De Novo Assembly of the Nearly Complete Fathead Minnow Reference Genome Reveals a Repetitive but Compact Genome.

The fathead minnow is a widely used model organism in environmental toxicology. The lack of a high-quality fathead minnow reference genome, however, has severely hampered its uses in toxicogenomics. We present the de novo assembly and annotation of the fathead minnow genome using long PacBio reads, Bionano and Hi-C scaffolding data, and large RNA-sequencing data sets from different tissues and life stages. The new annotated fathead minnow reference genome has a scaffold N50 of 12.0 Mbp and a complete benchmarking universal single-copy orthologs score of 95.1%. The completeness of annotation for the new reference genome is comparable to that of the zebrafish GRCz11 reference genome. The fathead minnow genome, revealed to be highly repetitive and sharing extensive syntenic regions with the zebrafish genome, has a much more compact gene structure than the zebrafish genome. Particularly, comparative genomic analysis with zebrafish, mouse, and human showed that fathead minnow homologous genes are relatively conserved in exon regions but had strikingly shorter intron regions. The new fathead minnow reference genome and annotation data, publicly available from the National Center for Biotechnology Information and the University of California Santa Cruz genome browser, provides an essential resource for aquatic toxicogenomic studies in ecotoxicology and public health. Environ Toxicol Chem 2022;41:448-461. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.

Development of omics biomarkers for estrogen exposure using mRNA, miRNA and piRNAs.

The number of chemicals requiring risk evaluation exceeds our capacity to provide the underlying data using traditional methodology. This has led to an increased focus on the development of novel approach methodologies. This work aimed to expand the panel of gene expression-based biomarkers to include responses to estrogens, to identify training strategies that maximize the range of applicable concentrations, and to evaluate the potential for two classes of small non-coding RNAs (sncRNAs), microRNA (miRNA) and piwi-interacting RNA (piRNA), as biomarkers. To this end larval Pimephales promelas (96 hpf +/- 1h) were exposed to 5 concentrations of 17α- ethinylestradiol (0.12, 1.25, 2.5, 5.0, 10.0 ng/L) for 48 h. For mRNA-based biomarker development, RNA-seq was conducted across all concentrations. For sncRNA biomarkers, small RNA libraries were prepared only for the control and 10.0 ng/L EE2 treatment. In order to develop an mRNA classifier that remained accurate over the range of exposure concentrations, three different training strategies were employed that focused on 10 ng/L, 2.5 ng/L or a combination of both. Classifiers were tested against an independent test set of individuals exposed to the same concentrations used in training and subsequently against concentrations not included in model training. Both random forest (RF) and logistic regression with elastic net regularizations (glmnet) models trained on 10 ng/L EE2 performed poorly when applied to lower concentrations. RF models trained with either the 2.5 ng/L or combination (2.5 + 10 ng/L) treatments were able to accurately discriminate exposed vs. non-exposed across all but the lowest concentrations. glmnet models were unable to accurately classify below 5 ng/L. With the exception of the 10 ng/L treatment, few mRNA differentially expressed genes (DEG) were observed, however, there was marked overlap of DEGs across treatments. Overlapping DEGs have well established linkages to estrogen and several of the 81 DEGs identified in the 10 ng/L treatment have been previously utilized as estrogenic biomarkers (vitellogenin, estrogen receptor-ß). Following multiple test correction, no sncRNAs were found to be differentially expressed, however, both miRNA and piRNA classifiers were able to accurately discriminate control and 10 ng/L exposed organisms with AUCs of 0.83 and 1.0 respectively. We have developed a highly discriminative estrogenic mRNA biomarker that is accurate over a range of concentrations likely to be found in real-world exposures. We have demonstrated that both miRNA and piRNA are responsive to estrogenic exposure, suggesting the need to further investigate their regulatory roles in the estrogenic response.

Multigene Biomarkers of Pyrethroid Exposure: Exploratory Experiments.

