Predictive Analysis Using Chemical-Gene Interaction Networks

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Predictive Analysis Using Chemical-Gene Interaction Networks Consistent with Observed Endocrine Activity and Mutagenicity of U.S. Streams

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Jason P. Berninger,† David M. DeMarini,‡ Sarah H. Warren,‡ Jane Ellen Simmons,‡ Vickie S. Wilson,‡ Justin M. Conley,‡ Mikayla D. Armstrong,‡,§ Luke R. Iwanowicz,¶ Dana W. Kolpin,|| Kathryn M. Kuivila,⊥ Timothy J. Reilly,# Kristin M. Romanok,# Daniel L. Villeneuve,∇ and Paul M. Bradley*,○ †

Columbia Environmental Research Center, U.S. Geological Survey, Columbia, Missouri 65201, United States National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States ¶ Leetown Science Center, U.S. Geological Survey, Kearneysville, West Virginia 25430, United States || Central Midwest Water Science Center, U.S. Geological Survey, Iowa City, Iowa 52240, United States ⊥ Oregon Water Science Center, U.S. Geological Survey, Portland, Oregon 97201, United States # New Jersey Water Science Center, U.S. Geological Survey, Lawrenceville, New Jersey 08648, United States ∇ National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Duluth, Minnesota 55804, United States ○ South Atlantic Water Science Center, U.S. Geological Survey, Columbia, South Carolina 29210, United States ‡

S Supporting Information *

ABSTRACT: In a recent U.S. Geological Survey/U.S. Environmental Protection Agency study assessing more than 700 organic compounds in 38 streams, in vitro assays indicated generally low estrogen, androgen, and glucocorticoid receptor activities, with 13 surface waters with 17βestradiol-equivalent (E2Eq) activities greater than a 1-ng/L estimated effects-based trigger value for estrogenic effects in male fish. Among the 36 samples assayed for mutagenicity in the Salmonella bioassay (reported here), 25% had low mutagenic activity and 75% were not mutagenic. Endocrine and mutagenic activities of the water samples were well correlated with each other and with the total number and cumulative concentrations of detected chemical contaminants. To test the predictive utility of knowledge-base-leveraging approaches, site-specific predicted chemical-gene (pCGA) and predicted analogous pathway-linked (pPLA) association networks identified in the Comparative Toxicogenomics Database were compared with observed endocrine/mutagenic bioactivities. We evaluated pCGA/pPLA patterns among sites by cluster analysis and principal component analysis and grouped the pPLA into broad mode-of-action classes. Measured E2eq and mutagenic activities correlated well with predicted pathways. The pPLA analysis also revealed correlations with signaling, metabolic, and regulatory groups, suggesting that other effects pathways may be associated with chemical contaminants in these waters and indicating the need for broader bioassay coverage to assess potential adverse impacts.

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INTRODUCTION Aquatic ecosystems integrate diverse contaminant sources across the landscape, provide reactive matrices for innumerable biotic and abiotic chemical transformations, and, consequently, present complex contaminant exposure profiles.1−3 Inadequate characterization of aquatic-contaminant complexity and poorly understood mixed-contaminant effects are global concerns1,2,4−9 and compelling arguments for risk-assessment approaches that integrate chemical occurrence, bioassays, and knowledge-base-leveraging tools.7,10−15 Nontarget-16 or expanded-target-chemical1−3 assessment approaches are central to identification of contaminant sources This article not subject to U.S. Copyright. Published XXXX by the American Chemical Society

and remediation/mitigation strategies. Through alignment with adverse outcome pathways (AOP), effects-based monitoring approaches ranging from whole-organism exposure studies to the application of rapidly growing numbers of highthroughput bioassays can provide insight into potential health effects associated with exposure to complex mixtures, further informing environmental risk assessment.7,8,17−20 Additionally, Received: Revised: Accepted: Published: A

May 19, 2019 July 7, 2019 July 9, 2019 July 9, 2019 DOI: 10.1021/acs.est.9b02990 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Environmental Science & Technology

Article

Table 1. Mutagenic Potencies (rev/L-eq) of Surface Waters in Salmonella TA98 ≥2× increasec

rev/L-eqb no.

