Human genome-microbiome interaction: metagenomics frontiers for the aetiopathology of autoimmune diseases

Abstract

A short while ago, the human genome and microbiome were analysed simultaneously for the first time as a multi-omic approach. The analyses of heterogeneous population cohorts showed that microbiome components were associated with human genome variations. In-depth analysis of these results reveals that the majority of those relationships are between immune pathways and autoimmune disease-associated microbiome components. Thus, it can be hypothesized that autoimmunity may be associated with homeostatic disequilibrium of the human-microbiome interactome. Further analysis of human genome-human microbiome relationships in disease contexts with tailored systems biology approaches may yield insights into disease pathogenesis and prognosis.

Keywords: autoimmune disease; genome-microbiome interaction; metagenomics.

Fig. 1.

A systems biology approach to MGWAS. Fusion gene-disease interaction networks are compiled from the GWAS Catalog and the metagenomic unit (e.g. gene, ontology group, metabolic pathway or operational taxonomic unit) co-occurrence network built using metagenome data. Feature selection by machine learning wrappers suggests associations based on variations in the metagenomic units to infer direct and indirect associations between genomic variations and the microbiome in the context of the disease.