Molecular Networks of Platinum Drugs and Their Interaction with microRNAs in Cancer.

The precise mechanism of resistance to anti-cancer drugs such as platinum drugs is not fully revealed. To reveal the mechanism of drug resistance, the molecular networks of anti-cancer drugs such as cisplatin, carboplatin, oxaliplatin, and arsenic trioxide were analyzed in several types of cancers. Since diffuse-type stomach adenocarcinoma, which has epithelial-mesenchymal transition (EMT)-like characteristics, is more malignant than intestinal-type stomach adenocarcinoma, the gene expression and molecular networks in diffuse- and intestinal-type stomach adenocarcinomas were analyzed. Analysis of carboplatin revealed the causal network in diffuse large B-cell lymphoma. The upstream regulators of the molecular networks of cisplatin-treated lung adenocarcinoma included the anti-cancer drug trichostatin A (TSA), a histone deacetylase inhibitor. The upstream regulator analysis of cisplatin revealed an increase in FAS, BTG2, SESN1, and CDKN1A, and the involvement of the tumor microenvironment pathway. The molecular networks were predicted to interact with several microRNAs, which may contribute to the identification of new drug targets for drug-resistant cancer. Analysis of oxaliplatin, a platinum drug, revealed that the SPINK1 pancreatic cancer pathway is inactivated in ischemic cardiomyopathy. The study showed the importance of the molecular networks of anti-cancer drugs and tumor microenvironment in the treatment of cancer resistant to anti-cancer drugs.

Gene Expression and Pathway Analysis in Rat Brain and Liver After Exposure to Royal Demolition Explosive (Hexahydro-1,3,5-Trinitro-1,3,5-Triazine).

The nitramine explosive, hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) is associated with acute and chronic toxicity in mammals and targets both the central nervous system and liver. After a single oral dose of RDX in male rats, the systemic distribution of RDX and the toxicodynamic response was measured using clinical chemistry and Affymetrix Rat Genome® 230 2.0 gene expression arrays, respectively. Nominal doses of 0, 9 and 36 mg/kg pure RDX were administered to animals followed by liver, cerebral cortex, and hippocampus gene expression analysis at 0, 3.5, 24, and 48 hours. RDX quickly entered the liver and brain, increasing up to 24 hours. For the 36 mg/kg dose, RDX was still measurable in liver and brain at 48 hours, but was non-detectible for the 9 mg/kg dose. At 3.5 hours, the time within which most convulsions reportedly occur after RDX ingestion, the hippocampus displayed the highest response for both gene expression and pathways, while the cortex was relatively non-responsive. The top 2 impacted pathways, primarily involved in neurotransmission, were the GABAergic and glutamatergic pathways. High numbers of genes also responded to RDX in the liver with P450 metabolism pathways significantly involved. Compared to the liver, the hippocampus displayed more consistent biological effects across dose and time with neurotransmission pathways predominating. Overall, based on gene expression data, RDX responses were high in both the hippocampus and liver, but were minimal in the cerebral cortex. These results identify the hippocampus as an important target for RDX based on gene expression.

Male fathead minnow transcriptomes and associated chemical analytes in the Milwaukee estuary system.

Contaminants of Emerging Concern (CECs) can be measured in waters across the United States, including the tributaries of the Great Lakes. The extent to which these contaminants affect gene expression in aquatic wildlife is unclear. This dataset presents the full hepatic transcriptomes of laboratory-reared fathead minnows (Pimephales promelas) caged at multiple sites within the Milwaukee Estuary Area of Concern and control sites. Following 4 days of in situ exposure, liver tissue was removed from males at each site for RNA extraction and sequencing, yielding a total of 116 samples from which libraries were prepared, pooled, and sequenced. For each exposure site, 179 chemical analytes were also assessed. These data were created with the intention of inviting research on possible transcriptomic changes observed in aquatic species exposed to CECs. Access to both full sequencing reads of animal samples as well as water contaminant data across multiple Great Lakes sites will allow others to explore the health of these ecosystems in support of the aims of the Great Lakes Restoration Initiative.

