Grim Sharon L.

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Grim
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Sharon L.
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Author: Sogin, Mitchell L. ×

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  • Article
    Identification and removal of contaminant sequences from ribosomal gene databases : lessons from the Census of Deep Life
    (Frontiers Media, 2018-04-30) Sheik, Cody S. ; Kiel Reese, Brandi ; Twing, Katrina I. ; Sylvan, Jason B. ; Grim, Sharon L. ; Schrenk, Matthew O. ; Sogin, Mitchell L. ; Colwell, Frederick S.
    Earth’s subsurface environment is one of the largest, yet least studied, biomes on Earth, and many questions remain regarding what microorganisms are indigenous to the subsurface. Through the activity of the Census of Deep Life (CoDL) and the Deep Carbon Observatory, an open access 16S ribosomal RNA gene sequence database from diverse subsurface environments has been compiled. However, due to low quantities of biomass in the deep subsurface, the potential for incorporation of contaminants from reagents used during sample collection, processing, and/or sequencing is high. Thus, to understand the ecology of subsurface microorganisms (i.e., the distribution, richness, or survival), it is necessary to minimize, identify, and remove contaminant sequences that will skew the relative abundances of all taxa in the sample. In this meta-analysis, we identify putative contaminants associated with the CoDL dataset, recommend best practices for removing contaminants from samples, and propose a series of best practices for subsurface microbiology sampling. The most abundant putative contaminant genera observed, independent of evenness across samples, were Propionibacterium, Aquabacterium, Ralstonia, and Acinetobacter. While the top five most frequently observed genera were Pseudomonas, Propionibacterium, Acinetobacter, Ralstonia, and Sphingomonas. The majority of the most frequently observed genera (high evenness) were associated with reagent or potential human contamination. Additionally, in DNA extraction blanks, we observed potential archaeal contaminants, including methanogens, which have not been discussed in previous contamination studies. Such contaminants would directly affect the interpretation of subsurface molecular studies, as methanogenesis is an important subsurface biogeochemical process. Utilizing previously identified contaminant genera, we found that ∼27% of the total dataset were identified as contaminant sequences that likely originate from DNA extraction and DNA cleanup methods. Thus, controls must be taken at every step of the collection and processing procedure when working with low biomass environments such as, but not limited to, portions of Earth’s deep subsurface. Taken together, we stress that the CoDL dataset is an incredible resource for the broader research community interested in subsurface life, and steps to remove contamination derived sequences must be taken prior to using this dataset.
  • Preprint
    Chemical dispersants can suppress the activity of natural oil-degrading microorganisms
    ( 2015-09) Kleindienst, Sara ; Seidel, Michael ; Ziervogel, Kai ; Grim, Sharon L. ; Loftis, Kathy ; Harrison, Sarah ; Malkin, Sairah Y. ; Perkins, Matthew J. ; Field, Jennifer ; Sogin, Mitchell L. ; Dittmar, Thorsten ; Passow, Uta ; Medeiros, Patricia M. ; Joye, Samantha B.
    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 in the hydrocarbon-rich, 1100m 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 lacking dispersant stimulated growth of natural hydrocarbon-degrading Marinobacter. Dispersants did not enhance heterotrophic microbial activity or hydrocarbon oxidation rates. Extrapolating this comprehensive data set 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.
  • Article
    Oligotyping : differentiating between closely related microbial taxa using 16S rRNA gene data
    (John Wiley & Sons, 2013-10-23) Eren, A. Murat ; Maignien, Lois ; Sul, Woo Jun ; Murphy, Leslie G. ; Grim, Sharon L. ; Morrison, Hilary G. ; Sogin, Mitchell L.
    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.