a

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

−S9

+S9

0.0 0.0 0.0 66.4 ± 16.4 0.0 0.0 0.0 0.0 0.0 0.0 88.2 ± 24.7 0.0 0.0 78.8 ± 23.8 0.0 NAf 0.0 NA 86.1 ± 21.9 87.4 ± 18.8 0.0 31.0 ± 11.6 0.0 62.6 ± 19.7 74.7 ± 14.4 51.8 ± 9.7 0.0 170.3 ± 43.3 87.4 ± 20.7 0.0 43.0 ± 9.8g 89.1 ± 24.0 35.5 ± 10.5 0.0 75.4 ± 23.6 33.1 ± 13.5 0.0 0.0

0.0 0.0 0.0 93.2 ± 38.3 0.0 0.0 39.2 ± 6.6 0.0 0.0 28.4 ± 9.8 103.3 ± 21.6 45.8 ± 16.8 0.0 61.9 ± 15.0 0.0 NA 0.0 NA 0.0 90.7 ± 32.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 109.6 ± 28.7 95.6 ± 19.0 0.0 81.1 ± 12.8g 121.6 ± 23.3 0.0 0.0 0.0 0.0 0.0 0.0

sample (abbreviation) state/territory Penn Swamp Branch (PennSwamp) NJ West Clear Creek (WestClearCk) AZ North Sylamore Creek (NSylamoreCk) AR New River (NewR) CA Santa Ana River (SantaAnaR) CA Sycamore Slough (SycamoreS1) CA South Platte River (SPlatte) CO C-111 Canal (C-111) FL Hillsboro Canal (HillsboroCa) FL Sope Creek (SopeCk) GA Fourmile Creek (FourmileCk) IA Sand Run Gulch (SandRunGl) ID South Fork Zumbro River (Zumbro) MN Sunrise River Tributatary (SunriseTrib) MN Hohokus Brook (Hohokus) NJ West Branch Delaware River (DelawareR) NY Chisholm Creek (ChisholmCk) OK Zollner Creek (ZollnerCk) OR Rio Bairoa (RioBairoa) PR Trinity River (TrinityR) TX Hawksbill Creek (HawksbillCk) VA Fishtrap Creek (FishtrapCk) WA Rio Fajardo (RioFajardo) PR Abrams Creek (AbramsCk) GA Mill Creek (MillCk) OH North Dry Creek (NorthDryCk) NE Jordan Creek (JordanCk) PA Blue River (BlueR) MO Tembladero Slough (TembladeroSl) CA Deep Creek (DeepCk) OR East Branch Perkiomen Ck (PerkiomenCk) PA Chicago Sanitary Ship Canal (ChicagoSSC) IL Fall Creek (FallCk) NY Enoree River (EnoreeR) SC Hite Creek (HiteCk) KY South Fork Iowa River (IowaR) IA Rush Creek (RushCk) TX Swiftcurrent Creek/Lake (SwiftcurrentCk) MT

e

−S9

+S9

2.4

mutd

+

2.1

+

2.0

2.7 2.3

+ +

2.3

+

3.3 2.8

2.8

3.1 2.0

+ + + +

a Details about samples can be obtained from Bradley et al. (2017) and Conley et al. (2017). bSlopes of linear regressions are from Figure S1; all nonzero values are significant (p ≤ 0.05). cFold increase ≥2.0 achieved by any dose relative to the DMSO control. dSamples were evaluated as mutagenic if they had both a significant mutagenic potency (slope) and when at least one dose produced at least a 2-fold increase in rev/plate relative to the DMSO control. e“0.0”, indicates “not mutagenic” for the binary mutagenicity “call”. f“NA”, not assessed. gAmong the eight samples that produced significant mutagenic potencies in both +S9 and −S9, the mutagenic potencies of only sample 31 (PerkiomenCk) were significantly different between +S9 and −S9 (p = 0.035).

several recent Great Lakes tributary investigations10−12,24 leveraged the extensive chemical-effects data in the Comparative Toxicogenomics Database (CTD, http://ctdbase.org/ , accessed October 6, 2018)33 to aid selection of site-specific effects-biomarkers and identification of priority contaminants. The analytical scope of these assessments (ranging from 4 to 9 sites or treatments associated with 1−3 streams or source waters, 71−190 target-organic analytes, and gene interactions for 38−88 detected organics10−12,24) was relatively narrow, leaving open questions about the practicability of knowledgebase-leveraging approaches for more comprehensive characterization of environmental contaminants and their potential health effects.

