Background/Purpose: Systemic lupus erythematosus (SLE) is a multi-organ autoimmune disorder with a prominent genetic component. Evidence has shown that individuals of African-Ancestry (AA) experience the disease more severely and with an increased co-morbidity burden compared to European-Ancestry (EA) populations. However, the relationship between genetics, molecular pathways, and disease severity has not been fully delineated. Here, we examined AA and EA SLE-associated single nucleotide polymorphisms (SNPs) and linked them via expression quantitative trait loci (eQTL) across multiple tissues to genes with altered expression (E-Genes). Putative EA and AA E-Gene signatures were coupled with SLE differential expression (DE) datasets and upstream regulators to map candidate molecular pathways. Together, these genetic and gene expression analyses enable a better understanding of how the identified SNPs could contribute to aberrant immune function as well as the influence of ancestry on the genetic basis of SLE.

Methods: A previous SLE Immunochip study (Langefeld et al., 2017) identified SNPs significantly associated with SLE in AA (2,970 cases; 2,452 controls) and EA (6,748 cases; 11,516 controls) cohorts. eQTL mapping identified E-Genes from SLE SNPs and their ancestry-specific SNP proxies (based on linkage disequilibrium) via the GTEx database. For both ancestral groups, E-Gene lists were examined for the significant enrichment of gene ontogeny (GO) terms, canonical IPA^® (Qiagen) pathways and BIG-C™ categories. Next, we analyzed the gene expression profiles of predicted E-Genes across multiple SLE DE datasets, including those from blood and multiple tissues. Differential expressed genes (DEGs) were identified and subjected to pathway analysis with IPA^®, clustering using MCODE and visualization in Cytoscape with the ClusterMaker2 plugin. Drug candidates targeting E-Genes, DEGs and upstream regulators (UPRs) were identified using CLUE, IPA^® and STITCH.

Results: A total of 908 Immunochip SNPs were mapped to 252 eQTLs and coupled to 760 E-Genes (207 in EAs, 30 in AAs, 523 shared; Figure 1A). Shared E-Genes were highly enriched in interferon signaling, whereas EA E-Genes were associated with nucleotide degradation and AA E-Genes were linked to multiple biosynthesis and intracellular signaling pathways (e.g., retinol biosynthesis and AMPK signaling). Protein-protein interaction (PPI) networks of clustered EA, AA and shared E-Genes illustrate the high degree of ancestral overlap evident within each E-Gene set (Figure 1B). Clustering analysis of all DE E-Genes and IPA-predicted UPRs highlight disease-associated pathways that are both shared and ancestry-specific. Drug candidate comparison identified a total of 115 drugs targeting EA, AA and shared E-Genes and their molecular pathways.

Conclusion: Using a bioinformatics-based approach that utilizes pathway analysis and gene expression data, we were able to discover novel ancestry-dependent and ancestry-agnostic candidate causal targets in SLE and couple those with drug discovery tools to identify new therapies with the potential to impact disease processes within and across specific populations.  

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/the-integration-of-genetic-data-molecular-pathway-analysis-and-differential-expression-to-delineate-the-impact-of-ancestral-differences-on-lupus/