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Defining the pathways of epigenomic immunity training can reveal novel pathogenetic factors and biomarkers. The present study was undertaken to seminally probe differential\n                      <jats:styled-content style=\"fixed-case\">DNA<\/jats:styled-content>\n                      methylation patterns utilizing epigenome\u2010wide analyses in patients with gout.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Peripheral blood mononuclear cells (\n                      <jats:styled-content style=\"fixed-case\">PBMC<\/jats:styled-content>\n                      s) were obtained from a San Diego cohort of patients with gout (n = 16) and individually matched healthy controls (n = 14).\n                      <jats:styled-content style=\"fixed-case\">PBMC<\/jats:styled-content>\n                      methylome data were processed with Ch\n                      <jats:styled-content style=\"fixed-case\">AMP<\/jats:styled-content>\n                      package in R.\n                      <jats:styled-content style=\"fixed-case\">ENCODE<\/jats:styled-content>\n                      data and Taiji data analysis software were used to analyze transcription factor (\n                      <jats:styled-content style=\"fixed-case\">TF<\/jats:styled-content>\n                      )\u2013gene networks. As an independent validation cohort, whole blood\n                      <jats:styled-content style=\"fixed-case\">DNA<\/jats:styled-content>\n                      samples from New Zealand M\u0101ori subjects (n = 13 patients with gout, n = 16 control subjects without gout) were analyzed.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Differentially methylated loci clearly separated gout patients from controls, as determined by hierarchical clustering and principal components analyses.\n                      <jats:italic>\n                        <jats:styled-content style=\"fixed-case\">IL<\/jats:styled-content>\n                        23R\n                      <\/jats:italic>\n                      , which mediates granuloma formation and cell invasion, was identified as one of the multiple differentially methylated gout risk genes. Epigenome\u2010wide analyses revealed differential methylome pathway enrichment for B and T cell receptor signaling, Th17 cell differentiation and interleukin\u201017 signaling, convergent longevity regulation, circadian entrainment, and\n                      <jats:styled-content style=\"fixed-case\">AMP<\/jats:styled-content>\n                      \u2010activated protein kinase signaling, which are pathways that impact inflammation via insulin\u2010like growth factor 1 receptor, phosphatidylinositol 3\u2010kinase\/Akt,\n                      <jats:styled-content style=\"fixed-case\">NF<\/jats:styled-content>\n                      \u2010\u03baB, mechanistic target of rapamycin signaling, and autophagy. The gout cohorts overlapped for 37 (52.9%) of the 70\n                      <jats:styled-content style=\"fixed-case\">TF<\/jats:styled-content>\n                      s with hypomethylated sequence enrichment and for 30 (78.9%) of the 38 enriched\n                      <jats:styled-content style=\"fixed-case\">KEGG<\/jats:styled-content>\n                      pathways identified via\n                      <jats:styled-content style=\"fixed-case\">TF<\/jats:styled-content>\n                      s. Evidence of shared differentially methylated gout\n                      <jats:styled-content style=\"fixed-case\">TF<\/jats:styled-content>\n                      \u2010gene networks, including the\n                      <jats:styled-content style=\"fixed-case\">NF<\/jats:styled-content>\n                      \u2010\u03baB activation\u2013limiting\n                      <jats:styled-content style=\"fixed-case\">TF<\/jats:styled-content>\n                      s\n                      <jats:italic>\n                        <jats:styled-content style=\"fixed-case\">MEF<\/jats:styled-content>\n                        2C and\n                        <jats:styled-content style=\"fixed-case\">NFATC<\/jats:styled-content>\n                        2\n                      <\/jats:italic>\n                      , pointed to osteoclast differentiation as the most strongly weighted differentially methylated pathway that overlapped in both gout cohorts.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      These findings of differential\n                      <jats:styled-content style=\"fixed-case\">DNA<\/jats:styled-content>\n                      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.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/art.41173","type":"journal-article","created":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T08:29:51Z","timestamp":1574065791000},"page":"802-814","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Differential\n                    <scp>DNA<\/scp>\n       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