approaches employing chemical analysis and bioassays simultaneously3,11,14,21−25 or sequentially4,7,8,10,26−29 can help identify the chemical classes and some of the individual chemicals responsible for any observed biological activity and promote methodological advances and site-customized monitoring.4,7,8,11,13,15,21,28,30 Effects-based screening approaches in particular are a recognized means of reducing in situ contaminant-monitoring costs.4,8,10 Rapidly expanding knowledge bases of empirical exposureeffects relations provide an important bridge between chemical and biological monitoring approaches.24,27,31 Knowledge-basedriven predictions of biological perturbations and possible adverse outcomes based on chemical occurrence data18,32 can aid in the selection of appropriate bioassays.10−12 For example, B

DOI: 10.1021/acs.est.9b02990 Environ. Sci. Technol. XXXX, XXX, XXX−XXX


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EXPERIMENTAL METHODS Surface-Water Sites and Analyses. In 2012−14, grab samples of surface waters were collected at center-of-flow from 34 urban/agricultural-impacted and 4 undeveloped streams in 24 States and Puerto Rico.3 Surface water at each site was homogenized in Teflon churns and split for analysis of 719 nonredundant organic analytes;3 molecularly targeted ER-, AR-, and GR-agonism activities (described in detail previously23,41,42); and mutagenic potency43 (as follows). For mutagenic potency, Amberlite polymeric resin (XAD)/ethyl acetate extracts were prepared from 36 of the 38 water samples, solvent-exchanged into dimethyl sulfoxide (DMSO) at 10 000×,43 and diluted in DMSO for evaluation of mutagenicity in the Salmonella mutagenicity assay by the standard plate-incorporation method in strain TA98 with (+) and without (−) metabolic activation provided by Aroclorinduced Sprague−Dawley rat liver (S9) obtained from Moltox (Boone, NC).38,44 Extracts were tested at 1 plate per dose at 0.1, 0.25, and 0.5 L-equivalents/plate (L-eq/plate) in 2 or 3 replicate experiments. Plates were incubated for 3 days at 37 °C, and mutant colonies (revertants, rev) were counted on an automatic colony counter (ProtoCOL 3, Microbiology International). Linear regressions were calculated over the linear portion of the dose−response curves as defined by the r2-value to calculate the mutagenic potencies of the water samples, which were defined as the slopes of the regressions and expressed as rev/L-eq (Table 1 and Supporting Information Figure S1 and Table S1) as described previously.44 Samples were considered mutagenic if (a) at least one dose produced at least a 2-fold increase in rev/plate relative to the DMSO control and (b) the dose−response curve gave a significant (p ≤ 0.05) trend test (GraphPad Prism, San Diego, CA). Unpaired, 2-tailed t tests were used to compare the mutagenic potencies between +S9 versus −S9, with p ≤ 0.05 for significance. Chemical-Gene/-Pathway Association Knowledge Bases. A chemical-gene/protein association network was constructed (Figures S2−S3, Tables S2−S5) as described previously10,12,24,27 to predict/map potential biological responses to the 719 target analytes assessed in the USGS/EPA national stream study3 using exposure-effects information mined from the CTD. Multiple species and interactionresponse-note entries for each chemical were condensed to a single pCGA to create a 186 170 cell (406 chemicals, 25 453 genes) pCGA matrix (Table S5). Multiple species and interaction-response-note information was retained in a chemical-information repository for downstream hypothesis generation, along with the chemical name, chemical abstract service (CAS) number, molecular weight, the search term used for CTD chemical−gene interactions, CTD search code, and a unique knowledge base identification (ID). An analogous pPLA matrix of 58 351 cells (406 chemicals, 1861 pathways) was constructed based on pathways identified in the KEGG (Kyoto Encyclopedia of Genes and Genomes)45−48 and REACTOME49,50 databases (Figures S4−S7, Table S6). Site-specific pCGA networks were generated based on the total number of chemicals (4−161) detected per site (Table S7).3 The probability of a biologically significant effect was assumed to increase with increasing number of chemical−gene interactions and with increasing concentration. To begin to address the latter, site-specific cumulative concentrations (nanomoles L−1, nM) of interacting detected chemicals were

