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Research Article

Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning

Zhili He, Ping Zhang, Linwei Wu, Andrea M. Rocha, Qichao Tu, Zhou Shi, Bo Wu, Yujia Qin, Jianjun Wang, Qingyun Yan, Daniel Curtis, Daliang Ning, Joy D. Van Nostrand, Liyou Wu, Yunfeng Yang, Dwayne A. Elias, David B. Watson, Michael W. W. Adams, Matthew W. Fields, Eric J. Alm, Terry C. Hazen, Paul D. Adams, Adam P. Arkin, Jizhong Zhou
Jennifer Martiny, Editor
Zhili He
aEnvironmental Microbiomics Research Center, School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou, China
bGuangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou, China
cInstitute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA
dDepartment of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
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Ping Zhang
cInstitute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA
dDepartment of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
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Linwei Wu
cInstitute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA
dDepartment of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
eSchool of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, Oklahoma, USA
fSchool of Environment, Tsinghua University, Beijing, China
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Andrea M. Rocha
gBiosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
hDepartment of Civil and Environmental Engineering, University of Tennessee, Knoxville, Tennessee, USA
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Qichao Tu
cInstitute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA
dDepartment of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
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  • ORCID record for Qichao Tu
Zhou Shi
cInstitute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA
dDepartment of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
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Bo Wu
aEnvironmental Microbiomics Research Center, School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou, China
bGuangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou, China
cInstitute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA
dDepartment of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
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Yujia Qin
cInstitute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA
dDepartment of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
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Jianjun Wang
cInstitute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA
dDepartment of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
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Qingyun Yan
aEnvironmental Microbiomics Research Center, School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou, China
bGuangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou, China
cInstitute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA
dDepartment of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
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Daniel Curtis
cInstitute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA
dDepartment of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
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Daliang Ning
cInstitute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA
dDepartment of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
eSchool of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, Oklahoma, USA
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Joy D. Van Nostrand
cInstitute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA
dDepartment of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
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Liyou Wu
cInstitute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA
dDepartment of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
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Yunfeng Yang
fSchool of Environment, Tsinghua University, Beijing, China
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Dwayne A. Elias
gBiosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
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David B. Watson
gBiosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
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Michael W. W. Adams
iDepartment of Biochemistry and Molecular Biology, University of Georgia, Athens, Georgia, USA
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Matthew W. Fields
jDepartment of Microbiology and Immunology, Montana State University, Bozeman, Montana, USA
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Eric J. Alm
kBiological Engineering Department, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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Terry C. Hazen
gBiosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
hDepartment of Civil and Environmental Engineering, University of Tennessee, Knoxville, Tennessee, USA
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Paul D. Adams
lEarth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, California, USA
mDepartment of Bioengineering, University of California, Berkeley, California, USA
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Adam P. Arkin
lEarth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, California, USA
mDepartment of Bioengineering, University of California, Berkeley, California, USA
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Jizhong Zhou
cInstitute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA
dDepartment of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
eSchool of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, Oklahoma, USA
fSchool of Environment, Tsinghua University, Beijing, China
lEarth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, California, USA
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Jennifer Martiny
University of California, Irvine
Roles: Editor
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Tamar Barkay
Rutgers, The State University of New Jersey
Roles: Solicited external reviewer
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Wen-Tso Liu
University of Illinois at Urbana Champaign
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DOI: 10.1128/mBio.02435-17
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  • FIG 1 
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    FIG 1 

    Relationships between the overall functional richness and concentrations of uranium (A) and nitrate (B), as well as pH (C), in groundwater. Uranium and nitrate concentrations were first log transformed, and then linear regressions were performed for functional richness and uranium or nitrate concentrations. Nonlinear regression was used for functional richness and pH.

