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MBio. 2015 May 12;6(3):e00326-15. doi: 10.1128/mBio.00326-15.

Natural bacterial communities serve as quantitative geochemical biosensors.

Author information

1
Microbiology Graduate Program, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
2
Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
3
Computational and Systems Biology Initiative, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
4
Biological Engineering Department, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
5
Department of Earth and Planetary Sciences, University of Tennessee, Knoxville, Tennessee, USA.
6
Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA.
7
Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, Tennessee, USA.
8
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
9
Lawrence Berkeley National Laboratory, Berkeley, California, USA.
10
Department of Microbiology & Immunology, Montana State University, Bozeman, Montana, USA.
11
tchazen@utk.edu.

Abstract

Biological sensors can be engineered to measure a wide range of environmental conditions. Here we show that statistical analysis of DNA from natural microbial communities can be used to accurately identify environmental contaminants, including uranium and nitrate at a nuclear waste site. In addition to contamination, sequence data from the 16S rRNA gene alone can quantitatively predict a rich catalogue of 26 geochemical features collected from 93 wells with highly differing geochemistry characteristics. We extend this approach to identify sites contaminated with hydrocarbons from the Deepwater Horizon oil spill, finding that altered bacterial communities encode a memory of prior contamination, even after the contaminants themselves have been fully degraded. We show that the bacterial strains that are most useful for detecting oil and uranium are known to interact with these substrates, indicating that this statistical approach uncovers ecologically meaningful interactions consistent with previous experimental observations. Future efforts should focus on evaluating the geographical generalizability of these associations. Taken as a whole, these results indicate that ubiquitous, natural bacterial communities can be used as in situ environmental sensors that respond to and capture perturbations caused by human impacts. These in situ biosensors rely on environmental selection rather than directed engineering, and so this approach could be rapidly deployed and scaled as sequencing technology continues to become faster, simpler, and less expensive.

IMPORTANCE:

Here we show that DNA from natural bacterial communities can be used as a quantitative biosensor to accurately distinguish unpolluted sites from those contaminated with uranium, nitrate, or oil. These results indicate that bacterial communities can be used as environmental sensors that respond to and capture perturbations caused by human impacts.

PMID:
25968645
PMCID:
PMC4436078
DOI:
10.1128/mBio.00326-15
[Indexed for MEDLINE]
Free PMC Article

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