The U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (EPA) recently assessed 719 unique organic analytes3 in surface water split samples from 38 United States (US) stream sites representing a national gradient in watershed urban/agricultural development. Designed-bioactive anthropogenic contaminants (biocides, pharmaceuticals) with documented exposure-effects relations comprised 57% of the 406 organics detected at least once in the study, and total detections ranged from 4 to 161 compounds (median 70) per site. To complement the chemical analysis of these water samples, organic extracts of the samples were evaluated for in vitro estrogen (ER), androgen (AR), and glucocorticoid (GR) activity.23 The observed ER activity correlated well with concentrations of key natural estrogens (estrone [E1], 17βestradiol [E2], and estriol [E3]) detected in the water, after correcting for the in vitro potency of each compound.23 These results indicated that the study had reasonable analyticalchemical coverage of estrogenic compounds (i.e., explained approximately 90% of observed ER activity), consistent with the documented importance of these estrogens as drivers of estrogenic activity in impacted surface water.34−37 In contrast, concentrations of recognized AR- and GR-active compounds in the target-analyte list were not sufficient to account for the AR and GR activity observed in samples.23 The lack of association between chemical targets and AR and GR activity reflected either the absence of the relevant target analytes, unrecognized AR or GR activities among the detected compounds, or unrecognized chemical interactions.23 To expand the bioassay-activity profile of these water extracts, we report here the mutagenic potency of 36 of the 38 surface water extracts in the Salmonella (Ames) mutagenicity assay29,38 used extensively for characterizing the mutagenicity of surface waters.39 Although surface waters worldwide generally have low to moderate mutagenic activity,39 no detailed survey has been conducted to characterize the mutagenicity of a representative set of surface waters in the U.S. Combined with the endocrine activity23 and chemical occurrence data,3 these mutagenicity results provide a more comprehensive assessment of the potential of U.S. surface waters to cause adverse health effects. Importantly, the extensive chemical,3 endocrine activity,23 and mutagenic potency (presented here) results from the USGS/EPA surface water study also provided an opportunity to assess the predictive utility of knowledge-base-leveraging of comprehensive environmental-contaminant-exposure data sets. Here, we used exposure-effects relations identified in CTD33,40 to computationally predict chemical-gene/protein associations (pCGA)18−20 and analogous chemical-pathway associations (pPLA) for direct comparison with observed ER-, AR-, and GR-activity and mutagenicity results for corresponding split water samples. In addition, we assessed correlations between the endocrine and mutagenic activities of the surface waters and identified the pPLA networks most associated with the bioassay and target chemical. The specific aim of the present study was to explicitly evaluate the hypothesis that elevated numbers of pCGA and pPLA for ER, AR, GR, or mutagenesis were positively related to elevated levels of corresponding in vitro biological activity. The resulting analyses provide an assessment of the utility of computational methods to predict observed bioassay results and to identify additional biological pathways and health end points of potential importance. C



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Figure 1. Site-specific pCGA counts for the USGS chemical mixtures study. Sites were clustered according to similarity in count among individual genes (black, high; red, midhigh; yellow, mid; green, midlow; blue, low). Diamonds indicate total number of detected chemicals at each site.

