Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning.

Contamination from anthropogenic activities has significantly impacted Earth's biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly (P < 0.05) as uranium or nitrate increased, and their changes could be used to successfully predict uranium and nitrate contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning.IMPORTANCE Disentangling the relationships between biodiversity and ecosystem functioning is an important but poorly understood topic in ecology. Predicting ecosystem functioning on the basis of biodiversity is even more difficult, particularly with microbial biomarkers. As an exploratory effort, this study used key microbial functional genes as biomarkers to provide predictive understanding of environmental contamination and ecosystem functioning. The results indicated that the overall functional gene richness/diversity decreased as uranium increased in groundwater, while specific key microbial guilds increased significantly as uranium or nitrate increased. These key microbial functional genes could be used to successfully predict environmental contamination and ecosystem functioning. This study represents a significant advance in using functional gene markers to predict the spatial distribution of environmental contaminants and ecosystem functioning toward predictive microbial ecology, which is an ultimate goal of microbial ecology.

Natural bacterial communities serve as quantitative geochemical biosensors.

UNLABELLED: 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.

Identification of a sphingolipid α-glucuronosyltransferase that is essential for pollen function in Arabidopsis.

Glycosyl inositol phosphorylceramide (GIPC) sphingolipids are a major class of lipids in fungi, protozoans, and plants. GIPCs are abundant in the plasma membrane in plants, comprising around a quarter of the total lipids in these membranes. Plant GIPCs contain unique glycan decorations that include a conserved glucuronic acid (GlcA) residue and various additional sugars; however, no proteins responsible for glycosylating GIPCs have been identified to date. Here, we show that the Arabidopsis thaliana protein INOSITOL PHOSPHORYLCERAMIDE GLUCURONOSYLTRANSFERASE1 (IPUT1) transfers GlcA from UDP-GlcA to GIPCs. To demonstrate IPUT1 activity, we introduced the IPUT1 gene together with genes for a UDP-glucose dehydrogenase from Arabidopsis and a human UDP-GlcA transporter into a yeast mutant deficient in the endogenous inositol phosphorylceramide (IPC) mannosyltransferase. In this engineered yeast strain, IPUT1 transferred GlcA to IPC. Overexpression or silencing of IPUT1 in Nicotiana benthamiana resulted in an increase or a decrease, respectively, in IPC glucuronosyltransferase activity in vitro. Plants in which IPUT1 was silenced accumulated IPC, the immediate precursor, as well as ceramides and glucosylceramides. Plants overexpressing IPUT1 showed an increased content of GIPCs. Mutations in IPUT1 are not transmitted through pollen, indicating that these sphingolipids are essential in plants.