We describe initial development of microarray-based assays for detecting 4 pyrethroid pesticides (bifenthrin, cypermethrin, esfenvalerate, and permethrin) in water. To facilitate comparison of transcriptional responses with gross apical responses, we estimated concentration-mortality curves for these pyrethroids using flow-through exposures of newly hatched Daphnia magna, Pimephales promelas adults, and 24 h posthatch P. promelas. Median lethal concentration (LC50) estimates were below most reported values, perhaps attributable to the use of flow-through exposures or of measured rather than nominal concentrations. Microarray analysis of whole P. promelas larvae and brains from exposed P. promelas adults showed that assays using either tissue type can detect these pyrethroids at concentrations below LC50 values reported for between 72 and 96% of aquatic species, depending on the pesticide. These estimates are conservative because they correspond to the lowest concentrations tested. This suggests that the simpler and less expensive whole-larval assay provides adequate sensitivity for screening contexts where acute aquatic lethality is observed, but the responsible agent is not known. Gene set analysis (GSA) highlighted several Gene Ontology (GO) terms consistent with known pyrethroid action, but the implications of other GO terms are less clear. Exploration of the sensitivity of results to changes in data processing suggests robustness of the detection assay results, but GSA results were sensitive to methodological variations. Environ Toxicol Chem 2019;38:2436-2446. Published 2019 Wiley Periodicals, Inc. on behalf of SETAC. This article is a US government work, and as such, is in the public domain in the United States of America.

A statistical framework for applying RNA profiling to chemical hazard detection.

Use of 'omics technologies in environmental science is expanding. However, application is mostly restricted to characterizing molecular steps leading from toxicant interaction with molecular receptors to apical endpoints in laboratory species. Use in environmental decision-making is limited, due to difficulty in elucidating mechanisms in sufficient detail to make quantitative outcome predictions in any single species or in extending predictions to aquatic communities. Here we introduce a mechanism-agnostic statistical approach, supplementing mechanistic investigation by allowing probabilistic outcome prediction even when understanding of molecular pathways is limited, and facilitating extrapolation from results in laboratory test species to predictions about aquatic communities. We use concepts familiar to environmental managers, supplemented with techniques employed for clinical interpretation of 'omics-based biomedical tests. We describe the framework in step-wise fashion, beginning with single test replicates of a single RNA variant, then extending to multi-gene RNA profiling, collections of test replicates, and integration of complementary data. In order to simplify the presentation, we focus on using RNA profiling for distinguishing presence versus absence of chemical hazards, but the principles discussed can be extended to other types of 'omics measurements, multi-class problems, and regression. We include a supplemental file demonstrating many of the concepts using the open source R statistical package.

Aquatic concentrations of chemical analytes compared to ecotoxicity estimates.

We describe screening level estimates of potential aquatic toxicity posed by 227 chemical analytes that were measured in 25 ambient water samples collected as part of a joint USGS/USEPA drinking water plant study. Measured concentrations were compared to biological effect concentration (EC) estimates, including USEPA aquatic life criteria, effective plasma concentrations of pharmaceuticals, published toxicity data summarized in the USEPA ECOTOX database, and chemical structure-based predictions. Potential dietary exposures were estimated using a generic 3-tiered food web accumulation scenario. For many analytes, few or no measured effect data were found, and for some analytes, reporting limits exceeded EC estimates, limiting the scope of conclusions. Results suggest occasional occurrence above ECs for copper, aluminum, strontium, lead, uranium, and nitrate. Sparse effect data for manganese, antimony, and vanadium suggest that these analytes may occur above ECs, but additional effect data would be desirable to corroborate EC estimates. These conclusions were not affected by bioaccumulation estimates. No organic analyte concentrations were found to exceed EC estimates, but ten analytes had concentrations in excess of 1/10th of their respective EC: triclocarban, norverapamil, progesterone, atrazine, metolachlor, triclosan, para-nonylphenol, ibuprofen, venlafaxine, and amitriptyline, suggesting more detailed characterization of these analytes.

Nationwide reconnaissance of contaminants of emerging concern in source and treated drinking waters of the United States.

