Microbial community function and biomarker discovery in the human microbiome

Abstract

Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes such as metabolism and immune homeostasis in the human microbiome. Whole genome shotgun sequencing of such communities' metagenomes is becoming an increasingly feasible complement to obtaining organismal information from taxonomic markers. However, the resultant dataset typically comprises short reads from hundreds of diff erent organisms, making it challenging to assemble and functionally annotate these sequences in the standard manner for singleorganism genomes. We describe an alternative to this approach to infer the functional and metabolic potential of a microbial community metagenome by determining whether gene families and pathways are present or absent, as well as their relative abundances, directly from short sequence reads. We validated this methodology using synthetic metagenomes, recovering the presence and abundance of large pathways and of small functional modules with high accuracy. We subsequently applied this approach to the microbial communities of 649 metagenomes drawn from 7 primary body sites on 102 individuals as part of the Human Microbiome Project (HMP), demonstrating the scalability of our methodology and the critical importance of microbial metabolism in the human microbiota. This provided a framework in which to define functional diversity in comparison to organismal ecology, including an example of microbial metabolism linked to specific organisms and to host phenotype (vaginal pH) in the posterior fornix. We provide profiles of 168 functional modules and 196 metabolic pathways that were determined to be specific to one or more niches within the human microbiome, including details of glycosaminoglycan degradation in the gut. Understanding how and why these biomolecular activities diff er among environmental conditions or disease phenotypes is, more broadly, one of the central questions addressed by high-throughput biology. We have thus developed the linear discriminant analysis (LDA) eff ect size algorithm (LEfSe) to discover and explain microbial and functional biomarkers in the human microbiota and other microbiomes. We demonstrate this method to be eff ective for mining human microbiomes for metagenomic biomarkers associated with mucosal tissues and with diff erent levels of oxygen availability. Similarly, when applied to 16S rRNA gene data from a murine ulcerative colitis gut community, LEfSe c…