Differential DNA Methylation of Networked Signaling, Transcriptional, Innate and Adaptive Immunity, and Osteoclastogenesis Genes and Pathways in Gout

• Published in: Arthritis & rheumatology (Hoboken, N.J.) N.J.), 2020-05, Vol.72 (5), p.802-814
• Main Authors: Wang, Zengmiao, Zhao, Ying, Phipps‐Green, Amanda, Liu‐Bryan, Ru, Ceponis, Arnoldas, Boyle, David L., Wang, Jun, Merriman, Tony R., Wang, Wei, Terkeltaub, Robert
• Format: Article
• Language: English
• Subjects: Adaptive Immunity - genetics; AKT protein; AMP; Arthritis; Beta cells; Biomarkers; Biomedical materials; Blood; Cell differentiation; Circadian rhythms; Cluster analysis; Clustering; Cohort Studies; Crystals; Data analysis; Deoxyribonucleic acid; Differentiation (biology); DNA; DNA methylation; DNA Methylation - physiology; Enrichment; Entrainment; Female; Flares; Gene Regulatory Networks; Genes; Genetic testing; Gout; Gout - genetics; Gout - immunology; Granuloma; Growth factors; Helper cells; Humans; Immunity; Immunity, Innate - genetics; Insulin; Interleukins; Kinases; Leukocytes, Mononuclear; Lymphocytes; Lymphocytes T; Male; Osteoclastogenesis; Osteogenesis - genetics; Peripheral blood mononuclear cells; Phagocytosis; Protein kinase; Rapamycin; Receptors; Rheumatism; Signal transduction; Signal Transduction - genetics; Signaling; Synovitis; T cell receptors; Therapeutic applications; TOR protein; Transcription, Genetic
• ISSN: 2326-5191; 2326-5205
• DOI: 10.1002/art.41173

Objective In gout, autoinflammatory responses to urate crystals promote acute arthritis flares, but the pathogeneses of tophi, chronic synovitis, and erosion are less well understood. Defining the pathways of epigenomic immunity training can reveal novel pathogenetic factors and biomarkers. The present study was undertaken to seminally probe differential DNA methylation patterns utilizing epigenome‐wide analyses in patients with gout. Methods Peripheral blood mononuclear cells (PBMCs) were obtained from a San Diego cohort of patients with gout (n = 16) and individually matched healthy controls (n = 14). PBMC methylome data were processed with ChAMP package in R. ENCODE data and Taiji data analysis software were used to analyze transcription factor (TF)–gene networks. As an independent validation cohort, whole blood DNA samples from New Zealand Māori subjects (n = 13 patients with gout, n = 16 control subjects without gout) were analyzed. Results Differentially methylated loci clearly separated gout patients from controls, as determined by hierarchical clustering and principal components analyses. IL23R, which mediates granuloma formation and cell invasion, was identified as one of the multiple differentially methylated gout risk genes. Epigenome‐wide analyses revealed differential methylome pathway enrichment for B and T cell receptor signaling, Th17 cell differentiation and interleukin‐17 signaling, convergent longevity regulation, circadian entrainment, and AMP‐activated protein kinase signaling, which are pathways that impact inflammation via insulin‐like growth factor 1 receptor, phosphatidylinositol 3‐kinase/Akt, NF‐κB, mechanistic target of rapamycin signaling, and autophagy. The gout cohorts overlapped for 37 (52.9%) of the 70 TFs with hypomethylated sequence enrichment and for 30 (78.9%) of the 38 enriched KEGG pathways identified via TFs. Evidence of shared differentially methylated gout TF‐gene networks, including the NF‐κB activation–limiting TFs MEF2C and NFATC2, pointed to osteoclast differentiation as the most strongly weighted differentially methylated pathway that overlapped in both gout cohorts. Conclusion These findings of differential DNA methylation of networked signaling, transcriptional, innate and adaptive immunity, and osteoclastogenesis genes and pathways suggest that they could serve as novel therapeutic targets in the management of flares, tophi, chronic synovitis, and bone erosion in patients with gout.