  • FIG 2 
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    FIG 2 

    Linear relationships between the levels of abundance of specific functional gene families and log-transformed Uranium (A to D) or nitrate (E to H) concentrations in groundwater, including data for dsrA, encoding the alpha subunit of sulfite reductase for dissimilatory sulfite reduction (A), sqr, encoding sulfide-quinone reductase (B), cytochrome genes from well-known organisms, e.g., Geobacter, Anaeromyxobacter, Dechloromonas, Desulfovibrio, Shewanella, Desulfurobacterium, Desulfobacterium, Rhodobacter, Pseudomonas, Enterobacter, and Ochrobactrum (C), hydrogenase genes from well-known organisms, e.g., Geobacter, Desulfovibrio, Desulfurobacterium, Desulfobacterium, and Rhodobacter (D), nirK, encoding nitrite reductase for denitrification (E), nosZ, encoding nitrous oxide reductase for denitrification (F), napA, encoding nitrate reductase for dissimilatory nitrate reduction (G), and nasA, encoding nitrate reductase for assimilatory nitrate reduction (H).

  • FIG 3 
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    FIG 3 

    Significantly (P < 0.05) positive correlations between the levels of abundance of stimulated populations and log-transformed uranium (A and B) or nitrate (C and D) concentrations, including data for dsrA gene variants gi237846130, gi46308012, gi46307974, gi37726843, and gi46307858, derived from uncultured sulfate-reducing bacteria (A), cytochrome genes gi70733596 from Pseudomonas fluorescens, gi393759946 from Alcaligenes faecalis, gi157375053 from Shewanella sediminis, gi394728887 from Enterobacter sp., and gi254982574 from Geobacter sp. (B), nirK gene variants gi116204223 from Chaetomium globosum, gi256723237 from Nectria haematococca, and gi46409951, gi73762878, and gi50541845 from uncultured denitrifying bacteria (C), and napA gene variants gi219549420 from Vibrio parahaemolyticus, gi257458839 from Campylobacter gracilis, gi157913465 from Dinoroseobacter shibae, and gi157285650 and gi169793654 from uncultured nitrate-reducing bacteria (D).

  • FIG 4 
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    FIG 4 

    Random forest predictions of N2O concentrations in groundwater using different sets of genes, including 16S rRNA genes (A); all N cycling genes (B); all norB and nosZ genes (C); key (significantly increased/decreased) norB and nosZ genes (D); all norB genes (E); all nosZ genes (F); key norB genes (G); and key nosZ genes (H). All norB and nosZ key genes are listed in Table S4 in the supplemental material.

Tables

  • Figures
  • Supplemental Material
  • TABLE 1 

    Performance of the random forest model for predicting environmental contamination by uranium or nitrate in 69 wells at the OR-IFRC site using microbial functional genes as predictors

    ContaminantPredictoraOOB error
    rate (%)
    No. of wells predicted/no. of wells defined
    Background wellsbContaminated wellsc
    UraniumAll S cycling and metal-related genes28.9947/472/22
    All dsrA, cytochrome, and hydrogenase genes24.6447/475/22
    All dsrA genes24.6447/475/22
    All cytochrome genes26.0946/475/22
    All hydrogenase genes28.9941/478/22
    Key dsrA, cytochrome, and hydrogenase genes27.5445/475/22
    Key dsrA genes24.6445/477/22
    Key cytochrome genes39.1338/474/22
    Key hydrogenase genes42.0333/477/22
    AUC-RF selection11.5947/4714/22
     
    NitrateAll N cycling genes36.2339/445/25
    All nifH, amoA, narG, nasA, and napA genes34.7840/445/25
    All nifH genes33.3341/445/25
    All amoA genes27.5441/449/25
    All narG genes36.2340/444/25
    All nasA genes36.2337/447/25
    All napA genes34.7841/444/25
    Key nifH, amoA, narG, nasA, and napA genes30.4340/448/25
    Key nifH genes27.5441/449/25
    Key amoA genes28.9939/4410/25
    Key narG genes37.6837/446/25
    Key nasA genes40.5832/449/25
    Key napA genes40.5832/449/25
    AUC-RF selection15.9442/4416/25
    • ↵a Key functional genes detected from each family are listed in Tables S3 and S4 in the supplemental material.

    • ↵b In background wells, the concentrations of uranium or nitrate were 30 µg/liter or below or 10 mg/liter or below, respectively.

    • ↵c In contaminated wells, the concentrations of uranium or nitrate were higher than 30 µg/liter or 10 mg/liter, respectively.

