U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights

Using Natural Microbial Communities as Biosensors for Environmental Contaminants
Published: May 12, 2015
Posted: May 14, 2015

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.

Reference: 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. (Reference link)

Contact: Joseph Graber, SC-23.2, (301) 903-1239
Topic Areas:

  • Research Area: Subsurface Biogeochemical Research
  • Research Area: Genomic Analysis and Systems Biology
  • Research Area: Microbes and Communities
  • Research Area: Computational Biology, Bioinformatics, Modeling

Division: SC-23.2 Biological Systems Science Division, BER


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