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      Ecology drives a global network of gene exchange connecting the human microbiome.

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          Abstract

          Horizontal gene transfer (HGT), the acquisition of genetic material from non-parental lineages, is known to be important in bacterial evolution. In particular, HGT provides rapid access to genetic innovations, allowing traits such as virulence, antibiotic resistance and xenobiotic metabolism to spread through the human microbiome. Recent anecdotal studies providing snapshots of active gene flow on the human body have highlighted the need to determine the frequency of such recent transfers and the forces that govern these events. Here we report the discovery and characterization of a vast, human-associated network of gene exchange, large enough to directly compare the principal forces shaping HGT. We show that this network of 10,770 unique, recently transferred (more than 99% nucleotide identity) genes found in 2,235 full bacterial genomes, is shaped principally by ecology rather than geography or phylogeny, with most gene exchange occurring between isolates from ecologically similar, but geographically separated, environments. For example, we observe 25-fold more HGT between human-associated bacteria than among ecologically diverse non-human isolates (P = 3.0 × 10(-270)). We show that within the human microbiome this ecological architecture continues across multiple spatial scales, functional classes and ecological niches with transfer further enriched among bacteria that inhabit the same body site, have the same oxygen tolerance or have the same ability to cause disease. This structure offers a window into the molecular traits that define ecological niches, insight that we use to uncover sources of antibiotic resistance and identify genes associated with the pathology of meningitis and other diseases.

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          Author and article information

          Journal
          Nature
          Nature
          Springer Science and Business Media LLC
          1476-4687
          0028-0836
          Oct 30 2011
          : 480
          : 7376
          Affiliations
          [1 ] Computational and Systems Biology Initiative, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
          Article
          nature10571
          10.1038/nature10571
          22037308
          0a8b43ab-8bc8-42bd-8045-72bb7d6613dd
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