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Ma Q, Augusto DG, Montero-Martin G, Caillier SJ, Osoegawa K, Cree BAC, Hauser SL, Didonna A, Hollenbach JA, Norman PJ, Fernandez-Vina M, Oksenberg JR. High-resolution DNA methylation screening of the major histocompatibility complex in multiple sclerosis. Front Neurol 2023; 14:1326738. [PMID: 38145128 PMCID: PMC10739394 DOI: 10.3389/fneur.2023.1326738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 11/23/2023] [Indexed: 12/26/2023] Open
Abstract
Background The HLA-DRB1 gene in the major histocompatibility complex (MHC) region in chromosome 6p21 is the strongest genetic factor identified as influencing multiple sclerosis (MS) susceptibility. DNA methylation changes associated with MS have been consistently detected at the MHC region. However, understanding the full scope of epigenetic regulations of the MHC remains incomplete, due in part to the limited coverage of this region by standard whole genome bisulfite sequencing or array-based methods. Methods We developed and validated an MHC capture protocol coupled with bisulfite sequencing and conducted a comprehensive analysis of the MHC methylation landscape in blood samples from 147 treatment naïve MS study participants and 129 healthy controls. Results We identified 132 differentially methylated region (DMRs) within MHC region associated with disease status. The DMRs overlapped with established MS risk loci. Integration of the MHC methylome with human leukocyte antigen (HLA) genetic data indicate that the methylation changes are significantly associated with HLA genotypes. Using DNA methylation quantitative trait loci (mQTL) mapping and the causal inference test (CIT), we identified 643 cis-mQTL-DMRs paired associations, including 71 DMRs possibly mediating causal relationships between 55 single nucleotide polymorphisms (SNPs) and MS risk. Results The results describe MS-associated methylation changes in MHC region and highlight the association between HLA genotypes and methylation changes. Results from the mQTL and CIT analyses provide evidence linking MHC region variations, methylation changes, and disease risk for MS.
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Affiliation(s)
- Qin Ma
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Danillo G. Augusto
- Department of Biological Sciences, University of North Carolina at Charlotte, Charlotte, NC, United States
| | - Gonzalo Montero-Martin
- Histocompatibility and Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, United States
- Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, United States
- HLA Histocompatibility and Immunogenetics Laboratory, Vitalant, Phoenix, AZ, United States
| | - Stacy J. Caillier
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Kazutoyo Osoegawa
- Histocompatibility and Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, United States
| | - Bruce A. C. Cree
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Stephen L. Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Alessandro Didonna
- Department of Anatomy and Cell Biology, Brody School of Medicine, East Carolina University, Greenville, NC, United States
| | - Jill A. Hollenbach
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Paul J. Norman
- Department of Biomedical Informatics and Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Marcelo Fernandez-Vina
- Histocompatibility and Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, United States
- Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Jorge R. Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
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Islam T, Rezanur Rahman M, Khan A, Ali Moni M. Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke. J Biomed Inform 2023; 141:104345. [PMID: 36958462 DOI: 10.1016/j.jbi.2023.104345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 02/04/2023] [Accepted: 03/16/2023] [Indexed: 03/25/2023]
Abstract
Stroke is the second largest cause of mortality in the world. Genome-wide association studies (GWAS) have identified some genetic variants associated with stroke risk, but their putative functional causal genes are unknown. Hence, we aimed to identify putative functional causal gene biomarkers of stroke risk. We used a summary-based Mendelian randomisation (SMR) approach to identify the pleiotropic associations of genetically regulated traits (i.e., gene expression and DNA methylation) with stroke risk. Using SMR approach, we integrated cis-expression quantitative loci (cis-eQTLs) and cis-methylation quantitative loci (cis-mQTLs) data with GWAS summary statistics of stroke. We also utilised heterogeneity in dependent instruments (HEIDI) test to distinguish pleiotropy from linkage from the observed associations identified through SMR analysis. Our integrative SMR analyses and HEIDI test revealed 45 candidate biomarker genes (FDR < 0.05; PHEIDI>0.01) that were pleiotropically or potentially causally associated with stroke risk. Of those candidate biomarker genes, 10 genes (HTRA1, PMF1, FBN2, C9orf84, COL4A1, BAG4, NEK6, SH2B3, SH3PXD2A, ACAD10) were differentially expressed in genome-wide blood transcriptomics data from stroke and healthy individuals (FDR<0.05). Functional enrichment analysis of the identified candidate biomarker genes revealed gene ontologies and pathways involved in stroke, including "cell aging", "metal ion binding" and "oxidative damage". Based on the evidence of genetically regulated expression of genes through SMR and directly measured expression of genes in blood, our integrative analysis suggests ten genes as blood biomarkers of stroke risk. Furthermore, our study provides a better understanding of the influence of DNA methylation on the expression of genes linked to stroke risk.
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Affiliation(s)
- Tania Islam
- School of Health and Rehabilitation Sciences, Faculty of Health, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Md Rezanur Rahman
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Asaduzzaman Khan
- School of Health and Rehabilitation Sciences, Faculty of Health, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, Faculty of Health, The University of Queensland, Brisbane, QLD 4072, Australia.
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