Current ecotoxicity testing needs among selected U.S. federal agencies.

U.S. regulatory and research agencies use ecotoxicity test data to assess the hazards associated with substances that may be released into the environment, including but not limited to industrial chemicals, pharmaceuticals, pesticides, food additives, and color additives. These data are used to conduct hazard assessments and evaluate potential risks to aquatic life (e.g., invertebrates, fish), birds, wildlife species, or the environment. To identify opportunities for regulatory uses of non-animal replacements for ecotoxicity tests, the needs and uses for data from tests utilizing animals must first be clarified. Accordingly, the objective of this review was to identify the ecotoxicity test data relied upon by U.S. federal agencies. The standards, test guidelines, guidance documents, and/or endpoints that are used to address each of the agencies' regulatory and research needs regarding ecotoxicity testing are described in the context of their application to decision-making. Testing and information use, needs, and/or requirements relevant to the regulatory or programmatic mandates of the agencies taking part in the Interagency Coordinating Committee on the Validation of Alternative Methods Ecotoxicology Workgroup are captured. This information will be useful for coordinating efforts to develop and implement alternative test methods to reduce, refine, or replace animal use in chemical safety evaluations.

Developmental, Behavioral and Transcriptomic Changes in Zebrafish Embryos after Smoke Dye Exposure.

(1) Background: Disperse Blue 14, Disperse Red 9, Solvent Red 169 and Solvent Yellow 33 have been used to color smoke; however, they have not been comprehensively assessed for their potential health hazards. (2) Methods: To assess the effects of these dyes, zebrafish embryos were exposed from 6 to 120 h post fertilization (hpf) to 10-55 µM Disperse Red 9, 1-50 µM Solvent Red 169, 7.5-13.5 µM Solvent Yellow 33 or 133-314 µM Disperse Blue 14. Embryos were monitored for adverse effects on gene expression at 48 hpf as well as for mortality, development and behavior at 120 hpf. The dyes were examined for their potential to cross the blood-brain barrier. (3) Results: Solvent Yellow 33 and Disperse Blue 14 impaired development and behavior at all concentrations. Disperse Red 9 impaired behavior at all concentrations and development at all concentrations except for 10 µM. Solvent Red 169 caused no effects. Mortality was only seen in Disperse Blue 14 at 261.5 and 314 µM. Gene expression indicated impacts on neurodevelopment and folate and retinol metabolism as potential mechanisms of toxicity. (4) Conclusions: Smoke dyes have a high potential for causing developmental changes and neurotoxicity and should be examined more closely using comprehensive approaches as used here.

Integration of Adverse Outcome Pathways, Causal Networks and 'Omics to Support Chemical Hazard Assessment.

Several approaches have been used in an attempt to simplify and codify the events that lead to adverse effects of chemicals including systems biology, 'omics, in vitro assays and frameworks such as the Adverse Outcome Pathway (AOP). However, these approaches are generally not integrated despite their complementary nature. Here we propose to integrate toxicogenomics data, systems biology information and AOPs using causal biological networks to define Key Events in AOPs. We demonstrate this by developing a causal subnetwork of 28 nodes that represents the Key Event of regenerative proliferation - a critical event in AOPs for liver cancer. We then assessed the effects of three chemicals known to cause liver injury and cell proliferation (carbon tetrachloride, aflatoxin B1, thioacetamide) and two with no known cell proliferation effects (diazepam, simvastatin) on the subnetwork using rat liver gene expression data from the toxicogenomic database Open TG-GATEs. Cyclin D1 (Ccnd1), a gene both causally linked to and sufficient to infer regenerative proliferation activity, was overexpressed after exposures to carbon tetrachloride, aflatoxin B1 and thioacetamide, but not in exposures to diazepam and simvastatin. These results were consistent with known effects on rat livers and liver pathology of exposed rats. Using these approaches, we demonstrate that transcriptomics, AOPs and systems biology can be applied to examine the presence and progression of AOPs in order to better understand the hazards of chemical exposure.

