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Title: Phylogenetic and Functional Biomakers as Indicators of Bacterial Community Responses to Mixed-Waste Contamination

Abstract

Few studies have demonstrated changes in community structure along a contaminant plume in terms of phylogenetic, functional, and geochemical changes, and such studies are essential to understand how a microbial ecosystem responds to perturbations. Clonal libraries of multiple genes (SSU rDNA, nirK, nirS, amoA, pmoA, and dsrAB) were analyzed from groundwater samples (n = 6) that varied in contaminant levels, and 107 geochemical parameters were measured. Principal components analyses (PCA) were used to compare the relationships among the sites with respect to the biomarker (n = 785 for all sequences) distributions and the geochemical variables. A major portion of the geochemical variance measured among the samples could be accounted for by tetrachloroethene, {sup 99}Tc, NO{sub 3}, SO{sub 4}, Al, and Th. The PCA based on the distribution of unique biomarkers resulted in different groupings compared to the geochemical analysis, but when the SSU rRNA gene libraries were directly compared ({Delta}C{sub xy} values) the sites were clustered in a similar fashion compared to geochemical measures. The PCA based upon functional gene distributions each predicted different relationships among the sites, and comparisons of Euclidean distances based upon diversity indices for all functional genes (n = 432) grouped the sites by extreme ormore » intermediate contaminant levels. The data suggested that the sites with low and high perturbations were functionally more similar than sites with intermediate conditions, and perhaps captured the overall community structure better than a single phylogenetic biomarker. Moreover, even though the background site was phylogenetically and geochemically distinct from the acidic sites, the extreme conditions of the acidic samples might be more analogous to the limiting nutrient conditions of the background site. An understanding of microbial community-level responses within an ecological framework would provide better insight for restoration strategies at contaminated field sites.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
989657
DOE Contract Number:  
DE-AC05-00OR22725
Resource Type:
Journal Article
Resource Relation:
Journal Name: Environmental Science & Technology; Journal Volume: 40; Journal Issue: 8
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; CONTAMINATION; DISTRIBUTION; ECOSYSTEMS; FUNCTIONALS; GENES; NUTRIENTS; PLUMES

Citation Formats

Fields, Matthew Wayne, Carroll, Sue L, Watson, David B, Jardine, Philip M, Criddle, Craig, and Zhou, Jizhong. Phylogenetic and Functional Biomakers as Indicators of Bacterial Community Responses to Mixed-Waste Contamination. United States: N. p., 2006. Web. doi:10.1021/es051748q.
Fields, Matthew Wayne, Carroll, Sue L, Watson, David B, Jardine, Philip M, Criddle, Craig, & Zhou, Jizhong. Phylogenetic and Functional Biomakers as Indicators of Bacterial Community Responses to Mixed-Waste Contamination. United States. doi:10.1021/es051748q.
Fields, Matthew Wayne, Carroll, Sue L, Watson, David B, Jardine, Philip M, Criddle, Craig, and Zhou, Jizhong. Wed . "Phylogenetic and Functional Biomakers as Indicators of Bacterial Community Responses to Mixed-Waste Contamination". United States. doi:10.1021/es051748q.
@article{osti_989657,
title = {Phylogenetic and Functional Biomakers as Indicators of Bacterial Community Responses to Mixed-Waste Contamination},
author = {Fields, Matthew Wayne and Carroll, Sue L and Watson, David B and Jardine, Philip M and Criddle, Craig and Zhou, Jizhong},
abstractNote = {Few studies have demonstrated changes in community structure along a contaminant plume in terms of phylogenetic, functional, and geochemical changes, and such studies are essential to understand how a microbial ecosystem responds to perturbations. Clonal libraries of multiple genes (SSU rDNA, nirK, nirS, amoA, pmoA, and dsrAB) were analyzed from groundwater samples (n = 6) that varied in contaminant levels, and 107 geochemical parameters were measured. Principal components analyses (PCA) were used to compare the relationships among the sites with respect to the biomarker (n = 785 for all sequences) distributions and the geochemical variables. A major portion of the geochemical variance measured among the samples could be accounted for by tetrachloroethene, {sup 99}Tc, NO{sub 3}, SO{sub 4}, Al, and Th. The PCA based on the distribution of unique biomarkers resulted in different groupings compared to the geochemical analysis, but when the SSU rRNA gene libraries were directly compared ({Delta}C{sub xy} values) the sites were clustered in a similar fashion compared to geochemical measures. The PCA based upon functional gene distributions each predicted different relationships among the sites, and comparisons of Euclidean distances based upon diversity indices for all functional genes (n = 432) grouped the sites by extreme or intermediate contaminant levels. The data suggested that the sites with low and high perturbations were functionally more similar than sites with intermediate conditions, and perhaps captured the overall community structure better than a single phylogenetic biomarker. Moreover, even though the background site was phylogenetically and geochemically distinct from the acidic sites, the extreme conditions of the acidic samples might be more analogous to the limiting nutrient conditions of the background site. An understanding of microbial community-level responses within an ecological framework would provide better insight for restoration strategies at contaminated field sites.},
doi = {10.1021/es051748q},
journal = {Environmental Science & Technology},
number = 8,
volume = 40,
place = {United States},
year = {Wed Mar 01 00:00:00 EST 2006},
month = {Wed Mar 01 00:00:00 EST 2006}
}