Supplemental Material

  • Figures
  • Tables
  • TABLE S1 

    Key geochemical and ecosystem data for the 69 wells selected. Download TABLE S1, DOCX file, 0.03 MB.

    Copyright © 2018 He et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S1 

    Relationships between the overall functional diversity (Shannon diversity index) and concentrations (log transformed) of uranium (A) and nitrate (B), as well as pH (C), in groundwater. Linear regression was used for the Shannon index and uranium or nitrate concentrations, and nonlinear regression for the Shannon index and pHs. Download FIG S1, TIF file, 0.7 MB.

    Copyright © 2018 He et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S2 

    Relationships between log-transformed uranium (A) or nitrate (B) concentrations, pH (C), or functional richness (D) and groundwater microbial biomass determined by the acridine orange direct count (AODC) method. Linear regression was used for such relationships. Download FIG S2, TIF file, 0.5 MB.

    Copyright © 2018 He et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TABLE S2 

    Regressions of key nitrogen cycling gene abundance with uranium, nitrate, or pH. Uranium and nitrate concentrations were first log transformed and then used for linear regressions, while nonlinear regression without log transformation was used for pHs. P values of <0.05 are in boldface. Download TABLE S2, DOCX file, 0.01 MB.

    Copyright © 2018 He et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TABLE S3 

    Relationships between the abundances of significantly increased or decreased populations (bearing key genes) and uranium concentrations by linear regression. Significantly increased slopes are in boldface, and the relative abundances are presented as mean ratios. Download TABLE S3, DOCX file, 0.03 MB.

    Copyright © 2018 He et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TABLE S4 

    Relationships between the abundances of significantly increased or decreased populations (bearing key genes) and nitrate concentrations by linear regression. Significantly increased slopes are in boldface, and the relative abundances are presented as mean ratios. Download TABLE S4, DOCX file, 0.1 MB.

    Copyright © 2018 He et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TABLE S5 

    Fifty predictors from 2,361 detected functional genes related to uranium reduction automatically selected by AUS-RF for predicting uranium contamination in groundwater. Items in boldface were also identified as belonging to populations significantly increased/decreased with increasing uranium concentrations in groundwater (see Table S3). Download TABLE S5, DOCX file, 0.02 MB.

    Copyright © 2018 He et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TABLE S6 

    Fifty-four predictors from 5,273 detected N cycling genes automatically selected by AUC-RF for predicting nitrate contamination in groundwater. Items in boldface were also identified as belonging to populations significantly increased/decreased with increasing nitrate concentrations in groundwater (see Table S4). Download TABLE S6, DOCX file, 0.02 MB.

    Copyright © 2018 He et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

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Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning
Zhili He, Ping Zhang, Linwei Wu, Andrea M. Rocha, Qichao Tu, Zhou Shi, Bo Wu, Yujia Qin, Jianjun Wang, Qingyun Yan, Daniel Curtis, Daliang Ning, Joy D. Van Nostrand, Liyou Wu, Yunfeng Yang, Dwayne A. Elias, David B. Watson, Michael W. W. Adams, Matthew W. Fields, Eric J. Alm, Terry C. Hazen, Paul D. Adams, Adam P. Arkin, Jizhong Zhou
mBio Feb 2018, 9 (1) e02435-17; DOI: 10.1128/mBio.02435-17

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Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning
Zhili He, Ping Zhang, Linwei Wu, Andrea M. Rocha, Qichao Tu, Zhou Shi, Bo Wu, Yujia Qin, Jianjun Wang, Qingyun Yan, Daniel Curtis, Daliang Ning, Joy D. Van Nostrand, Liyou Wu, Yunfeng Yang, Dwayne A. Elias, David B. Watson, Michael W. W. Adams, Matthew W. Fields, Eric J. Alm, Terry C. Hazen, Paul D. Adams, Adam P. Arkin, Jizhong Zhou
mBio Feb 2018, 9 (1) e02435-17; DOI: 10.1128/mBio.02435-17
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KEYWORDS

groundwater microbiome
random forest
ecosystem functioning
environmental contamination
metagenomics
microbial functional gene

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