Flow of bacterial genes in the environment analyzed
The human body is a complex biological network that relies on microbes to help with metabolism and other essential functions. The microbes living in humans outnumber human cells 10 to 1. Their interaction with microbes in the environment often leads to horizontal gene transfer (HGT), the acquisition of genetic material from non-parental lineages, an important feature of bacterial evolution. In particular, HGT provides bacteria with rapid access to genetic innovations, allowing traits such as virulence and antibiotic resistance to spread through the human microbiome. Recent studies — like the one in 2010 that identified the so-called sushi gene, which had been transferred from a marine microbe to a bacteria living in the human gut — provide snapshots of active gene flow. These studies, along with growing concern that antibiotic resistance is being transferred from bacteria in livestock to bacteria in humans, highlight the need to determine the frequency of such transfers and the forces that govern them.
Using computational biology to determine the evolutionary ancestry of the genes in 2,235 complete bacteria genomes, Professor Eric Alm and graduate students Chris Smillie and Mark Smith first identified genes that had recently been transferred between human-associated bacteria and non-human associated bacteria via HGT. (Their heuristic locates blocks of nearly identical DNA in distantly related bacteria taken from different human subjects or environments, often on different continents.) The researchers then determined whether the HGT networks in play were influenced more by phylogeny (the evolutionary history of organisms), geographic proximity or similarity in ecological niche. Phylogeny could strongly influence HGT because selection favors the persistence of genes acquired from close relatives. Geography might provide a means of restricting HGT by proximity. Ecological similarity shapes networks of gene exchange by selecting for the transfer and proliferation of adaptive traits or through the increased physical interactions among community members.
Alm, Smith and Smillie discovered a human-associated network of 10,770 unique, recently transferred genes that is shaped principally by ecology rather than by geography or phylogeny, with most gene exchange occurring between bacteria from ecologically similar, but geographically separated, environments. Their analyses indicate that recent HGT has frequently crossed continents and evolutionary history to globally connect the human microbiome. They found 42 antibiotic resistance genes that had transferred between human and farm isolates, and 43 that had crossed national borders. They observed that HGT is 25 times more likely to occur between human-associated bacteria than among ecologically diverse non-human isolates. They also showed that within the human microbiome the transfer is further enriched among bacteria that inhabit the same body site, have the same oxygen tolerance or have the same ability to cause disease.
This work shows that ecology governs recent HGT among human-associated bacteria and between human and non-human associated bacteria, such as microbes living in livestock, and indicates that antibiotic resistance is likely transferring from livestock to the humans who eat it via HGT. The evolutionary rate heuristic developed by Alm could analyze bacterial genomes in groups of people that differ in diet, disease or ancestry to uncover sources of antibiotic resistance and identify genes associated with the pathology of diseases. It could also be used to study bacteria living in contaminated water, soil or groundwater to identify which transferred genes provide microbes with the capability to live in toxic environments and reduce substances in ways helpful to humans.
Alm, Smillie, Smith and co-authors graduate student Jonathan Friedman, postdoctoral fellow Otto Cordero and alumnus Lawrence David (now a Fellow at Harvard University) wrote a paper about this work that appeared online in Nature Oct. 30, 2011. A graphic from the paper was featured in Wired Science as one of 10 great research graphics.
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