Article Figures & Data
Figures
- 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
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
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
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
- 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
Contaminant Predictora OOB error
rate (%)No. of wells predicted/no. of wells defined Background wellsb Contaminated wellsc Uranium All S cycling and metal-related genes 28.99 47/47 2/22 All dsrA, cytochrome, and hydrogenase genes 24.64 47/47 5/22 All dsrA genes 24.64 47/47 5/22 All cytochrome genes 26.09 46/47 5/22 All hydrogenase genes 28.99 41/47 8/22 Key dsrA, cytochrome, and hydrogenase genes 27.54 45/47 5/22 Key dsrA genes 24.64 45/47 7/22 Key cytochrome genes 39.13 38/47 4/22 Key hydrogenase genes 42.03 33/47 7/22 AUC-RF selection 11.59 47/47 14/22 Nitrate All N cycling genes 36.23 39/44 5/25 All nifH, amoA, narG, nasA, and napA genes 34.78 40/44 5/25 All nifH genes 33.33 41/44 5/25 All amoA genes 27.54 41/44 9/25 All narG genes 36.23 40/44 4/25 All nasA genes 36.23 37/44 7/25 All napA genes 34.78 41/44 4/25 Key nifH, amoA, narG, nasA, and napA genes 30.43 40/44 8/25 Key nifH genes 27.54 41/44 9/25 Key amoA genes 28.99 39/44 10/25 Key narG genes 37.68 37/44 6/25 Key nasA genes 40.58 32/44 9/25 Key napA genes 40.58 32/44 9/25 AUC-RF selection 15.94 42/44 16/25 ↵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.
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.
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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.
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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.
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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.
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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.
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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.