calculated for each gene, assuming equipotency as a first-pass analysis (Table S8). Similarly, site-specific pPLA networks were generated27 and adjusted for detected concentrations (Tables S9, S10). Although both assumptions may be overly simplistic, such an approach has merit as an initial screening to generate testable hypotheses (see Table S2 for additional discussion of the assumptions and their limitations). Pathways were further categorized into nine broad biological response groups (BBRG) based on the KEGG hierarchical mapping function (KEGG BRITE45,51) in order to help identify and predict physiological functions most likely to be impacted by chemical exposure, including signaling (S), neurotransmitters (N), regulatory (R), metabolic (M), genetic (G), endocrine (E), immune (I), disease (D), and cancer (C) (Figure S8, Table S11). Site-specific pCGA/pPLA were compared to in vitro receptor transcriptional activation (ER, AR, and GR)23 and mutagenicity43 bioassay results to assess the predictivity of the computational approach and to identify potential drivers of the observed bioactivities. Statistical Analyses. Differences in pCGA and pPLA networks between sites were evaluated using the web multiexperiment viewer (WebMEV)52−54 and K-means cluster analysis (Euclidean distance, 4−8 initial clusters, 1000 permutations). Significant differences between pCGA/pPLA clusters were assessed using ANOVA (SigmaPlot 13, Systat Software, San Jose, CA). Relations between the number of detected chemicals and total pCGA or pPLA at each site were assessed by linear regression. Site-specific predicted-interaction metrics (cumulative numbers, cumulative concentrations assuming equipotency, and potency-adjusted concentrations based on agonist/antagonist ratios in ToxCast) for ESR1, AR, NR3C1, and for cancer-associated-gene groups were compared to ER, AR, GR, and mutagenicity bioassay results, respectively (Figure S9, Tables S12−S15), using Spearman Rank Correlation (SigmaPlot 13, Systat Software, San Jose, CA).

Chemicals with no reported ToxCast activity were treated as zero concentrations.

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RESULTS AND DISCUSSION Chemical-Gene/Protein Associations Using the CTD Database. As reported previously,3 406 of the 719 unique organic chemicals were detected among all 38 sites, with the numbers ranging from 4 to 161 analytes per site (Table S4). The CTD database provided good coverage of the extensive list of detected chemicals. Gene interaction information was available for 91% of the 406 analytes detected in this study, with 306 (75%) having an exact Chemical Abstract Service (CAS) number or name match and 62 analytes (15.3%) represented by parent compound for degradates/metabolites or another surrogate chemical. At the time of access, no information was available for 38 (9.4%) detected analytes (9 not listed in CTD and 29 without published gene-interaction data). For the 368 chemicals with CTD gene interaction information, more than 186 000 pCGA were identified across 25 453 genes (Table S5). Evaluation of individual sites is described in Table S3. The total number of pCGA per site ranged from 990 to >93,000 (Figure 1). As expected, the number of pCGA was positively related to the total number of chemicals detected per site (simple linear regression; p ≤ 0.05, r2 = 0.68; Figure 1), but substantial differences in total pCGA were observed between sites with comparable numbers of detected chemicals. For example, the Hillsboro Canal, Abrams Creek, and Fishtrap Creek sites each had 32 detected chemicals but 43 412; 29 470; and 21 239 pCGA, respectively, largely due to differences in the occurrence of chemicals such as BPA, E2, and EE2, for which there are abundant endocrine toxicology data. Cluster analysis identified significantly different groups (Figure S2) and further emphasized the importance of chemical D



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Figure 2. Interaction counts (solid bars, left axis) and concentrations (hatched bars, right axis) of selected individual genes at example sites within the five clusters identified in the total interaction count analysis. The listed genes were key to cluster separation and represent common neurotransmitters, hormone regulators, and metabolic signaling genes.

contaminants. Of those chemicals, 75% were detected at five or fewer sites. Those detected at more sites tended to be industrial organic precursor molecules, likely resulting from breakdown of other chemicals. The exceptions were two pharmaceutical muscle relaxants, metaxalone and methocarbamol, and the fungicide metalaxyl, present at 16, 11, and 8 sites, respectively. The pCGA and pPLA approaches help to move screening beyond overly simplistic single contaminant-effect models. All sites were predicted to have multiple gene associations per chemical as well as multiple chemicals interacting with individual genes (Figure 2). The number of predicted gene targets per chemical varied substantially (median = 34), with approximately 5% of detected chemicals exceeding 2500 interactions per chemical. For example, CTD documented 6140 and 18 439 unique gene interactions for 17β-estradiol and bisphenol A, respectively. The number of chemicals associated with individual genes varied substantially, ranging from three chemicals associated with NR1I2 at N Sylamore Creek to 75 chemicals associated with CYP3A4 at Blue River (Table S5). Similarly, the number of chemicals associated with individual pathways ranged from 3 for IL-17 signaling at N Sylamore Creek to 107 pPLA for metabolism at Blue River (Table S6). The results indicate that, even at moderately contaminated sites, several thousand genes and several hundred pathways may be affected. For example, North Dry Creek (56 detected chemicals) was predicted to have approximately 5000 pCGA and 1000 pPLA. Agonist/antagonist information in CTD potentially could be used to predict net pCGA for further potential hazard screening. The pPLA workflow supports screening of broader processes, more akin to mode of action, allowing quick evaluation across multiple sites of specific concerns, such as endocrine disruption.

