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Title: The Study of Microbial Environmental Processes Related to the Natural Attenuation of Uranium at the Rifle Site using Systems-level Biology

Abstract

Microbes exist in communities in the environment where they are fundamental drivers of global carbon, nutrient and metal cycles. In subsurface environments, they possess significant metabolic potential to affect these global cycles including the transformation of radionuclides. This study examined the influence of microbial communities in sediment zones undergoing biogeochemical cycling of carbon, nutrients and metals including natural attenuation of uranium. This study examined the relationship of both the microbiota (taxonomy) and their metabolic capacity (function) in driving carbon, nutrient and metal cycles including uranium reduction at the Department of Energy (DOE) Rifle Integrated Field Research Challenge (RIFRC). Objectives of this project were: 1) to apply systems-level biology through application of ‘metaomics’ approaches (collective analyses of whole microbial community DNA, RNA and protein) to the study of microbial environmental processes and their relationship to C, N and metals including the influence of microbial communities on uranium contaminant mobility in subsurface settings undergoing natural attenuation, 2) improve methodologies for data generation using metaomics (collectively metagenomics, metatranscriptomics and proteomics) technologies and analysis and interpretation of that data and 3) use the data generated from these studies towards microbial community-scale metabolic modeling. The strategy for examining these subsurface microbial communities was to generatemore » sequence reads from microbial community DNA (metagenomics or whole genome shotgun sequencing (WGS)) and RNA (metatranscriptomcs or RNAseq) and protein information using proteomics. Results were analyzed independently and through computational modeling. Overall, the community model generated information on the microbial community structure that was observed using metaomic approaches at RIFRC sites and thus provides an important framework for continued community modeling development. The model as created is capable of predicting the response of the community structure in changing environments such as anoxic/oxic conditions or limitations by carbon or nutrients. The ability to more accurately model these responses is critical to understanding carbon and energy flows in an ecosystem is critical towards improving our ability to make predictions that can be used to design more efficient remediation and management strategies, and better understand the implications of environmental perturbations on these ecosystems.« less

Authors:
 [1];  [2];  [3]
  1. J. Craig Venter Inst. (JCVI), Rockville, MD (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. Univ. of Toronto, ON (Canada)
Publication Date:
Research Org.:
J. Craig Venter Inst., Rockville, MD (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1358080
Report Number(s):
FinalreportDOE-JCVI-SC-0006837
DOE Contract Number:
SC0006837
Resource Type:
Technical Report
Resource Relation:
Related Information: Conferences, Publications and Products2013 Genomic Science Annual Contractor-Grantee Meeting2014 Genomic Science Annual Contractor-Grantee Meeting2015 Genomic Science Annual Contractor-Grantee Meeting2016 Genomic Science Annual Contractor-Grantee MeetingCell Factories & Biosustainability Technologies for Cell Factory Construction Conference, Copenhagen, Denmark, 2015Department of Plant and Environmental Sciences (PLEN), University of Copenhagen, Denmark 2015International Environmental Omics Synthesis Conference (iEOS), St. Andrews, Scotland, 2015Phytobiomes 2015, Washington DC 2015EU-US Working Group on Environmental Biotechnology Short-CourseMethé BA, Li K, Zhao J, Lipton M, Mahadevan R. The Application of Ecosystem Modeling atSubsurface Sites to Study Carbon, Nutrient and Metal Cycling in Microbial Communities. In prep.Li K, Shrivastava S, Stockwell TB. (2015). "Degenerate Primer Design for Highly Variable Genomes". PCR Primer Design, 103-115.Goll J, Rusch DB, Tanenbaum DM, Thiagarajan M, Li K, Methé BA and Yooseph S (2010). "METAREP: JCVI metagenomics reports: an open source tool for high-performance comparative metagenomics". Bioinformatics. 26(20):2631-2.Improvements to Corbata software tools at Github
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Methe, Barbara, Lipton, Mary, and Mahadevan, Krishna. The Study of Microbial Environmental Processes Related to the Natural Attenuation of Uranium at the Rifle Site using Systems-level Biology. United States: N. p., 2016. Web. doi:10.2172/1358080.
Methe, Barbara, Lipton, Mary, & Mahadevan, Krishna. The Study of Microbial Environmental Processes Related to the Natural Attenuation of Uranium at the Rifle Site using Systems-level Biology. United States. doi:10.2172/1358080.
Methe, Barbara, Lipton, Mary, and Mahadevan, Krishna. Wed . "The Study of Microbial Environmental Processes Related to the Natural Attenuation of Uranium at the Rifle Site using Systems-level Biology". United States. doi:10.2172/1358080. https://www.osti.gov/servlets/purl/1358080.
@article{osti_1358080,
title = {The Study of Microbial Environmental Processes Related to the Natural Attenuation of Uranium at the Rifle Site using Systems-level Biology},
author = {Methe, Barbara and Lipton, Mary and Mahadevan, Krishna},
abstractNote = {Microbes exist in communities in the environment where they are fundamental drivers of global carbon, nutrient and metal cycles. In subsurface environments, they possess significant metabolic potential to affect these global cycles including the transformation of radionuclides. This study examined the influence of microbial communities in sediment zones undergoing biogeochemical cycling of carbon, nutrients and metals including natural attenuation of uranium. This study examined the relationship of both the microbiota (taxonomy) and their metabolic capacity (function) in driving carbon, nutrient and metal cycles including uranium reduction at the Department of Energy (DOE) Rifle Integrated Field Research Challenge (RIFRC). Objectives of this project were: 1) to apply systems-level biology through application of ‘metaomics’ approaches (collective analyses of whole microbial community DNA, RNA and protein) to the study of microbial environmental processes and their relationship to C, N and metals including the influence of microbial communities on uranium contaminant mobility in subsurface settings undergoing natural attenuation, 2) improve methodologies for data generation using metaomics (collectively metagenomics, metatranscriptomics and proteomics) technologies and analysis and interpretation of that data and 3) use the data generated from these studies towards microbial community-scale metabolic modeling. The strategy for examining these subsurface microbial communities was to generate sequence reads from microbial community DNA (metagenomics or whole genome shotgun sequencing (WGS)) and RNA (metatranscriptomcs or RNAseq) and protein information using proteomics. Results were analyzed independently and through computational modeling. Overall, the community model generated information on the microbial community structure that was observed using metaomic approaches at RIFRC sites and thus provides an important framework for continued community modeling development. The model as created is capable of predicting the response of the community structure in changing environments such as anoxic/oxic conditions or limitations by carbon or nutrients. The ability to more accurately model these responses is critical to understanding carbon and energy flows in an ecosystem is critical towards improving our ability to make predictions that can be used to design more efficient remediation and management strategies, and better understand the implications of environmental perturbations on these ecosystems.},
doi = {10.2172/1358080},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Aug 31 00:00:00 EDT 2016},
month = {Wed Aug 31 00:00:00 EDT 2016}
}

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