Bacteria comprise the most diverse domain of life on Earth, where they occupy nearly every possible ecological niche and play key roles in biological and chemical processes. Studying the composition and ecology of bacterial ecosystems and understanding their function are of prime importance. High-throughput sequencing technologies enable nearly comprehensive descriptions of bacterial diversity through 16S ribosomal RNA gene amplicons. Analyses of these communities generally rely upon taxonomic assignments through reference data bases or clustering approaches using de facto sequence similarity thresholds to identify operational taxonomic units. However, these methods often fail to resolve ecologically meaningful differences between closely related organisms in complex microbial data sets.In this paper, we describe oligotyping, a novel supervised computational method that allows researchers to investigate the diversity of closely related but distinct bacterial organisms in final operational taxonomic units identified in environmental data sets through 16S ribosomal RNA gene data by the canonical approaches.Our analysis of two data sets from two different environments demonstrates the capacity of oligotyping at discriminating distinct microbial populations of ecological importance.Oligotyping can resolve the distribution of closely related organisms across environments and unveil previously overlooked ecological patterns for microbial communities. The URL http://oligotyping.org offers an open-source software pipeline for oligotyping.

During the Deepwater Horizon oil well blowout in the Gulf of Mexico, the application of 7 million liters of chemical dispersants aimed to stimulate microbial crude oil degradation by increasing the bioavailability of oil compounds. However, the effects of dispersants on oil biodegradation rates are debated. In laboratory experiments, we simulated environmental conditions comparable to the hydrocarbon-rich, 1,100 m deep plume that formed during the Deepwater Horizon discharge. The presence of dispersant significantly altered the microbial community composition through selection for potential dispersant-degrading Colwellia, which also bloomed in situ in Gulf deep waters during the discharge. In contrast, oil addition to deepwater samples in the absence of dispersant stimulated growth of natural hydrocarbon-degrading Marinobacter. In these deepwater microcosm experiments, dispersants did not enhance heterotrophic microbial activity or hydrocarbon oxidation rates. An experiment with surface seawater from an anthropogenically derived oil slick corroborated the deepwater microcosm results as inhibition of hydrocarbon turnover was observed in the presence of dispersants, suggesting that the microcosm findings are broadly applicable across marine habitats. Extrapolating this comprehensive dataset to real world scenarios questions whether dispersants stimulate microbial oil degradation in deep ocean waters and instead highlights that dispersants can exert a negative effect on microbial hydrocarbon degradation rates.

The exploration of microbial communities by sequencing 16S rRNA genes has expanded with low-cost, high-throughput sequencing instruments. Illumina-based 16S rRNA gene sequencing has recently gained popularity over 454 pyrosequencing due to its lower costs, higher accuracy and greater throughput. Although recent reports suggest that Illumina and 454 pyrosequencing provide similar beta diversity measures, it remains to be demonstrated that pre-existing 454 pyrosequencing workflows can transfer directly from 454 to Illumina MiSeq sequencing by simply changing the sequencing adapters of the primers. In this study, we modified 454 pyrosequencing primers targeting the V4-V5 hyper-variable regions of the 16S rRNA gene to be compatible with Illumina sequencers. Microbial communities from cows, humans, leeches, mice, sewage, and termites and a mock community were analyzed by 454 and MiSeq sequencing of the V4-V5 region and MiSeq sequencing of the V4 region. Our analysis revealed that reference-based OTU clustering alone introduced biases compared to de novo clustering, preventing certain taxa from being observed in some samples. Based on this we devised and recommend an analysis pipeline that includes read merging, contaminant filtering, and reference-based clustering followed by de novo OTU clustering, which produces diversity measures consistent with de novo OTU clustering analysis. Low levels of dataset contamination with Illumina sequencing were discovered that could affect analyses that require highly sensitive approaches. While moving to Illumina-based sequencing platforms promises to provide deeper insights into the breadth and function of microbial diversity, our results show that care must be taken to ensure that sequencing and processing artifacts do not obscure true microbial diversity.

The Deepwater Horizon (DWH) oil well blowout generated an enormous plume of dispersed hydrocarbons that substantially altered the Gulf of Mexico's deep-sea microbial community. A significant enrichment of distinct microbial populations was observed, yet, little is known about the abundance and richness of specific microbial ecotypes involved in gas, oil and dispersant biodegradation in the wake of oil spills. Here, we document a previously unrecognized diversity of closely related taxa affiliating with Cycloclasticus, Colwellia and Oceanospirillaceae and describe their spatio-temporal distribution in the Gulf's deepwater, in close proximity to the discharge site and at increasing distance from it, before, during and after the discharge. A highly sensitive, computational method (oligotyping) applied to a data set generated from 454-tag pyrosequencing of bacterial 16S ribosomal RNA gene V4-V6 regions, enabled the detection of population dynamics at the sub-operational taxonomic unit level (0.2% sequence similarity). The biogeochemical signature of the deep-sea samples was assessed via total cell counts, concentrations of short-chain alkanes (C 1 -C 5 ), nutrients, (colored) dissolved organic and inorganic carbon, as well as methane oxidation rates. Statistical analysis elucidated environmental factors that shaped ecologically relevant dynamics of oligotypes, which likely represent distinct ecotypes. Major hydrocarbon degraders, adapted to the slow-diffusive natural hydrocarbon seepage in the Gulf of Mexico, appeared unable to cope with the conditions encountered during the DWH spill or were outcompeted. In contrast, diverse, rare taxa increased rapidly in abundance, underscoring the importance of specialized subpopulations and potential ecotypes during massive deep-sea oil discharges and perhaps other large-scale perturbations.