Natural Bacterial Communities Serve as Quantitative Geochemical Biosensors

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.

Bacteria as Emerging Indicators of Soil Condition

“…The use of machine learning tools such as a random forest classifier trained on bacterial data to indicate the health state of soil environments would be of particular interest. Methods such as this have previously been successful in creating an in situ environmental indicator that can classify sites either as being contaminated with uranium or nitrate or as being uncontaminated (39). Although the success of such models is promising, it remains to be seen if they can be applied to detect subtler changes induced by land use management, which would likely result in weaker, and less-specific, selective forces acting on the bacterial communities.…”

Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone

“…This conclusion is generalizable and should be applied to other ecosystems exhibiting dispersal across strong environmental gradients, such as estuaries (62) or hydrothermal vents (6). Moreover, in dynamic ecosystems with rapidly changing geochemical conditions, past population growth rates can influence future community structure and biomolecular patterns, and hence cross-sectional community profiles may not reflect current dynamics (63). In such systems, an incorporation of multiple layers of geochemical and biological information into a mechanistic model-as shown here-will be crucial for disentangling the multitude of processes underlying experimental observations.…”

Molybdenum Availability Is Key to Nitrate Removal in Contaminated Groundwater Environments

“…Recently a comprehensive survey of the groundwater was conducted that included 16S rRNA gene sequencing. The results showed that bacterial communities could be linked to geochemical parameters, showing that bacteria could be used as biosensors to detect contamination (9).…”

“…In a previous study, 93 groundwater samples taken from 80 wells at various distances from the S-3 ponds on the ORR were analyzed for a number of geochemical parameters, including pH, nitrate, and dissolved O 2 and several metals (9). Here, the same groundwater samples were further processed by centrifugation, and the soluble fractions were then analyzed by ICP-MS to determine the concentrations of 26 metals, including Mo, W, Fe, and Cu, using a previously described method (23).…”