaction analysis.
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Choi H, Song WM, Zhang B. Linking childhood allergic asthma phenotypes with endotype through integrated systems biology: current evidence and research needs. REVIEWS ON ENVIRONMENTAL HEALTH 2017; 32:55-63. [PMID: 28170342 DOI: 10.1515/reveh-2016-0054] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 12/11/2016] [Indexed: 06/06/2023]
Abstract
Asthma and other complex diseases results from a complex web of interactions involving inflammation, immunity, cell cycle, apoptosis, and metabolic perturbations across multiple organ systems. The extent to which various degrees of the age at onset, symptom severity, and the natural progression of the disease reflect multiple disease subtypes, influenced by unique process of development remains unknown. One of the most critical challenges to our understanding stems from incomplete understanding of the mechanisms. Within this review, we focus on the phenotypes of childhood allergic asthma as the basis to better understand the endotype for quantitative define subtypes of asthma. We highlight some of the known mechanistic pathways associated with the key hallmark events before the asthma onset. In particular, we examine how the recent advent of multiaxial -omics technologies and systems biology could help to clarify our current understanding of the pathway. We review how a large volume of molecular, genomic data generated by multiaxial technologies could be digested to identify cogent pathophysiologic molecular networks. We highlight some recent successes in application of these technologies within the context of other disease conditions for therapeutic interventions. We conclude by summarizing the research needs for the predictive value of preclinical biomarkers.
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Zhang M, Mu H, Lv H, Duan L, Shang Z, Li J, Jiang Y, Zhang R. Integrative analysis of genome-wide association studies and gene expression analysis identifies pathways associated with rheumatoid arthritis. Oncotarget 2017; 7:8580-9. [PMID: 26885899 PMCID: PMC4890988 DOI: 10.18632/oncotarget.7390] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Accepted: 01/28/2016] [Indexed: 11/25/2022] Open
Abstract
Rheumatoid arthritis (RA) is a complex and systematic autoimmune disease, which is usually influenced by both genetic and environmental factors. Pathway analyses based on a single data type such as microarray data or SNP data have successfully revealed some biology pathways associated with RA. However, we found that the pathway analysis based on a single data type only provide limited understanding about the pathogenesis of RA. Gene-disease association is usually caused by many ways, such as genotype, gene expression and so on. Therefore, the integrative analysis method combining multiple levels of evidence can more precisely and comprehensively identify the pathway associations. In this study, we performed a pathway analysis by integrating GWAS and gene expression analysis to detect the RA-related pathways. The integrative analysis identified 28 pathways associated with RA. Among these pathways, 18 pathways were also found by both GWAS and gene expression analysis, 7 pathways are novel RA-related pathways, such as B cell receptor signaling pathway, Toll-like receptor signaling pathway, Fc gamma R-mediated phagocytosis and so on. Compared with pathway analyses using only one type genomic data, we found integrative analysis can increase the power to identify the real associations and provided more stable and accurate results. We believe these results will contribute to perform future genetic studies in RA pathogenesis and may promote the development of new therapeutic strategies by targeting these pathways.
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Affiliation(s)
- Mingming Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongbo Mu
- College of Science, Northeast Forestry University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Lian Duan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhenwei Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jin Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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Kim K, Bang SY, Lee HS, Bae SC. Update on the genetic architecture of rheumatoid arthritis. Nat Rev Rheumatol 2016; 13:13-24. [PMID: 27811914 DOI: 10.1038/nrrheum.2016.176] [Citation(s) in RCA: 93] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Human genetic studies into rheumatoid arthritis (RA) have uncovered more than 100 genetic loci associated with susceptibility to RA and have refined the RA-association model for HLA variants. The majority of RA-risk variants are highly shared across multiple ancestral populations and are located in noncoding elements that might have allele-specific regulatory effects in relevant tissues. Emerging multi-omics data, high-density genotype data and bioinformatic approaches are enabling researchers to use RA-risk variants to identify functionally relevant cell types and biological pathways that are involved in impaired immune processes and disease phenotypes. This Review summarizes reported RA-risk loci and the latest insights from human genetic studies into RA pathogenesis, including how genetic data has helped to identify currently available drugs that could be repurposed for patients with RA and the role of genetics in guiding the development of new drugs.
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Affiliation(s)
- Kwangwoo Kim
- Department of Biology, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
| | - So-Young Bang
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, 222-1 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Hye-Soon Lee
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, 222-1 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Sang-Cheol Bae
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, 222-1 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
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Reviews and prospectives of signaling pathway analysis in idiopathic pulmonary fibrosis. Autoimmun Rev 2014; 13:1020-5. [DOI: 10.1016/j.autrev.2014.08.028] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2014] [Accepted: 05/30/2014] [Indexed: 12/15/2022]
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Mooney MA, Nigg JT, McWeeney SK, Wilmot B. Functional and genomic context in pathway analysis of GWAS data. Trends Genet 2014; 30:390-400. [PMID: 25154796 DOI: 10.1016/j.tig.2014.07.004] [Citation(s) in RCA: 86] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Revised: 07/18/2014] [Accepted: 07/18/2014] [Indexed: 02/07/2023]
Abstract
Gene set analysis (GSA) is a promising tool for uncovering the polygenic effects associated with complex diseases. However, the available techniques reflect a wide variety of hypotheses about how genetic effects interact to contribute to disease susceptibility. The lack of consensus about the best way to perform GSA has led to confusion in the field and has made it difficult to compare results across methods. A clear understanding of the various choices made during GSA - such as how gene sets are defined, how single-nucleotide polymorphisms (SNPs) are assigned to genes, and how individual SNP-level effects are aggregated to produce gene- or pathway-level effects - will improve the interpretability and comparability of results across methods and studies. In this review we provide an overview of the various data sources used to construct gene sets and the statistical methods used to test for gene set association, as well as provide guidelines for ensuring the comparability of results.