composition versus numbers of detected chemicals, with Santa Ana River and Zollner Creek exhibiting significantly different interaction patterns (Figure S3) despite similar chemical counts and pCGA numbers (Figure 1). No significant relation was apparent between concentration-adjusted total pCGA and the numbers of detected chemicals (Figure S3, Table S8). Pathway Analysis Using the pPLA Database. The pPLA analysis predicted 58 351 interactions across 1861 pathways and 324 chemicals (80% of detected chemicals had pathway-interaction information) (Figures S4−S7, Table S6). As expected, the total pPLA were positively related to the total number of detected chemicals (simple linear regression; p ≤ 0.05, r2 = 0.91). The surface waters clustered into significantly different pPLA cluster groups (Figure S4). As with the pCGA network, concentration adjustment obscured the general relation between pPLA numbers and total detected chemical concentration (Figures S5,S6). Little difference in the relative distribution of BBRG-related pathways was apparent in this study (Table S11), with similar BBRG distribution patterns observed across all sites (Figure S8). The approach warrants caution. The pCGA and pPLA for approximately 15% of the detected chemicals were estimated using parent or related compounds as surrogates, an approach that assumes comparable potencies and modes of actions. These chemicals were primarily pharmaceutical or pesticide metabolites, and most (57%) were present at five or fewer sites. Several of the atrazine and fipronil metabolites were detected at 15 or more sites. Parent-compound substitution captured the presence of these chemicals for screening-level analysis, but possible potency differences should be considered to better predict potential health effects. Another 9% (38/406) of the chemicals detected in the study were not listed in CTD or had no gene/pathway interaction information or suitable surrogates, emphasizing the limited toxicological data available for many commonly found E



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Predictivity of pCGA for ER, AR, and GR Activity of Surface Waters. The predictive utility of the pCGA approach was tested by comparison of in vitro ER, AR, and GR bioassay results from Conley et al.23 with the site-specific counts and concentrations of ESR1-, AR-, and NR3C1-active chemicals in the CTD (Figure 3; Tables S12−S14). The cumulative (sum of all gene-active chemicals), net-cumulative (sum of agonists

minus antagonists), and potency-adjusted (based on ToxCast) concentrations of gene-active chemicals were calculated for each end point. All sites had at least one CTD-predicted chemical association with ER, AR, or GR, except North Sylamore Creek (no ER-actives) and West Clear Creek (no GR-actives) low-impact sites. The number of gene-interacting chemicals at each site was normally distributed around the means (ER, 27; AR, 30; GR, 14). CTD-predicted site-specific counts and concentrations for ESR1 were well correlated with ER bioassay responses,23 and ToxCast-potency adjustment did not improve the correlation (Table S12). More than 40% (50/118) of predicted ESR1active chemicals correlated significantly with observed ER bioactivity. ER bioassay responses also correlated significantly (α ≤ 0.05; Spearman ρ range, 0.35−0.85) to the site-specific detected-chemical counts and concentrations of several individual wastewater indicators (e.g., triclosan, galaxolide, metformin, carbamazepine), consistent with numerous reports of the importance of wastewater as an estrogenic source37,55,56 and driver of instream estrogenic effects.57−60 Estimating the average estrogenicity of ER-active chemicals from the ESR1 pCGA approach as 1000 times less potent than the reference estrogen, E2, the exceedance of a 1-ng/L (0.00367 nM) E2Eq response trigger value suggested in Conley et al.23 (i.e., cumulative concentrations of presumptive ESR1-active chemicals greater than 3.67 nM) was predicted at 25 sites. In vitro ER activity was reported above the trigger value at 13 sites;23 the ESR1 pCGA approach identified all 13 of these sites and correctly predicted above/below trigger-value responses for 24 of 35 sites (69%). Incorrectly scored sites were all false-positives, in which trigger-value exceedance was predicted by the pCGA approach but not observed in vitro. Using a more conservative (protective) trigger value of 0.1 ng/ L (0.000367 nM) E2Eq proposed recently,61,62 32 sites were predicted to exceed the trigger value (compared to 30 observed in vitro), with the pCGA approach correctly predicting the above/below trigger value at 33 of 35 sites (94.3%) with no false-negatives observed. Significant (α ≤ 0.05) but weaker (Spearman Rank; ρ < 0.5) correlations were observed between CTD-predicted and observed23 AR- and GR-activities (Tables S13 and S14). Of the 148 predicted AR-active chemicals, 39 exhibited significant correlations with AR-bioassay results, but only 18 had correlation coefficients greater than 0.5 (Table S13). Currently, no proposed AR trigger values are available for reference in the literature. Conley et al.23 also identified significant GR-bioactivity without recognized GR-active chemical candidates, suggesting incomplete analytical coverage, unrecognized GR-bioactivity of detected compounds, or undocumented effects interactions. Significant (α ≤ 0.05) Spearman Rank (ρ ca. 0.5) correlations between reported GRbioactivity23 and 18 of the 63 detected compounds identified as NR3C1-active in CTD (Table S14) suggested potential candidates for GR-activity testing. ToxCast-potency adjustment did not improve the correlations. GR-active chemicals are increasingly reported in surface water and wastewater,63−65 including some endogenous corticosteroids and pharmaceuticals with potencies in the CV-1 GR assay comparable to and, in some cases, exceeding that of dexamethasone (DEX).66 Predictivity of pCGA for Mutagenicity. The primary mutagenicity data (rev/plate) are shown in Table S1. Linear regressions for those samples with significant slopes are shown in Figure S1 and corresponding mutagenic potencies