Translatable pathways classification (TransPath-C) for inferring processes germane to human biology from animal studies data: example application in neurobiology.

How to translate insights gained from studies in one organismal species for what is most likely to be germane in another species, such as from mice to humans, is a ubiquitous challenge in basic biology as well as biomedicine. This is an especially difficult problem when there are few molecular features that are obviously important in both species for a given phenotype of interest. Neuropathologies are a prominent realm of this complication. Schizophrenia is complex psychiatric disorder that affects 1% of the population. Many genetic factors have been proposed to drive the development of schizophrenia, and the 22q11 microdeletion (MD) syndrome has been shown to dramatically increase this risk. Due to heterogeneity of presentation of symptoms, diagnosis and formulation of treatment options for patients can often be delayed, and there is an urgent need for novel therapeutics directed toward the treatment of schizophrenia. Here, we present a novel computational approach, Translational Pathways Classification (TransPath-C), that can be used to identify shared pathway dysregulation between mouse models and human schizophrenia cohorts. This method uses variation of pathway activation in the mouse model to predict both mouse and human disease phenotype. Analysis of shared dysregulated pathways called out by both the mouse and human classifiers of TransPath-C can identify pathways that can be targeted in both preclinical and human cohorts of schizophrenia. In application to the 22q11 MD mouse model, our findings suggest that PAR1 pathway activation found upregulated in this mouse phenotype is germane for the corresponding human schizophrenia cohort such that inhibition of PAR1 may offer a novel therapeutic target.

Morphological and Behavioral Effects in Zebrafish Embryos after Exposure to Smoke Dyes.

Solvent Violet 47 (SV47) and Disperse Blue 14 (DB14) are two anthraquinone dyes that were previously used in different formulations for the production of violet-colored smoke. Both dyes have shown potential for toxicity; however, there is no comprehensive understanding of their effects. Zebrafish embryos were exposed to SV47 or DB14 from 6 to 120 h post fertilization (hpf) to assess the dyes' potential adverse effects on developing embryos. The potential ability of both dyes to cross the blood-brain barrier was also assessed. At concentrations between 0.55 and 5.23 mg/L, SV47 showed a dose-dependent increase in mortality, jaw malformation, axis curvature, and edemas. At concentrations between 0.15 and 7.54 mg/L, DB14 did not have this same dose-dependence but had similar morphological outcomes at the highest doses. Nevertheless, while SV47 showed significant mortality from 4.20 mg/L, there was no significant mortality on embryos exposed to DB14. Regardless, decreased locomotor movement was observed at all concentrations of DB14, suggesting an adverse neurodevelopmental effect. Overall, our results showed that at similar concentrations, SV47 and DB14 caused different types of phenotypic effects in zebrafish embryos.

Perfluorooctanesulfonic Acid-Induced Toxicity on Zebrafish Embryos in the Presence or Absence of the Chorion.

Perfluorooctanesulfonic acid (PFOS) is a perfluorinated compound used in many industrial and consumer products. It has been linked to a broad range of adverse effects in several species, including zebrafish (Danio rerio). The zebrafish embryo is a widely used vertebrate model to elucidate potential adverse effects of chemicals because it is amenable to medium and high throughput. However, there is limited research on the full extent of the impact the chorion has on those effects. Results from the present study indicate that the presence of the chorion affected the timing and incidence of mortality as well as morphometric endpoints such as spinal curvature and swim bladder inflation in zebrafish embryos exposed to PFOS. Furthermore, removal of the chorion prior to exposure resulted in a lower threshold of sensitivity to PFOS for effects on transcriptional expression within the peroxisome proliferator-activated receptor (PPAR) nuclear signaling pathway. Perturbation of PPAR pathway gene expression can result in disruption of metabolic signaling and regulation, which can adversely affect development, energy availability, and survival. It can be concluded that removal of the chorion has significant effects on the timing and incidence of impacts associated with PFOS exposure, and more research is warranted to fully elucidate the protective role of the chorion and the critical timing of these events. Environ Toxicol Chem 2021;40:780-791. Published 2020. This article is a US Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

AOPERA: A proposed methodology and inventory of effective tools to link chemicals to adverse outcome pathways.