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Affiliation(s)
- Michael A Mooney
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; OHSU Knight Cancer Institute, Portland, OR, USA
| | - Joel T Nigg
- Division of Psychology, Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA; Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - Shannon K McWeeney
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; Oregon Clinical and Translational Research Institute, Portland, OR, USA; OHSU Knight Cancer Institute, Portland, OR, USA.
| | - Beth Wilmot
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; Oregon Clinical and Translational Research Institute, Portland, OR, USA; OHSU Knight Cancer Institute, Portland, OR, USA
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Pathway-based association analysis of two genome-wide screening data identifies rheumatoid arthritis-related pathways. Genes Immun 2014; 15:487-94. [DOI: 10.1038/gene.2014.48] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Revised: 05/06/2014] [Accepted: 06/23/2014] [Indexed: 12/26/2022]
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Bunyavanich S, Schadt EE, Himes BE, Lasky-Su J, Qiu W, Lazarus R, Ziniti JP, Cohain A, Linderman M, Torgerson DG, Eng CS, Pino-Yanes M, Padhukasahasram B, Yang JJ, Mathias RA, Beaty TH, Li X, Graves P, Romieu I, Navarro BDR, Salam MT, Vora H, Nicolae DL, Ober C, Martinez FD, Bleecker ER, Meyers DA, Gauderman WJ, Gilliland F, Burchard EG, Barnes KC, Williams LK, London SJ, Zhang B, Raby BA, Weiss ST. Integrated genome-wide association, coexpression network, and expression single nucleotide polymorphism analysis identifies novel pathway in allergic rhinitis. BMC Med Genomics 2014; 7:48. [PMID: 25085501 PMCID: PMC4127082 DOI: 10.1186/1755-8794-7-48] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2014] [Accepted: 06/04/2014] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Allergic rhinitis is a common disease whose genetic basis is incompletely explained. We report an integrated genomic analysis of allergic rhinitis. METHODS We performed genome wide association studies (GWAS) of allergic rhinitis in 5633 ethnically diverse North American subjects. Next, we profiled gene expression in disease-relevant tissue (peripheral blood CD4+ lymphocytes) collected from subjects who had been genotyped. We then integrated the GWAS and gene expression data using expression single nucleotide (eSNP), coexpression network, and pathway approaches to identify the biologic relevance of our GWAS. RESULTS GWAS revealed ethnicity-specific findings, with 4 genome-wide significant loci among Latinos and 1 genome-wide significant locus in the GWAS meta-analysis across ethnic groups. To identify biologic context for these results, we constructed a coexpression network to define modules of genes with similar patterns of CD4+ gene expression (coexpression modules) that could serve as constructs of broader gene expression. 6 of the 22 GWAS loci with P-value ≤ 1x10-6 tagged one particular coexpression module (4.0-fold enrichment, P-value 0.0029), and this module also had the greatest enrichment (3.4-fold enrichment, P-value 2.6 × 10-24) for allergic rhinitis-associated eSNPs (genetic variants associated with both gene expression and allergic rhinitis). The integrated GWAS, coexpression network, and eSNP results therefore supported this coexpression module as an allergic rhinitis module. Pathway analysis revealed that the module was enriched for mitochondrial pathways (8.6-fold enrichment, P-value 4.5 × 10-72). CONCLUSIONS Our results highlight mitochondrial pathways as a target for further investigation of allergic rhinitis mechanism and treatment. Our integrated approach can be applied to provide biologic context for GWAS of other diseases.
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Affiliation(s)
- Supinda Bunyavanich
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 10029 New York, NY, USA
- Division of Pediatric Allergy and Immunology, Department of Pediatrics, and Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 10029 New York, NY, USA
| | - Blanca E Himes
- Channing Division of Network Medicine, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Weiliang Qiu
- Channing Division of Network Medicine, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Ross Lazarus
- Channing Division of Network Medicine, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Medical Bioinformatics, Baker IDI, Melbourne, Australia
| | - John P Ziniti
- Channing Division of Network Medicine, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Ariella Cohain
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 10029 New York, NY, USA
| | - Michael Linderman
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 10029 New York, NY, USA
| | - Dara G Torgerson
- Department of Medicine and Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Celeste S Eng
- Department of Medicine and Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Maria Pino-Yanes
- Department of Medicine and Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
- IBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - Badri Padhukasahasram
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, MI, USA
| | - James J Yang
- Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Rasika A Mathias
- Departments of Medicine and Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | - Terri H Beaty
- Departments of Medicine and Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | - Xingnan Li
- Center for Genomics, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Penelope Graves
- Arizona Respiratory Center and BIO5 Institute, University of Arizona, Tucson, AZ, USA
| | | | | | - M Towhid Salam
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hita Vora
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Dan L Nicolae
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Fernando D Martinez
- Arizona Respiratory Center and BIO5 Institute, University of Arizona, Tucson, AZ, USA
| | - Eugene R Bleecker
- Center for Genomics, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Deborah A Meyers
- Center for Genomics, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - W James Gauderman
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Frank Gilliland
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Esteban G Burchard
- Department of Medicine and Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Kathleen C Barnes
- Departments of Medicine and Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | - L Keoki Williams
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, MI, USA
- Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Stephanie J London
- Division of Intramural Research, Department of Health and Human Services, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle, Park, NC, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 10029 New York, NY, USA
| | - Benjamin A Raby
- Channing Division of Network Medicine, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, MA, USA
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