Leveraging Multilayered “Omics” Data for Atopic Dermatitis: A Road Map to Precision Medicine - Ghosh, Debajyoti; Bernstein, Jonathan A.; Khurana Hershey, Gurjit K.; Rothenberg, Marc E.; Mersha, Tesfaye B. (Frontiers in Immunology, 2018)
Atopic dermatitis (AD) is a complex inflammatory skin disease affecting approximately 280 million people worldwide.1 About 85% of AD cases begin in childhood, a significant portion of which can persist into adulthood.2, 3 Moreover, there is a typical progression of children with AD to food allergy, asthma or allergic rhinitis later in life—a phenomenon known as ‘allergic march’ or ‘atopic march’.4 AD comprises highly heterogeneous sub-phenotypes/endotypes resulting from complex interplay between intrinsic and extrinsic factors, such as environmental stimuli, and genetic factors regulating cutaneous functions (impaired barrier function, epidermal lipid and protease abnormalities), immune functions and the microbiome. Though the roles of high-throughput “omics” integrations in defining endotypes are recognized, current analyses are primarily based on individual omics data and using binary clinical outcomes. Although individual omics analysis, such as genome-wide association studies (GWAS), can effectively map variants correlated with AD, the majority of the heritability and the functional relevance of discovered variants are not explained or known by the identified variants. The limited success of singular approaches underscores the need for holistic and integrated approaches to investigate complex phenotypes using trans-omics data integration strategies. Integrating omics layers (e.g., genome, epigenome, transcriptome, proteome, metabolome, lipidome, exposome, microbiome), which often have complementary and synergistic effects, offers the opportunity to comprehensively understand the flow of information that underlines the biological basis of AD. Overlapped genes among multiple omics types include FLG, SPINK5, S100A8 and SERPINB3 in AD pathogenesis. Overlapping pathways include macrophage, endothelial cell and fibroblast activation pathways, in addition to well-known Th1/Th2 and NFB activation pathways. Interestingly, there was more multi-omics overlap at the pathway level than gene level. Further analysis of multi-omics overlap at the tissue level showed that among 30 tissue types from the GTEx database, skin and esophagus were significantly enriched, indicating the biological interconnection between AD and food allergy. The present work explores multi-omics integration and provides new biological insights to better define the biological basis of AD etiology and confirm previously reported AD genes/pathways. In this context, we also discuss opportunities and challenges introduced by ‘big omics data’ and their integration.