New approaches, like the Adverse Outcome Pathway (AOP) framework, have been developed to describe how chemicals cause toxicity by linking in vitro assays to adverse health outcomes. However, approaches, tools and resources for development of AOPs have not been well described. Here we review information resources for AOP development and define a streamlined process for linking a chemical to an existing AOP. We propose a four step process to facilitate AOP development: link the uncharacterized chemical directly to Molecular Initiating Events, Key Events, or Adverse Outcomes; identify analogs with toxicological information for the uncharacterized chemical; link the characterized chemical (initial chemical if characterized, a characterized analog if initial chemical is not) to Molecular Initiating Events, Key Events, or Adverse Outcomes; and identify AOPs that contain the Molecular Initiating Events, Key Events, or Adverse Outcomes that were found in Steps 1 and 3. The process and library of informational resources proposed and tested here served as the foundation for an informational online tool (AOPERA) that helps practitioners identify their current-state knowledge gaps, navigate the four-step process, and connect to relevant resources. AOPERA can be found at https://igbb.github.io/AOPERA_HTML. Additionally, we anticipate that by simplifying and standardizing the process of linking a chemical to a known AOP, we will lower the barrier to entry for this objective and increase its accessibility to new practitioners. In turn, this may increase the demand for new or improved AOPs to which practitioners can link chemicals, thereby contributing to the expansion of the library of known AOPs.

Stochastically modeling multiscale stationary biological processes.

Large scale biological responses are inherently uncertain, in part as a consequence of noisy systems that do not respond deterministically to perturbations and measurement errors inherent to technological limitations. As a result, they are computationally difficult to model and current approaches are notoriously slow and computationally intensive (multiscale stochastic models), fail to capture the effects of noise across a system (chemical kinetic models), or fail to provide sufficient biological fidelity because of broad simplifying assumptions (stochastic differential equations). We use a new approach to modeling multiscale stationary biological processes that embraces the noise found in experimental data to provide estimates of the parameter uncertainties and the potential mis-specification of models. Our approach models the mean stationary response at each biological level given a particular expected response relationship, capturing variation around this mean using conditional Monte Carlo sampling that is statistically consistent with training data. A conditional probability distribution associated with a biological response can be reconstructed using this method for a subset of input values, which overcomes the parameter identification problem. Our approach could be applied in addition to dynamical modeling methods (see above) to predict uncertain biological responses over experimental time scales. To illustrate this point, we apply the approach to a test case in which we model the variation associated with measurements at multiple scales of organization across a reproduction-related Adverse Outcome Pathway described for teleosts.

Building and Applying Quantitative Adverse Outcome Pathway Models for Chemical Hazard and Risk Assessment.

An important goal in toxicology is the development of new ways to increase the speed, accuracy, and applicability of chemical hazard and risk assessment approaches. A promising route is the integration of in vitro assays with biological pathway information. We examined how the adverse outcome pathway (AOP) framework can be used to develop pathway-based quantitative models useful for regulatory chemical safety assessment. By using AOPs as initial conceptual models and the AOP knowledge base as a source of data on key event relationships, different methods can be applied to develop computational quantitative AOP models (qAOPs) relevant for decision making. A qAOP model may not necessarily have the same structure as the AOP it is based on. Useful AOP modeling methods range from statistical, Bayesian networks, regression, and ordinary differential equations to individual-based models and should be chosen according to the questions being asked and the data available. We discuss the need for toxicokinetic models to provide linkages between exposure and qAOPs, to extrapolate from in vitro to in vivo, and to extrapolate across species. Finally, we identify best practices for modeling and model building and the necessity for transparent and comprehensive documentation to gain confidence in the use of qAOP models and ultimately their use in regulatory applications. Environ Toxicol Chem 2019;38:1850-1865. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Keanu: a novel visualization tool to explore biodiversity in metagenomes.

