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

Authors

Sahar Abubucker, Washington University School of Medicine
Nicola Segata, Harvard School of Public Health
Johannes Goll, J. Craig Venter Institute
Alyxandria M. Schubert, The University Of Michigan
Jacques Izard, Forsyth Institute & Harvard School of Dental MedicineFollow
Brandi L. Cantarel, University of Maryland School of Medicine
Beltran Rodriguez-Mueller, Harvard School of Dental Medicine
Jeremy Zucker, The Broad Institute of MIT and Harvard
Mathangi Thiagarajan, J. Craig Venter Institute
Bernard Henrissat, Universite´ de la Me´diterranee
Owen White, University of Maryland School of MedicineFollow
Scott T. Scott, San Diego State University
Barbara Methe´, J. Craig Venter Institute
Patrick D. Schloss, The University Of Michigan
Dirk GeverS, The Broad Institute of MIT and Harvard
Makedonka Mitreva, Washington University School of Medicine
Curtis Huttenhower, Harvard School of Public Health & The Broad Institute of MIT and HarvardFollow

Date of this Version

6-13-2012

Citation

2012 Abubucker et al.

Comments

PLoS Computational Biology | www.ploscompbiol.org 1 June 2012 | Volume 8 | Issue 6 | e1002358

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.