Figure 3. Comparison of cumulative net (agonist−antagonist) concentrations of CTD-predicted (solid bars) gene-active chemicals or cumulative concentrations of known (hatched bars; ER/ESR1, and AR only) gene-active chemicals with responses (▲) for the estrogen receptor (ER/ESR1 gene), androgen receptor (AR), and glucocorticoid receptor (GR/NR3C1 gene) in vitro bioassays reported in Conley et al.23 Open symbols indicate bioassay activity less than 1 ng/ L E2Eq (ER plots only) or not significantly different from control (AR, GR plots). F



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agricultural or urban areas, may exhibit low or no toxicities in these categories. A comparison with split-sample in vitro or high-throughputbioassay results improved pCGA/pPLA prediction, supported recognition and reconciliation of incongruent exposure and effects results, and helped to more directly connect chemical associations with adverse outcomes. The pCGA/pPLA approach correctly predicted AOP-linked ER bioactivities, and its utility in identifying potential molecular-initiating events should increase with continued development of bioassays, AOP, and AOP-networks. Arguably the greatest concern for this analysis is the underlying assumption that the potential for environmental effects generally correlates with the number of recognized chemical-gene/chemical-pathways interactions, creating a potential bias toward highly studied chemicals, such as bisphenol A or benzo[a]pyrene. However, this concern can be somewhat attenuated by limiting the database to one pCGA/pPLA per chemical. The CTD-knowledge-base (825 chemicals) approach resolved site-to-site differences at high (150+ detected chemicals) and low (less than 20 detected chemicals) ends of the site-contamination gradient and reliably predicted observed effects pathways (pCGA/pPLA), including endocrine and mutagenic potencies, in surface water samples. Importantly, the computational toxicology results suggest that the organic-contaminant mixtures in some of these waters may be associated with pathologies involving metabolic, signaling, or regulatory functions and support recent calls for expanded biological-end-point coverage to better characterize adverse effects potentials and for development of effect-based water quality triggers.68−70 The results suggest that the historical emphasis on mutagenicity, endocrine, and/or AhR-oriented bioassays to evaluate the health effects of surface waters may underestimate the potential for harmful biological effects.68−70

