Perennially ice-covered lakes in the McMurdo Dry Valleys, Antarctica, are chemically stratified with depth and have distinct biological gradients. Despite long-term research on these unique environments, data on the structure of the microbial communities in the water columns of these lakes are scarce. Here, we examined bacterial diversity in five ice-covered Antarctic lakes by 16S rRNA gene-based pyrosequencing. Distinct communities were present in each lake, reflecting the unique biogeochemical characteristics of these environments. Further, certain bacterial lineages were confined exclusively to specific depths within each lake. For example, candidate division WM88 occurred solely at a depth of 15 m in Lake Fryxell, whereas unknown lineages of Chlorobi were found only at a depth of 18 m in Lake Miers, and two distinct classes of Firmicutes inhabited East and West Lobe Bonney at depths of 30 m. Redundancy analysis revealed that community variation of bacterioplankton could be explained by the distinct conditions of each lake and depth; in particular, assemblages from layers beneath the chemocline had biogeochemical associations that differed from those in the upper layers. These patterns of community composition may represent bacterial adaptations to the extreme and unique biogeochemical gradients of ice-covered lakes in the McMurdo Dry Valleys.

Sequencing of plasmid-enriched samples revealed multiple genetic contexts for the resistance genes detected and highlighted the potential role of small plasmids in the movement of antibiotic resistance genes. The findings are informative for disease management in food animals, but also manure management and antibiotic therapy in human medicine for improved antibiotic stewardship.

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Despite advances in physicochemical remediation technologies, in situ bioremediation treatment based on Dehalococcoides mccartyi (Dhc) reductive dechlorination activity remains a cornerstone approach to remedy sites impacted with chlorinated ethenes. Selecting the best remedial strategy is challenging due to uncertainties and complexity associated with biological and geochemical factors influencing Dhc activity. Guidelines based on measurable biogeochemical parameters have been proposed, but contemporary efforts fall short of meaningfully integrating the available information. Extensive groundwater monitoring data sets have been collected for decades, but have not been systematically analyzed and used for developing tools to guide decision-making. In the present study, geochemical and microbial data sets collected from 35 wells at five contaminated sites were used to demonstrate that a data mining prediction model using the classification and regression tree (CART) algorithm can provide improved predictive understanding of a site's reductive dechlorination potential. The CART model successfully predicted the 3-month-ahead reductive dechlorination potential with 75.8% and 69.5% true positive rate (i.e., sensitivity) for the training set and the test set, respectively. The machine learning algorithm ranked parameters by relative importance for assessing in situ reductive dechlorination potential. The abundance of Dhc 16S rRNA genes, CH4, Fe(2+), NO3(-), NO2(-), and SO4(2-) concentrations, total organic carbon (TOC) amounts, and oxidation-reduction potential (ORP) displayed significant correlations (p < 0.01) with dechlorination potential, with NO3(-), NO2(-), and Fe(2+) concentrations exhibiting precedence over other parameters. Contrary to prior efforts, the power of data mining approaches lies in the ability to discern synergetic effects between multiple parameters that affect reductive dechlorination activity. Overall, these findings demonstrate that data mining techniques (e.g., machine learning algorithms) effectively utilize groundwater monitoring data to derive predictive understanding of contaminant degradation, and thus have great potential for improving decision-making tools. A major need for realizing the predictive capabilities of data mining approaches is a curated, open-access, up-to-date and comprehensive collection of biogeochemical groundwater monitoring data.

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