BACKGROUND: One of the main challenges when analyzing complex metagenomics data is the fact that large amounts of information need to be presented in a comprehensive and easy-to-navigate way. In the process of analyzing FASTQ sequencing data, visualizing which organisms are present in the data can be useful, especially with metagenomics data or data suspected to be contaminated. Here, we describe the development and application of a command-line tool, Keanu, for visualizing and exploring sample content in metagenomics data. We developed Keanu as an interactive tool to make viewing complex data easier. RESULTS: Keanu, a tool for exploring sequence content, helps a user to understand the presence and abundance of organisms in a sample by analyzing alignments against a database that contains taxonomy data and displaying them in an interactive web page. The content of a sample can be presented either as a collapsible tree, with node size indicating abundance, or as a bilevel partition graph, with arc size indicating abundance. Here, we illustrate how Keanu works by exploring shotgun metagenomics data from a sample collected from a bluff that contained paleosols and a krotovina in an alpine site in Ft. Greely, Alaska. CONCLUSIONS: Keanu provides a simple means by which researchers can explore and visualize species present in sequence data generated from complex communities and environments. Keanu is written in Python and is freely available at https://github.com/IGBB/keanu .

A modular approach for assembly of quantitative adverse outcome pathways.

The adverse outcome pathway (AOP) framework is a conceptual construct that mechanistically links molecular initiating events to adverse biological outcomes through a series of causal key events (KEs) that represent the perturbation of the biological system. Quantitative, predictive AOPs are necessary for screening emerging contaminants and potential substitutes to inform their prioritization for testing. In practice, they are not widely used because they can be costly to develop and validate. A modular approach for assembly of quantitative AOPs, based on existing knowledge, would allow for rapid development of biological pathway models to screen contaminants for potential hazards and prioritize them for subsequent testing and modeling. For each pair of KEs, a quantitative KE relationship (KER) can be derived as a response-response function or a conditional probability matrix describing the anticipated change in a KE based on the response of the prior KE. This transfer of response across KERs can be used to assemble a quantitative AOP. Here we demonstrate the use of proposed approach in two cases: inhibition of cytochrome P450 aromatase leading to reduced fecundity in fathead minnows and ionic glutamate receptor mediated excitotoxicity leading to memory impairment in rodents. The model created from these chains have value in characterizing the pathway and the potential or relative level of toxicological effect anticipated. This approach to simplistic, modular AOP models has wide applicability for rapid development of biological pathway models.

Chemical hazard prediction and hypothesis testing using quantitative adverse outcome pathways.

Current efforts in chemical safety are focused on utilizing human in vitro or alternative animal data in biological pathway context. However, it remains unclear how biological pathways, and toxicology data developed in that context, can be used to quantitatively facilitate decision-making.  The objective of this work is to determine if hypothesis testing using Adverse Outcome Pathways (AOPs) can provide quantitative chemical hazard predictions.  Current methods for predicting hazards of chemicals in a biological pathway context were extensively reviewed, specific case studies examined and computational modeling used to demonstrate quantitative hazard prediction based on an AOP. Since AOPs are chemically agnostic, we propose that AOPs function as hypotheses for how specific chemicals may cause adverse effects via specific pathways. Three broad approaches were identified for testing the hypothesis with AOPs, semi-quantitative weight of evidence, probabilistic, and mechanistic modeling. We then demonstrate how these approaches could be used to test hypotheses using high throughput in vitro data and alternative animal data. Finally, we discuss standards in development and documentation that would facilitate use in a regulatory context. We conclude that quantitative AOPs provide a flexible hypothesis framework for predicting chemical hazards. It accommodates a wide range of approaches that are useful at many stages and build upon one another to become increasingly quantitative.

Multiple environmental stressors induce complex transcriptomic responses indicative of phenotypic outcomes in Western fence lizard.

