1. Department of Biology,
2. Center for Microbial Sciences,
3. Department of Computer Sciences, and,
4. Computational Science Research Centre, San Diego State University, San Diego, California 92182, USA
5. School of Biological Sciences, Flinders University, Adelaide, South Australia 5042, Australia
6. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois 60439, USA
7. University of South Florida, College of Marine Science, 140 7th Avenue South, St Petersburg, Florida 33701, USA
8. Department of Animal Sciences, and,
9. The Institute for Genomic Biology, University of Illinois, Urbana, Illinois 61801, USA
10. Department of Marine Sciences, University of Georgia, Athens, 30602 Georgia, USA
11. The J. Craig Venter Institute, 9712 Medical Center Drive, Rockville, Maryland 20850, USA
12. Genome Institute of Singapore, 60 Biopolis Street, 02-01, Genome, Singapore 138672, Singapore
13. Department of Earth Science, University of California Santa Barbara, Santa Barbara, California 93106, USA
14. These authors contributed equally to this work.
15. Present address: Unité des Rickettsies, CNRS-UMR 6020, Faculté de médecine, 13385 Marseille, France.

Correspondence to: Elizabeth A. Dinsdale^1,5,14 Correspondence and requests for materials should be addressed to E.A.D. (Email: elizabeth_dinsdale@hotmail.com).

Abstract

Microbial activities shape the biogeochemistry of the planet^{1, 2} and macroorganism health³. Determining the metabolic processes performed by microbes is important both for understanding and for manipulating ecosystems (for example, disruption of key processes that lead to disease, conservation of environmental services, and so on). Describing microbial function is hampered by the inability to culture most microbes and by high levels of genomic plasticity. Metagenomic approaches analyse microbial communities to determine the metabolic processes that are important for growth and survival in any given environment. Here we conduct a metagenomic comparison of almost 15 million sequences from 45 distinct microbiomes and, for the first time, 42 distinct viromes and show that there are strongly discriminatory metabolic profiles across environments. Most of the functional diversity was maintained in all of the communities, but the relative occurrence of metabolisms varied, and the differences between metagenomes predicted the biogeochemical conditions of each environment. The magnitude of the microbial metabolic capabilities encoded by the viromes was extensive, suggesting that they serve as a repository for storing and sharing genes among their microbial hosts and influence global evolutionary and metabolic processes.