Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome

Abstract

Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/humann. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies.

Impact of short-chain galactooligosaccharides on the gut microbiome of lactose-intolerant individuals

“…PICRUSt (14), designed to deduce metagenomic information from 16S rRNA amplicon sequencing data, was applied to sequencing data using the default settings (version 0.9.1). The generated metagenomic tables then were entered into the Human Microbiome Project unified metabolic analysis network (HUMAnN) (38) pipeline (version 0.98) to sort individual genes into Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways representing varying proportions of each imputed sample metagenome.…”

Functional profiles of coronal and dentin caries in children

“…The raw RNASeq reads (in FASTQ format) sequenced from the 14 samples were analysed with the software package ‘HUMAnN2’, the HMP Unified Metabolic Analysis Network (http://huttenhower.sph.harvard.edu/humann2) [21]. HUMAnN is a pipeline for efficiently and accurately profiling the presence/absence and abundance of microbial pathways in a community from metagenomic or metatranscriptomic sequencing data.…”

Automated and accurate estimation of gene family abundance from shotgun metagenomes

“…Here, we build upon previous work [7,[19][20][21][22] and use mock communities and simulated metagenomes to systematically evaluate and optimize metagenome annotation (Fig 1). To our knowledge, our approach represents the first end-to-end evaluation and optimization of metagenome annotation.…”

“…These include stand-alone software such as MEGAN [6], HUMAnN [7], RAMMCAP [8], SmashCommunity [9], and MOCAT [10], as well as cloud-based tools like CloVR [11], and web portals like MG-RAST [12], MicrobesOnline [13], and the IMG/M annotation server [14](S1 Table). Generally, these methods operate by comparing metagenomic sequence reads to a reference database of functionally annotated protein families and use homology inference to annotate each read [5].…”