When chemical or microbial contaminants are assessed for potential effect or possible regulation in ambient and drinking waters, a critical first step is determining if the contaminants occur and if they are at concentrations that may cause human or ecological health concerns. To this end, source and treated drinking water samples from 29 drinking water treatment plants (DWTPs) were analyzed as part of a two-phase study to determine whether chemical and microbial constituents, many of which are considered contaminants of emerging concern, were detectable in the waters. Of the 84 chemicals monitored in the 9 Phase I DWTPs, 27 were detected at least once in the source water, and 21 were detected at least once in treated drinking water. In Phase II, which was a broader and more comprehensive assessment, 247 chemical and microbial analytes were measured in 25 DWTPs, with 148 detected at least once in the source water, and 121 detected at least once in the treated drinking water. The frequency of detection was often related to the analyte's contaminant class, as pharmaceuticals and anthropogenic waste indicators tended to be infrequently detected and more easily removed during treatment, while per and polyfluoroalkyl substances and inorganic constituents were both more frequently detected and, overall, more resistant to treatment. The data collected as part of this project will be used to help inform evaluation of unregulated contaminants in surface water, groundwater, and drinking water.

Initial development of a multigene 'omics-based exposure biomarker for pyrethroid pesticides.

Omics technologies have long since promised to address a number of long standing issues related to environmental regulation. Despite considerable resource investment, there are few examples where these tools have been adopted by the regulatory community, which is in part due to a focus of most studies on discovery rather than assay development. The current work describes the initial development of an omics based assay using 48h Pimephales promelas (FHM) larvae for identifying aquatic exposures to pyrethroid pesticides. Larval FHM were exposed to seven concentrations of each of four pyrethroids (permethrin, cypermethrin, esfenvalerate and bifenthrin) in order to establish dose response curves. Then, in three separate identical experiments, FHM were exposed to a single equitoxic concentration of each pyrethroid, corresponding to 33% of the calculated LC50. All exposures were separated by weeks and all materials were either cleaned or replaced between runs in an attempt to maintain independence among exposure experiments. Gene expression classifiers were developed using the random forest algorithm for each exposure and evaluated first by cross-validation using hold out organisms from the same exposure experiment and then against test sets of each pyrethroid from separate exposure experiments. Bifenthrin exposed organisms generated the highest quality classifier, demonstrating an empirical Area Under the Curve (eAUC) of 0.97 when tested against bifenthrin exposed organisms from other exposure experiments and 0.91 against organisms exposed to any of the pyrethroids. An eAUC of 1.0 represents perfect classification with no false positives or negatives. Additionally, the bifenthrin classifier was able to successfully classify organisms from all other pyrethroid exposures at multiple concentrations, suggesting a potential utility for detecting cumulative exposures. Considerable run-to-run variability was observed both in exposure concentrations and molecular responses of exposed fish across exposure experiments. The application of a calibration step in analysis successfully corrected this, resulting in a significantly improved classifier. Classifier evaluation suggested the importance of considering a number of aspects of experimental design when developing an expression based tool for general use in ecological monitoring and risk assessment, such as the inclusion of multiple experimental runs and high replicate numbers.

Evaluating the extent of pharmaceuticals in surface waters of the United States using a National-scale Rivers and Streams Assessment survey.

To assess the potential exposure of aquatic ecosystems to active pharmaceutical ingredients, the authors conducted a national-scale, probability-based statistical survey of the occurrence of these compounds in surface waters of the United States. The survey included 182 sampling sites and targeted rivers with close proximity to urban areas. The 46 analytes reported represent many classes of active pharmaceutical ingredients (APIs), including antibiotics, diuretics, antihypertensives, anticonvulsants, and antidepressants. Of the 46 analytes, 37 were detected in at least 1 sampling location. Sulfamethoxazole (an antibiotic) was the most frequently detected compound, being measured in 141 of the 182 surface waters surveyed at concentrations ranging up to 570 ng/L. Ten of the compounds were detected in 20% or more of the sampling sites. Weighted means of the analytical measurements are used with the statistical survey design and analysis to provide national estimates of the extent of contamination for these APIs in the nation's urban rivers. Published 2015 Wiley Periodicals, Inc. on behalf of SETAC. This article is a US Government work and as such, is in the public domain in the United States of America.

