05/12/2015
Using Natural Microbial Communities as Biosensors for Environmental Contaminants
The Science
Microbial communities are highly attuned to changes in environmental conditions, rapidly sensing and responding to shifts in temperature, pH, nutrient availability, toxin levels, and dozens of other variables. For decades, scientists have studied the abilities of microbes to survive exposure to (and in some cases make use of) environmental contaminants such as heavy metals, radionuclides, and hydrocarbons. However, microbial communities can contain hundreds of different species, and this complexity makes it extremely difficult to quantitatively measure community-level responses to contaminant exposure. In a new study, a team of researchers from Lawrence Berkeley National Laboratory’s ENIGMA (Ecosystems and Networks Integrated with Genes and Molecular Assemblies) science focus area developed a new computational approach for the analysis and computational modeling of microbial community responses to environmental contaminants. Using direct sequencing of DNA from environmental samples, the team examined the microbial community of a subsurface aquifer in Oak Ridge, Tennessee, that had been contaminated with uranium, nitrate, and a variety of other compounds. Drawing on this data, a modeling framework was constructed to enable prediction of the types and amounts of contaminants that had been experienced by the microbial community based on known physiological characteristics of detected bacterial species. The predictions of this model strongly correlated with amounts of uranium, nitrate, and a variety of other geochemical factors measured at the sampling sites. To test the utility of this approach using an independent dataset, the team applied the model to microbial DNA samples collected during the Deepwater Horizon oil spill in 2010. Again, the model accurately predicted which samples had experienced oil contamination based on microbial DNA sequences and suggested that the community fingerprint retained a “memory” of exposure even after oil was no longer detectable. The results of this study provide a powerful new approach for not only the identification of contaminants in environmental samples, but also the microbial processes that are acting on them and potentially impacting their movement and/or longevity in the environment.
The Impact
Researchers 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. Researchers found that altered bacterial communities encode a memory of prior contamination, even after the contaminants themselves have been fully degraded. Results 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.
BER Program Manager
Dawn Adin
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Biological Systems Science Division
[email protected]
References
Smith, M. B., A. M. Rocha, C. S. Smillie, S. W. Oleson, C. J. Paradis, L. Wu, J. H. Campbell, J. L. Fortney, T. L. Mehlhorn, K. A. Lowe, J. E. Earles, J. Phillips, S. M. Techtmann, D. C. Joyner, S. P. Preheim, M. S. Sanders, J. Yang, M. A. Mueller, S. C. Brooks, D. B. Watson, P. Zhang, Z. He, E. A. Dubinsky, P. D. Adams, A. P. Arkin, M. W. Fields, J. Zhou, E. J. Alm, and T. C. Hazen. 2015. “Natural Bacterial Communities as Quantitative Biosensors,” mBio 6(3), e00326-15. DOI: 10.1128/mBio.00326-15.