BD-01 E-genes identified via transancestral SNP mapping and gene expression analysis reveal novel targeted therapies for african-american and european-american SLE patients. (August 2018)

• Record Type: Journal Article
• Authors: Owen, Katherine A; Aidukaitis, Bryce N; Labonte, Adam C; Catalina, Michelle D; Bachali, Prathyusha; Dittman, James; Geraci, Nicholas; Rouffa, Sean; Ainsworth, Hannah C; Marion, Miranda C; Howard, Timothy D; Langefeld, Carl D; Lipsky, Peter E; Grammer, Amrie C
• Is Part Of: Lupus science & medicine. Volume 5(2018)Supplement 2
• Page Start: A12
• Page End: A13
• Publication Date: 2018-08
• Subjects: Systemic lupus erythematosus -- Periodicals; 616.772005
• DOI: 10.1136/lupus-2018-lsm.25
• Languages: English
• ISSNs: 2398-8851

Abstract

Abstract : Background: Systemic lupus erythematosus (SLE) in African-Americans (AA) is more prevalent, more severe and associated with an increased burden of co-morbidities compared to European-American (EA) populations. Genome-wide association studies (GWAS) have linked many single nucleotide polymorphisms (SNPs) to SLE. Recently, Langefeld (2017) conducted a large-scale transancestral association study of SLE to identify ancestry-dependent and independent contributions to SLE risk. We extend these findings to include a transancestral analysis linking SLE-associated SNPs to candidate-causal E-Genes specific to AA and EA populations and differential gene expression in these populations with the goal of matching genetic and genomic disease characteristics with available treatments unique to each ancestral group. Methods: SNPs proxy to SLE-associated SNPs were compared with known expression quantitative trait loci (eQTLs) contained in the GTEx (version 6) database. E-QTLs and their associated E-Genes were divided by ancestry and compared to differentially expressed (DE) genes from multiple SLE gene expression datasets. For both ancestral groups, E-Gene lists were examined for the significant enrichment of BIG-C categories and IPA (Qiagen) Canonical Pathways to predict novel upstream regulators (UPRs). For visualization and clustering analysis, STRING-generated networks of DE E-Genes were imported into Cytoscape (version 3.6.1) and partitioned with the community clustering (GLay) algorithm via the clusterMaker2 (version 1.2.1) plugin. Finally, drug candidates targeting E-Genes, DE genes and UPRs were identified using CLUE, REST, API, IPA and STITCH (version 5.0; http://stitch.embl.de). The process of unpacking an SLE-associated SNP is shown in figure 1. Results: E-QTL and DE gene queries of GTEx were combined and newly predicted E-Genes were pooled by ancestry. Here, we identify 52 SNPs with eQTLs unique to AA ancestry, 260 SNPs unique to EA ancestry and 1 SNP shared between ancestries. Together, these SNPs identified a total of 891 distinct E-Genes associated with both ancestral groups. In studies comparing E-Genes to SLE DE data sets, 516 EA E-Genes were differential expressed compared to 48 AA E-Genes. Comparison with various drug candidate databases resulted in the identification of 12 drugs targeting genes specific for AA, 77 drugs specific for EA genes and 13 shared between EA and AA. Predicted EA-specific drugs include hydroxychloroquine and drugs-in-development targeting CD40LG, CXCR1 and CXCR2 whereas AA-specific drugs include HDAC inhibitors, retinoids, and drugs targeting IRAK4 and CTLA4. Drugs targeting E-Genes/pathways shared by EA and AA include ibrutinib, ruxolitinib and ustekinumab. Conclusions: The ancestral SNP-associated E-Genes and gene expression profiles outlined here illustrate fundamental differences in lupus molecular pathways between AA and EA. Our results indicate that unique sets of drugs may be particularly effective at treating lupus within each ancestral group. Acknowledgments: Financial support for this research was provided by RILITE Research Institute.