149 Network-based analysis of clinical and molecular data in a multiethnic lupus cohort identifies molecular associations with serological manifestations. (April 2019)

• Record Type: Journal Article
• Authors: Paranjpe, Ishan; Lanata, Cristina; Nititham, Joanne; Taylor, Kimberly; Gianfrancesco, Milena A; Paranjpe, Manish; Andrews, Shan; Chung, Sharon; Rhead, Brooke; Barcellos, Lisa; Trupin, Laura; Katz, Patricia; DallEra, Maria C; Yazdany, Jinoos; Sirota, Marina; Criswell, Lindsey
• Is Part Of: Lupus science & medicine. Volume 6(2019)supplement 1
• Page Start: A107
• Page End: A108
• Publication Date: 2019-04
• Subjects: Systemic lupus erythematosus -- Periodicals; 616.772005
• DOI: 10.1136/lupus-2019-lsm.149
• Languages: English
• ISSNs: 2398-8851

Abstract

Abstract : Background: Systemic Lupus Erythematous (SLE) is a chronic autoimmune disease with heterogeneous disease manifestations and outcomes. Previous work has found associations between DNA methylation at specific CpG sites and lupus nephritis, serologies, and SLEDAI score. However, these methods examine single CpGs and do not capture the full biological complexity. Using an integrative network-based approach, we aim to define how DNA methylation and genetic variation underlie this clinical heterogeneity in a well-phenotyped multiethnic cohort of SLE patients. Methods: 333 SLE participants from diverse ethnic backgrounds were recruited as part of this study. From peripheral blood, DNA methylation was measured using the Illumina EPIC Beadchip and single nucleotide polymorphism (SNP) genotype data was generated on the Affymetrix LAT1 World Array. Weighted gene correlation network analysis (WGCNA) was applied to the DNA methylation data. The resulting CpG networks were associated with relevant SLE clinical features ina multivariate linear regression model adjusting for population stratification, cell composition, sex, smoking history, medications. Results: We identified one WGCNA CpG module significantly associated with SLEDAI score, anti-Sm, and anti-dsDNA serologies (FDR<0.05). This network consisted of 303 CpGs and was hypomethylated in patients with higher SLEDAI scores and positive anti-Sm and anti-dsDNA serologies. Pathway analysis of this module revealed significant enrichment of genes in the Type I interferon pathway. We also performed a cis-meQTL analysis to determine whether any of the network CpGs were under genetic control. Of the 303 CpGs in the network, 54 CpGs were under proximal genetic control (FDR<0.01), suggesting that specific genetic variants play a role in epigenetic regulation of interferon related gene expression in the context of SLE autoantibody production. Conclusions: Overall, we performed a network-based analysis of DNA methylation and identified a network of CpGs significantly associated with SLE serologies and SLEDAI score. Our approach identifies large-scale epigenetic remodeling that drives SLE pathology rather than single CpG associations as in previous studies that may be influenced by stochastic variation. By applying an integrative computational approach, our method serves to reveal the epigenetic and genetic role of the Type I interferon pathway in SLE. Funding Source(s): This study was funded through the following grants: P30 AR070155 (MS, IP, LC), P60AR053308 (LC), K01LM012381 (MS), F32 AR070585 NIAMS (MG), U01DP005120 CDC (LC, CL, JY, MD, LT, PK), the Rheumatology Research Foundation 128849A (CL), and the Lupus Research Alliance (LC).