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Title: Phylogenetic and functional biomarkers as indicators of bacterialcommunity responses to mixed-waste contamination

Abstract

Few studies have demonstrated changes in community structurealong a contaminant plume in terms of phylogenetic, functional, andgeochemical changes, and such studies are essential to understand how amicrobial ecosystem responds to perturbations. Clonal libraries ofmultiple genes (SSU rDNA, nirK, nirS, amoA, pmoA, and dsrAB) wereanalyzed from groundwater samples (n=6) that varied in contaminantlevels, and 107 geochemical parameters were measured. Principalcomponents analyses (PCA) were used to compare the relationships betweenthe sites with respect to the biomarker (n=785 for all sequences)distributions and the geochemical variables. A major portion of thegeochemical variance measured between the samples could be accounted forby tetrachloroethene, 99Tc, NO3, SO4, Al, and Th. The PCA based on thedistribution of unique biomarkers resulted in different groupingscompared to the geochemical analysis, but when the SSU rRNA genelibraries were directly compared (?Cxy values) the sites were clusteredin asimilar fashion compared to geochemical measures. The PCA based uponfunctional gene distributions each predicted different relationshipsbetween the sites, and comparisons of Euclidean distances based upondiversity indices for all functional genes (n=432) grouped the sites byextreme or intermediate contaminant levels. The data suggested that thesites with low and high perturbations were functionally more similar thansites with intermediate conditions, and perhaps captured the overallcommunity structure better than amore » single phylogenetic biomarker.Moreover, even though the background site was phylogenetically andgeochemically distinct from the acidic sites, the extreme conditions ofthe acidic samples might be more analogous to the limiting nutrientconditions of the background site. An understanding of microbialcommunity-level responses within an ecological framework would providebetter insight for restoration strategies at contaminated fieldsites.« less

Authors:
; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Ernest Orlando Lawrence Berkeley NationalLaboratory, Berkeley, CA (US)
OSTI Identifier:
922808
Report Number(s):
LBNL-60419
Journal ID: ISSN 0013-936X; ESTHAG; R&D Project: VGTLTH; BnR: KP1102010; TRN: US200803%%535
DOE Contract Number:  
DE-AC02-05CH11231
Resource Type:
Journal Article
Resource Relation:
Journal Name: Environmental Science and Technology; Journal Volume: 40; Journal Issue: 8; Related Information: Journal Publication Date: April 15,2006
Country of Publication:
United States
Language:
English
Subject:
59; 54; CONTAMINATION; DISTRIBUTION; ECOSYSTEMS; FUNCTIONALS; GENES; NUTRIENTS; PLUMES; Biomass Production Bioremediation Environmental GenomicsExtremophiles Stress Response Sulfate Reducers

Citation Formats

Fields, M.W., Bagwell, C.E., Carroll, S.L., Yan, T., Liu, X., Watson, D.B., Jardine, P.M., Criddle, C.S., Hazen, T.C., and Zhou, J. Phylogenetic and functional biomarkers as indicators of bacterialcommunity responses to mixed-waste contamination. United States: N. p., 2007. Web.
Fields, M.W., Bagwell, C.E., Carroll, S.L., Yan, T., Liu, X., Watson, D.B., Jardine, P.M., Criddle, C.S., Hazen, T.C., & Zhou, J. Phylogenetic and functional biomarkers as indicators of bacterialcommunity responses to mixed-waste contamination. United States.
Fields, M.W., Bagwell, C.E., Carroll, S.L., Yan, T., Liu, X., Watson, D.B., Jardine, P.M., Criddle, C.S., Hazen, T.C., and Zhou, J. Fri . "Phylogenetic and functional biomarkers as indicators of bacterialcommunity responses to mixed-waste contamination". United States. doi:.
@article{osti_922808,
title = {Phylogenetic and functional biomarkers as indicators of bacterialcommunity responses to mixed-waste contamination},
author = {Fields, M.W. and Bagwell, C.E. and Carroll, S.L. and Yan, T. and Liu, X. and Watson, D.B. and Jardine, P.M. and Criddle, C.S. and Hazen, T.C. and Zhou, J.},
abstractNote = {Few studies have demonstrated changes in community structurealong a contaminant plume in terms of phylogenetic, functional, andgeochemical changes, and such studies are essential to understand how amicrobial ecosystem responds to perturbations. Clonal libraries ofmultiple genes (SSU rDNA, nirK, nirS, amoA, pmoA, and dsrAB) wereanalyzed from groundwater samples (n=6) that varied in contaminantlevels, and 107 geochemical parameters were measured. Principalcomponents analyses (PCA) were used to compare the relationships betweenthe sites with respect to the biomarker (n=785 for all sequences)distributions and the geochemical variables. A major portion of thegeochemical variance measured between the samples could be accounted forby tetrachloroethene, 99Tc, NO3, SO4, Al, and Th. The PCA based on thedistribution of unique biomarkers resulted in different groupingscompared to the geochemical analysis, but when the SSU rRNA genelibraries were directly compared (?Cxy values) the sites were clusteredin asimilar fashion compared to geochemical measures. The PCA based uponfunctional gene distributions each predicted different relationshipsbetween the sites, and comparisons of Euclidean distances based upondiversity indices for all functional genes (n=432) grouped the sites byextreme or intermediate contaminant levels. The data suggested that thesites with low and high perturbations were functionally more similar thansites with intermediate conditions, and perhaps captured the overallcommunity structure better than a single phylogenetic biomarker.Moreover, even though the background site was phylogenetically andgeochemically distinct from the acidic sites, the extreme conditions ofthe acidic samples might be more analogous to the limiting nutrientconditions of the background site. An understanding of microbialcommunity-level responses within an ecological framework would providebetter insight for restoration strategies at contaminated fieldsites.},
doi = {},
journal = {Environmental Science and Technology},
number = 8,
volume = 40,
place = {United States},
year = {Fri Mar 30 00:00:00 EDT 2007},
month = {Fri Mar 30 00:00:00 EDT 2007}
}