Healthy microbiome - moving towards functional interpretation

Abstract

Microbiome-based disease prediction has significant potential as an early, non-invasive marker of gut dysbiosis-mediated conditions, thanks in part to decreasing sequencing and analysis costs. Existing tools, or microbiome health indexes, are based on a microbiome’s species richness and dependent on taxonomic classification. However, a developing understanding of microbiome metabolic and phenotypic complexity reveals substantial restrictions of such approaches. In this study, we introduce a new health microbiome index created as an answer to updated microbiome definitions. The novelty of our approach is a shift from a Linnean approach towards microbiome function, while incorporating inter-species interactions to distinguish between healthy and diseased states. We present this significant improvement on the taxonomy-based Gut Microbiome Health Index (GMHI), the most comprehensive index to date, using data obtained from the Human Microbiome Project 2 and American Gut Project cohorts, a study comparison of healthy and Inflammatory Bowel Disease individuals. We validate its performance on a range of diseases, demonstrating its robustness to sequencing depth. Overall, we emphasize the potential and the need to shift towards functional approaches to better understand microbiome health, and provide directions for future index enhancements. Our method,q2-predict-dysbiosis,is freely available as aQIIME 2plugin (https://github.com/bioinf-mcb/q2-predict-dysbiosis).