Natural Bacterial Communities Serve as Quantitative Geochemical Biosensors
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...
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| Format: | Article |
| Language: | en_US |
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American Society for Microbiology
2015
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| Online Access: | http://hdl.handle.net/1721.1/97056 https://orcid.org/0000-0001-8294-9364 https://orcid.org/0000-0003-4700-5987 https://orcid.org/0000-0002-8202-5222 https://orcid.org/0000-0001-5400-4945 https://orcid.org/0000-0002-3385-9490 |
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| author | Smith, Mark Burnham Smillie, Chris S. Olesen, Scott Wilder Preheim, Sarah P. Sanders, Matthew C. Yang, Joy Y. Alm, Eric J. |
| author2 | Massachusetts Institute of Technology. Computational and Systems Biology Program |
| author_facet | Massachusetts Institute of Technology. Computational and Systems Biology Program Smith, Mark Burnham Smillie, Chris S. Olesen, Scott Wilder Preheim, Sarah P. Sanders, Matthew C. Yang, Joy Y. Alm, Eric J. |
| author_sort | Smith, Mark Burnham |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:36:18Z |
| format | Article |
| id | mit-1721.1/97056 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:36:18Z |
| publishDate | 2015 |
| publisher | American Society for Microbiology |
| record_format | dspace |
| spelling | mit-1721.1/970562022-09-28T14:57:33Z Natural Bacterial Communities Serve as Quantitative Geochemical Biosensors Smith, Mark Burnham Smillie, Chris S. Olesen, Scott Wilder Preheim, Sarah P. Sanders, Matthew C. Yang, Joy Y. Alm, Eric J. Massachusetts Institute of Technology. Computational and Systems Biology Program Massachusetts Institute of Technology. Department of Biological Engineering Smith, Mark Burnham Smillie, Chris S. Olesen, Scott Wilder Preheim, Sarah P. Sanders, Matthew C. Yang, Joy Y. Alm, Eric J. 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. 2015-05-22T17:15:01Z 2015-05-22T17:15:01Z 2015-05 2015-03 Article http://purl.org/eprint/type/JournalArticle 2150-7511 http://hdl.handle.net/1721.1/97056 Smith, Mark B., Andrea M. Rocha, Chris S. Smillie, Scott W. Olesen, Charles Paradis, Liyou Wu, James H. Campbell, et al. “Natural Bacterial Communities Serve as Quantitative Geochemical Biosensors.” mBio 6, no. 3 (May 12, 2015): e00326–15. https://orcid.org/0000-0001-8294-9364 https://orcid.org/0000-0003-4700-5987 https://orcid.org/0000-0002-8202-5222 https://orcid.org/0000-0001-5400-4945 https://orcid.org/0000-0002-3385-9490 en_US http://dx.doi.org/10.1128/mBio.00326-15 mBio Creative Commons Attribution http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf American Society for Microbiology American Society for Microbiology |
| spellingShingle | Smith, Mark Burnham Smillie, Chris S. Olesen, Scott Wilder Preheim, Sarah P. Sanders, Matthew C. Yang, Joy Y. Alm, Eric J. Natural Bacterial Communities Serve as Quantitative Geochemical Biosensors |
| title | Natural Bacterial Communities Serve as Quantitative Geochemical Biosensors |
| title_full | Natural Bacterial Communities Serve as Quantitative Geochemical Biosensors |
| title_fullStr | Natural Bacterial Communities Serve as Quantitative Geochemical Biosensors |
| title_full_unstemmed | Natural Bacterial Communities Serve as Quantitative Geochemical Biosensors |
| title_short | Natural Bacterial Communities Serve as Quantitative Geochemical Biosensors |
| title_sort | natural bacterial communities serve as quantitative geochemical biosensors |
| url | http://hdl.handle.net/1721.1/97056 https://orcid.org/0000-0001-8294-9364 https://orcid.org/0000-0003-4700-5987 https://orcid.org/0000-0002-8202-5222 https://orcid.org/0000-0001-5400-4945 https://orcid.org/0000-0002-3385-9490 |
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