(regression slopes; rev/L-eq) are summarized in Table 1. Among the 36 samples assessed for mutagenic potency in this study, 53% (19/36) had significant slope values, and 28% (10/ 36) had at least one dose that produced at least a 2-fold (2×) increase in rev/plate (Table 1). Here, we conservatively determined that only those nine (25%) waters exhibiting both a significant slope and a 2× plus increase in rev/plate would be mutagenic (Table 1). The mutagenic potencies among these nine waters ranged from 43 to 170 rev/L-eq, placing them in the low-mutagenicity category defined as 100 rev/L-eq, the Blue River (170.3 rev/L-eq) and the Chicago Sanitary Ship Canal (121.6 rev/L-eq) (Table 1). The Blue River water sample was the most mutagenic and had the second highest level of endocrine activity.23 A review39 indicated that only 7−15% of surface waters worldwide were mutagenic in the Salmonella assay, and most observed potencies were categorized as low (1000 surface water samples over a 20-year period in São Paulo State, Brazil, found that only 14% were mutagenic.67 Thus, the 25% of water samples with lowmutagenic-potency reported herein is consistent with mutagenicity results worldwide. Mutagenicity was significantly positively correlated with total chemical count (Spearman Rank correlation: α ≤ 0.05; ρ = 0.52) across all sites. Among the 19 samples with significant slopes (Table 1), 84% (16/19) were detected under −S9 test conditions. Because all nine mutagenic (significant slope and 2× plus increase in rev/plate) samples were detected under −S9 conditions and only one (Perkiomen Creek) was significantly (p = 0.035) more mutagenic under +S9 conditions, we used the −S9 mutagenic potencies for comparison with computational analyses. Interestingly, mutagenicity was also significantly (α ≤ 0.05) correlated (ρ = 0.43) with ER-bioactivity, perhaps due to the previously discussed covariation between ER-bioactivity and wastewater contamination. Mutagenicity results correlated significantly with 33 of 38 CTD-identified cancer-pathways, with Spearman coefficients ranging from 0.41 to 0.63 and most (66%) exceeding 0.5 (Table S15). Thus, despite the low mutagenic activity exhibited by these surface waters, the computational analysis associated this limited activity to the presence of mutagens/ carcinogens in the waters. Implications of Results for Aquatic Ecosystems. The Salmonella mutagenicity assay has been used extensively for more than 40 years to identify key classes of mutagens/ carcinogens in complex environmental mixtures, as well as for general environmental biomonitoring.29 Although hundreds of organic compounds were found among these U.S. surface waters,3 based on the conservative criteria used here the waters had low or no levels of mutagenic activity (Table 1) in the commonly used TA98 and TA100. Thus, this cross section of U.S. surface waters, although variably impacted with a wide variety of organic compounds, did not indicate a widespread presence of mutagenic activity, suggesting little potential for carcinogenic activity. This is an important finding, suggesting that many surface waters in the U.S., running either through

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ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.9b02990. Linear regressions; pCGA and pPLA site clusters, count; by concentrations; other data as described in the text (PDF) Site summary data; information links; mutagenicity; other data as described in the text (XLSX)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Jason P. Berninger: 0000-0003-3045-7899 David M. DeMarini: 0000-0001-8357-7988 Vickie S. Wilson: 0000-0003-1661-8481 Justin M. Conley: 0000-0002-6622-5769 Mikayla D. Armstrong: 0000-0002-0381-9324 Luke R. Iwanowicz: 0000-0002-1197-6178 Dana W. Kolpin: 0000-0002-3529-6505 Kathryn M. Kuivila: 0000-0001-7940-489X Timothy J. Reilly: 0000-0002-2939-3050 Kristin M. Romanok: 0000-0002-8472-8765 Daniel L. Villeneuve: 0000-0003-2801-0203 Paul M. Bradley: 0000-0001-7522-8606 G



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§

Department of Environmental Science and Engineering, University of North Carolina, Chapel Hill, North Carolina 27599, United States.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was conducted and funded by the Toxic Substances Hydrology program of the USGS Environmental Health Mission Area. M. D. Armstrong acknowledges funding from the EPA-UNC−CH Cooperative Training Agreement CR-83591401-0. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. This article was reviewed by the National Health and Environmental Effects Laboratory, EPA, and approved for publication. The findings and conclusions in this article do not necessarily represent the views or policies of the US Environmental Protection Agency. This report contains CAS Registry Numbers, which is a Registered Trademark of the American Chemical Society. CAS recommends the verification of the CASRNs through CAS Client ServicesSM.

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