Concentrations of prioritized pharmaceuticals in effluents from 50 large wastewater treatment plants in the US and implications for risk estimation.

We measured concentrations of 56 active pharmaceutical ingredients (APIs) in effluent samples from 50 large wastewater treatment plants across the US. Hydrochlorothiazide was found in every sample. Metoprolol, atenolol, and carbamazepine were found in over 90% of the samples. Valsartan had the highest concentration (5300 ng/L), and also had the highest average concentration (1600 ng/L) across all 50 samples. Estimates of potential risks to healthy human adults were greatest for six anti-hypertensive APIs (lisinopril, hydrochlorothiazide, valsartan, atenolol, enalaprilat, and metoprolol), but nevertheless suggest risks of exposure to individual APIs as well as their mixtures are generally very low. Estimates of potential risks to aquatic life were also low for most APIs, but suggest more detailed study of potential ecological impacts from four analytes (sertraline, propranolol, desmethylsertraline, and valsartan).

Predicting variability of aquatic concentrations of human pharmaceuticals.

Potential exposure to active pharmaceutical ingredients (APIs) in the aquatic environment is a subject of ongoing concern. We recently published maximum likely exposure rates for several hundred human prescription pharmaceuticals commonly used in the US. These rates were estimated from nationally aggregated marketing data and wastewater production rates. The accuracy of these estimates is unclear, and it is unclear how to use the national-level estimates of exposure to predict local exposure rates. In this study we compare our previous predicted environmental concentrations (PECs), which were based on marketing data, with PECs based on regulatory data. We then use local dispensing rates for 12 APIs along with local wastewater production rates to estimate the distribution of local PECs relative to national averages, in order to identify an 'application factor' suitable for converting national-level PECs into reliable bounds for local concentrations. We compare the national-level PECs and the proposed application factor with measured environmental concentrations (MECs) published in 62 recent peer-reviewed publications. Regulatory data-based national average PECs are uniformly lower than marketing data-based national average PECs, corroborating the intended conservative nature of the marketing data-based PECs. Variability in local API usage and wastewater production rates suggest local PECs may occasionally exceed national averages by about 10-fold. Multiplying national average PECs by an 'application factor' of 10 and comparing the resulting predicted maximum local PECs to published MEC data for 83 APIs corroborates the usefulness of 10-fold adjusted national PECs as a reasonable ceiling for measured environmental concentrations.

Risks to aquatic organisms posed by human pharmaceutical use.

In order to help prioritize future research efforts within the US, risks associated with exposure to human prescription pharmaceutical residues in wastewater were estimated from marketing and pharmacological data. Masses of 371 active pharmaceutical ingredients (APIs) dispensed in the US in 2004 were estimated from marketing data, and then divided by therapeutic dose rate to normalize for potency. Metabolic inactivation of the 50 most dispensed APIs was estimated from published data, and active metabolites were tabulated. Comparing maximum likely average wastewater concentrations of API-associated activity to exposure rates that produce therapeutic effects in humans suggests that the threat to healthy human adults from aquatic exposure is low, even when likely mixture effects are considered. Comparing predicted wastewater concentrations to human therapeutic plasma concentrations suggests that some APIs may be present at sufficient concentrations to affect organisms which eliminate them inefficiently. Comparing predicted antimicrobial concentrations to published minimum inhibitory concentrations suggests that antibacterial APIs in wastewater, but probably not antifungal APIs, may select for low-level antimicrobial resistance. The taxonomic distribution of molecular targets of the 50 most dispensed APIs suggests that potential effects of some APIs are likely restricted to vertebrates, while other APIs can probably affect many eukaryotic and prokaryotic clades.