Background/Purpose:

Systemic lupus erythematosus (SLE) is a multi-organ autoimmune disorder with an important genetic component. Genome-wide association studies (GWAS) have linked many single nucleotide polymorphisms (SNPs) to SLE. Recently, Langefeld et al (2017) conducted a large-scale transancestral association study of SLE to identify ancestry-dependent and independent contributions to SLE risk. Here, we take African ancestry (AA) and European ancestry (EA) SLE-associated SNPs, link them to E-Genes and couple those with differentially expressed (DE) genes from SLE patients to develop a better understanding of ancestral-related molecular pathways and novel treatments unique to each ancestral group.

Methods:

The GTEx database was used to identify E-Genes from SLE-associated SNPs and their ancestry-specific SNP proxies. Ancestry-specific E-Genes were compared to DE genes from multiple SLE gene expression datasets. For both ancestral groups, E-Gene lists were examined for the significant enrichment of gene ontogeny (GO) terms, IPA and BIG-C categories, and to predict whether E-Genes were upstream regulators (UPRs). For visualization and clustering analysis, STRING-generated networks of DE E-Genes were imported into Cytoscape and partitioned with the community clustering (GLay) algorithm via the ClusterMaker2 plugin. Drug candidates targeting E-Genes, DE genes and UPRs were identified using CLUE, REST, API, IPA and STITCH.

Results:

Newly predicted E-Genes from the GTEx database were pooled by ancestry. We identified 52 AA-associated and 260 EA-associated SNPs; 1 SNP was shared across ancestries. These SNPs identified 891 distinct E-Genes, which were then compared to SLE DE datasets. We observed differential expression of 516 EA-associated E-Genes enriched in estrogen receptor signaling, neuronal signaling and cholesterol biosynthesis via IPA, and the positive regulation of synaptic transmission by GO-term enrichment. Clustering analysis showed EA E-Gene networks dominated by transcription, RNA processing, glycolysis and immune signaling. Drug candidate comparison identified 77 EA-specific drugs, including hydroxychloroquine and drugs targeting CD40LG and CXCR1/2. For AA, 48 E-Genes were DE in SLE and enriched in IPA categories for T helper cell differentiation and melatonin biosynthesis, and the GO biological process of keratinization. Clustering analysis of the AA E-Genes showed enrichment in PRRs, immune signaling and keratinocyte differentiation. AA-specific drug candidates included HDAC inhibitors, retinoids and inhibitors of IRAK4 and MAP8K4. A total of 46 DE E-Genes were shared between ancestries, with IRF7 upregulated in all SLE datasets. Drugs targeting shared E-Genes included ibrutinib, ruxolitinib and ustekinumab.

Conclusion:

The ancestral SNP-associated E-Genes and gene expression profiles outlined here illustrate fundamental differences in lupus molecular pathways between AA and EA. The results indicate that unique sets of drugs may be particularly effective at treating lupus within each ancestral group.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/genome-wide-association-studies-in-sle-predict-e-genes-and-gene-expression-patterns-that-inform-ancestral-specific-molecular-pathways-and-targeted-therapies/