Multi-omics integration reveals molecular networks and regulators of psoriasis

Abstract

Background: Psoriasis is a complex multi-factorial disease, involving both genetic susceptibilities and environmental triggers. Genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have been carried out to identify genetic and epigenetic variants that are associated with psoriasis. However, these loci cannot fully explain the disease pathogenesis.

Methods: To achieve a comprehensive mechanistic understanding of psoriasis, we conducted a systems biology study, integrating multi-omics datasets including GWAS, EWAS, tissue-specific transcriptome, expression quantitative trait loci (eQTLs), gene networks, and biological pathways to identify the key genes, processes, and networks that are genetically and epigenetically associated with psoriasis risk.

Results: This integrative genomics study identified both well-characterized (e.g., the IL17 pathway in both GWAS and EWAS) and novel biological processes (e.g., the branched chain amino acid catabolism process in GWAS and the platelet and coagulation pathway in EWAS) involved in psoriasis. Finally, by utilizing tissue-specific gene regulatory networks, we unraveled the interactions among the psoriasis-associated genes and pathways in a tissue-specific manner and detected potential key regulatory genes in the psoriasis networks.

Conclusions: The integration and convergence of multi-omics signals provide deeper and comprehensive insights into the biological mechanisms associated with psoriasis susceptibility.

Keywords: EWAS; GWAS; Integrative genomics; Psoriasis; Systems biology.

Fig. 1

Flowchart of the study. The integrative genomic approach leverages multiple genetic and genomic datasets to uncover the mechanisms of psoriasis. The data types included are psoriasis GWAS, EWAS, gene expression profiles of human psoriatic and normal skins (Additional file 1: Table S1), tissue-specific eQTLs from skin and blood, gene regulatory networks from skin and blood, and biological pathways. The framework can be roughly divided into five steps. First, we constructed data-driven co-expression networks and curated knowledge-driven pathways. These serve as gene sets containing genes with functional relevance and relationships. Second, GWAS and EWAS of psoriasis were integrated with the gene sets using Marker Set Enrichment Analysis (MSEA) to identify genetically (via GWAS) and epigenetically (via EWAS) perturbed pathways. Third, we identified the converging psoriasis pathways from both GWAS and EWAS and merged them into independent supersets. Fourth, Bayesian gene regulatory networks were integrated with the psoriasis-associated supersets to determine key driver (KD) genes based on network topology. Finally, the KD genes and their subnetworks were cross-validated using multiple in silico methods. GIANT: Genome-scale Integrated Analysis of Networks in Tissues, the experimental details of the GIANT interface can be found in [25]

Fig. 2

Comparison of significant pathways between GWAS and EWAS. Panels A-D represent significant canonical pathways/coexpression modules from Biocarta (a), Reactome (b), KEGG (c), and coexpression networks (d), respectively, that are associated with in psoriasis in GWAS and EWAS. The detailed MSEA results in GWAS and EWAS can be found in Additional file 1: Tables S5 and S6. The pathways are derived from various databases including Biocarta, Reactome, and KEGG, and were intersected with GWAS or EWAS using our MSEA procedure to identify pathways whose genes contain genetic or epigenetic variants showing coordinated association with psoriasis in GWAS or EWAS. “BCAA biosynthesis” stands for branched chain amino acids biosynthesis. In Additional file 1: Table S5, the corresponding full pathway name is “valine, leucine, and isoleucine biosynthesis”, where valine, leucine, and isoleucine are BCAAs

Fig. 3

Tissue-specific gene regulatory network of the top KDs in psoriasis. Panel (a) and (b) show the first level skin (a) and blood (b) subnetworks for top KDs derived from wKDA. The genes are colored according to the common processes associated with psoriasis in both GWAS and EWAS. The bigger nodes are the top KDs. Nodes with red outlines are known genes in the IL23/IL17 immune positive control pathway

Fig. 4

GWAS- and EWAS-unique KD subnetworks in psoriasis. Panel (a) and (b) show the GWAS- and EWAS-unique subnetworks for top KDs derived from wKDA. The genes are colored according to the unique processes associated with psoriasis in GWAS (a) or EWAS (b). The bigger nodes are the top KDs. Genes with moderate (1.0e-3 < p < 5.0e-8) to strong (p < 5.0e-8) GWAS/EWAS signals are indicated by the bold outline