Shared and Unique Patterns of DNA Methylation in Systemic Lupus Erythematosus and Primary Sjögren's Syndrome.

Imgenberg-Kreuz J, Almlöf JC, Leonard D, Sjöwall C, Syvänen AC, Rönnblom L, Sandling JK, Nordmark G

Front Immunol 10 (-) 1686 [2019-07-30; online 2019-07-30]

Objectives: To perform a cross-comparative analysis of DNA methylation in patients with systemic lupus erythematosus (SLE), patients with primary Sjögren's syndrome (pSS), and healthy controls addressing the question of epigenetic sharing and aiming to detect disease-specific alterations. Methods: DNA extracted from peripheral blood from 347 cases with SLE, 100 cases with pSS, and 400 healthy controls were analyzed on the Human Methylation 450k array, targeting 485,000 CpG sites across the genome. A linear regression model including age, sex, and blood cell type distribution as covariates was fitted, and association p-values were Bonferroni corrected. A random forest machine learning classifier was designed for prediction of disease status based on DNA methylation data. Results: We established a combined set of 4,945 shared differentially methylated CpG sites (DMCs) in SLE and pSS compared to controls. In pSS, hypomethylation at type I interferon induced genes was mainly driven by patients who were positive for Ro/SSA and/or La/SSB autoantibodies. Analysis of differential methylation between SLE and pSS identified 2,244 DMCs with a majority of sites showing decreased methylation in SLE compared to pSS. The random forest classifier demonstrated good performance in discerning between disease status with an area under the curve (AUC) between 0.83 and 0.96. Conclusions: The majority of differential DNA methylation is shared between SLE and pSS, however, important quantitative differences exist. Our data highlight neutrophil dysregulation as a shared mechanism, emphasizing the role of neutrophils in the pathogenesis of systemic autoimmune diseases. The current study provides evidence for genes and molecular pathways driving common and disease-specific pathogenic mechanisms.

Bioinformatics Support for Computational Resources [Service]

NGI Uppsala (SNP&SEQ Technology Platform) [Service]

National Genomics Infrastructure [Service]

PubMed 31428085

DOI 10.3389/fimmu.2019.01686

Crossref 10.3389/fimmu.2019.01686

pmc: PMC6688520

Publications 9.5.0