BACKGROUND: The health and resilience of species in natural environments is increasingly challenged by complex anthropogenic stressor combinations including climate change, habitat encroachment, and chemical contamination. To better understand impacts of these stressors we examined the individual- and combined-stressor impacts of malaria infection, food limitation, and 2,4,6-trinitrotoluene (TNT) exposures on gene expression in livers of Western fence lizards (WFL, Sceloporus occidentalis) using custom WFL transcriptome-based microarrays. RESULTS: Computational analysis including annotation enrichment and correlation analysis identified putative functional mechanisms linking transcript expression and toxicological phenotypes. TNT exposure increased transcript expression for genes involved in erythropoiesis, potentially in response to TNT-induced anemia and/or methemoglobinemia and caused dose-specific effects on genes involved in lipid and overall energy metabolism consistent with a hormesis response of growth stimulation at low doses and adverse decreases in lizard growth at high doses. Functional enrichment results were indicative of inhibited potential for lipid mobilization and catabolism in TNT exposures which corresponded with increased inguinal fat weights and was suggestive of a decreased overall energy budget. Malaria infection elicited enriched expression of multiple immune-related functions likely corresponding to increased white blood cell (WBC) counts. Food limitation alone enriched functions related to cellular energy production and decreased expression of immune responses consistent with a decrease in WBC levels. CONCLUSIONS: Despite these findings, the lizards demonstrated immune resilience to malaria infection under food limitation with transcriptional results indicating a fully competent immune response to malaria, even under bio-energetic constraints. Interestingly, both TNT and malaria individually increased transcriptional expression of immune-related genes and increased overall WBC concentrations in blood; responses that were retained in the TNT x malaria combined exposure. The results demonstrate complex and sometimes unexpected responses to multiple stressors where the lizards displayed remarkable resiliency to the stressor combinations investigated.

Production of tunable nanomaterials using hierarchically assembled bacteriophages.

Large-scale fabrication of precisely defined nanostructures with tunable functions is critical to the exploitation of nanoscience and nanotechnology for production of electronic devices, energy generators, biosensors, and bionanomedicines. Although self-assembly processes have been developed to exploit biological molecules for functional materials, the resulting nanostructures and functions are still very limited, and scalable synthesis is far from being realized. Recently, we have established a bacteriophage-based biomimetic process, called 'self-templating assembly'. We used bacteriophage as a nanofiber model system to exploit its liquid crystalline structure for the creation of diverse hierarchically organized structures. We have also demonstrated that genetic modification of functional peptides of bacteriophage results in structures that can be used as soft and hard tissue-regenerating materials, biosensors, and energy-generating materials. Here, we describe a comprehensive protocol to perform genetic engineering of phage, liter-scale amplification, purification, and self-templating assembly, and suggest approaches for characterizing hierarchical phage nanostructures using optical microscopy, atomic-force microscopy (AFM), and scanning electron microscopy (SEM). We also discuss sources of contamination, common mistakes during the fabrication process, and quality-control measures to ensure reproducible material production. The protocol takes ∼8-10 d to complete.

Rapid effects of the aromatase inhibitor fadrozole on steroid production and gene expression in the ovary of female fathead minnows (Pimephales promelas).

Cytochrome P450 aromatase catalyzes conversion of C19 androgens to C18 estrogens and is critical for normal reproduction in female vertebrates. Fadrozole is a model aromatase inhibitor that has been shown to suppress estrogen production in the ovaries of fish. However, little is known about the early impacts of aromatase inhibition on steroid production and gene expression in fish. Adult female fathead minnows (Pimephales promelas) were exposed via water to 0, 5, or 50µg fadrozole/L for a time-course of 0.5, 1, 2, 4, and 6h, or 0 or 50µg fadrozole/L for a time-course of 6, 12, and 24h. We examined ex vivo ovarian 17ß-estradiol (E2) and testosterone (T) production, and plasma E2 concentrations from each study. Expression profiles of genes known or hypothesized to be impacted by fadrozole including aromatase (cytochrome P450 [cyp] 19a1a), steriodogenic acute regulatory protein (star), cytochrome P450 side-chain cleavage (cyp11a), cytochrome P450 17 alpha hydroxylase/17,20 lyase (cyp17), and follicle stimulating hormone receptor (fshr) were measured in the ovaries by quantitative real-time polymerase chain reaction (QPCR). In addition, broader ovarian gene expression was examined using a 15k fathead minnow microarray. The 5µg/L exposure significantly reduced ex vivo E2 production by 6h. In the 50µg/L treatment, ex vivo E2 production was significantly reduced after just 2h of exposure and remained depressed at all time-points examined through 24h. Plasma E2 concentrations were significantly reduced as early as 4h after initiation of exposure to either 5 or 50µg fadrozole/L and remained depressed throughout 24h in the 50µg/L exposure. Ex vivo T concentrations remained unchanged throughout the time-course. Expression of transcripts involved in steroidogenesis increased within the first 24h suggesting rapid induction of a mechanism to compensate for fadrozole inhibition of aromatase. Microarray results also showed fadrozole exposure caused concentration- and time-dependent changes in gene expression profiles in many HPG-axis pathways as early as 4h. This study provides insights into the very rapid effects of aromatase inhibition on steroidogenic processes in fish.

Prioritization of Contaminants of Emerging Concern in Wastewater Treatment Plant Discharges Using Chemical:Gene Interactions in Caged Fish.

We examined whether contaminants present in surface waters could be prioritized for further assessment by linking the presence of specific chemicals to gene expression changes in exposed fish. Fathead minnows were deployed in cages for 2, 4, or 8 days at three locations near two different wastewater treatment plant discharge sites in the Saint Louis Bay, Duluth, MN and one upstream reference site. The biological impact of 51 chemicals detected in the surface water of 133 targeted chemicals was determined using biochemical endpoints, exposure activity ratios for biological and estrogenic responses, known chemical:gene interactions from biological pathways and knowledge bases, and analysis of the covariance of ovary gene expression with surface water chemistry. Thirty-two chemicals were significantly linked by covariance with expressed genes. No estrogenic impact on biochemical endpoints was observed in male or female minnows. However, bisphenol A (BPA) was identified by chemical:gene covariation as the most impactful estrogenic chemical across all exposure sites. This was consistent with identification of estrogenic effects on gene expression, high BPA exposure activity ratios across all test sites, and historical analysis of the study area. Gene expression analysis also indicated the presence of nontargeted chemicals including chemotherapeutics consistent with a local hospital waste stream. Overall impacts on gene expression appeared to be related to changes in treatment plant function during rain events. This approach appears useful in examining the impacts of complex mixtures on fish and offers a potential route in linking chemical exposure to adverse outcomes that may reduce population sustainability.

The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment.

In conjunction with the second International Environmental Omics Symposium (iEOS) conference, held at the University of Liverpool (United Kingdom) in September 2014, a workshop was held to bring together experts in toxicology and regulatory science from academia, government and industry. The purpose of the workshop was to review the specific roles that high-content omics datasets (eg, transcriptomics, metabolomics, lipidomics, and proteomics) can hold within the adverse outcome pathway (AOP) framework for supporting ecological and human health risk assessments. In light of the growing number of examples of the application of omics data in the context of ecological risk assessment, we considered how omics datasets might continue to support the AOP framework. In particular, the role of omics in identifying potential AOP molecular initiating events and providing supportive evidence of key events at different levels of biological organization and across taxonomic groups was discussed. Areas with potential for short and medium-term breakthroughs were also discussed, such as providing mechanistic evidence to support chemical read-across, providing weight of evidence information for mode of action assignment, understanding biological networks, and developing robust extrapolations of species-sensitivity. Key challenges that need to be addressed were considered, including the need for a cohesive approach towards experimental design, the lack of a mutually agreed framework to quantitatively link genes and pathways to key events, and the need for better interpretation of chemically induced changes at the molecular level. This article was developed to provide an overview of ecological risk assessment process and a perspective on how high content molecular-level datasets can support the future of assessment procedures through the AOP framework.