Stephen W. Edwards

Endocrine disrupting chemicals in fish: Developing exposure indicators and predictive models of effects based on mechanism of action

Knowledge of possible toxic mechanisms (or modes) of action (MOA) of chemicals can provide valuable insights as to appropriate methods for assessing exposure and effects, thereby reducing uncertainties related to extrapolation across species, endpoints and chemical structure. However, MOA-based testing seldom has been used for assessing the ecological risk of chemicals. This is in part because past regulatory mandates have focused more on adverse effects of chemicals (reductions in survival, growth or reproduction) than the pathways through which these effects are elicited. A recent departure from this involves endocrine-disrupting chemicals (EDCs), where there is a need to understand both MOA and adverse outcomes. To achieve this understanding, advances in predictive approaches are required whereby mechanistic changes caused by chemicals at the molecular level can be translated into apical responses meaningful to ecological risk assessment. In this paper we provide an overview and illustrative results from a large, integrated project that assesses the effects of EDCs on two small fish models, the fathead minnow (Pimephales promelas) and zebrafish (Danio rerio). For this work a systems-based approach is being used to delineate toxicity pathways for 12 model EDCs with different known or hypothesized toxic MOA. The studies employ a combination of state-of-the-art genomic (transcriptomic, proteomic, metabolomic), bioinformatic and modeling approaches, in conjunction with whole animal testing, to develop response linkages across biological levels of organization. This understanding forms the basis for predictive approaches for species, endpoint and chemical extrapolation. Although our project is focused specifically on EDCs in fish, we believe that the basic conceptual approach has utility for systematically assessing exposure and effects of chemicals with other MOA across a variety of biological systems.

Systems Biology and Mode of Action Based Risk Assessment

The applications of systems biology approaches have greatly increased in the past decade largely as a consequence of the human genome project and technological advances in genomics and proteomics. Systems approaches have been used in the medical and pharmaceutical realm for diagnostic purposes and target identification. During this same period, the use of mode of action (MOA) for risk assessment has been increasing and there is a need for quantitative risk assessments on an ever-growing number of environmental chemicals. Genome-wide (i.e., global) measurements provide both a discovery engine for identifying MOA and an information base for subsequent evaluation of MOA when conducting a risk assessment. These genome-wide measurements are not chosen based on the hypothesized MOA and therefore represent an unbiased check of the comprehensiveness of an MOA. In addition, optimal design for MOA studies is critical to provide the time and dose dependent data required for quantitative model building. Finally, identification of biomarkers and bioindicators of disease in humans provides a viable way to extrapolate from disease outcomes measured at high exposure levels to those at low exposure levels and thus provide the opportunity to reduce or perhaps eliminate in vivo animal testing. To realize the full potential of these approaches, larger integrated projects which include all these individual components are necessary.

Reverse Engineering Adverse Outcome Pathways

The toxicological effects of many stressors are mediated through unknown, or incompletely characterized, mechanisms of action. The application of reverse engineering complex interaction networks from high dimensional omics data (gene, protein, metabolic, signaling) can be used to overcome these limitations. This approach was used to characterize adverse outcome pathways (AOPs) for chemicals that disrupt the hypothalamus-pituitary-gonadal endocrine axis in fathead minnows (FHM, Pimephales promelas). Gene expression changes in FHM ovaries in response to seven different chemicals, over different times, doses, and in vivo versus in vitro conditions, were captured in a large data set of 868 arrays. Potential AOPs of the antiandrogen flutamide were examined using two mutual information-based methods to infer gene regulatory networks and potential AOPs. Representative networks from these studies were used to predict network paths from stressor to adverse outcome as candidate AOPs. The relationship of individual chemicals to an adverse outcome can be determined by following perturbations through the network in response to chemical treatment, thus leading to the nodes associated with the adverse outcome. Identification of candidate pathways allows for formation of testable hypotheses about key biological processes, biomarkers, or alternative endpoints that can be used to monitor an AOP. Finally, the unique challenges facing the application of this approach in ecotoxicology were identified and a road map for the utilization of these tools presented.

A transcriptomics-based biological framework for studying mechanisms of endocrine disruption in small fish species.

This study sought to construct a transcriptomics-based framework of signal transduction pathways, transcriptional regulatory networks, and the hypothalamic-pituitary gonadal (HPG) axis in zebrafish (Danio rerio) to facilitate formulation of specific, testable hypotheses regarding the mechanisms of endocrine disruption in fish. For the analyses involved, we used data from a total of more than 300 microarrays representing 58 conditions, which encompassed 4 tissue types from zebrafish of both genders exposed for 1 of 3 durations to 10 different test chemicals (17alpha-ethynyl estradiol, fadrozole, 17beta-trenbolone, fipronil, prochloraz, flutamide, muscimol, ketoconazole, trilostane, and vinclozolin). Differentially expressed genes were identified by one class t-tests for each condition, and those with false discovery rates of less than 40% and treatment/control ratios > or =1.3-fold were mapped to orthologous human, mouse, and rat pathways by Ingenuity Pathway Analysis to look for overrepresentation of known biological pathways. To complement the analysis of known biological pathways, the genes regulated by approximately 1800 transcription factors were inferred using the ARACNE mutual information-based algorithm. The resulting gene sets for all transcriptional factors, along with a group of compiled HPG-axis genes and approximately 130 publicly available biological pathways, were analyzed for their responses to the 58 treatment conditions by Gene Set Enrichment Analysis (GSEA) and its variant, Extended-GSEA. The biological pathways and transcription factors associated with multiple distinct treatments showed substantial interactions among the HPG-axis, TGF-beta, p53, and several of their cross-talking partners. These candidate networks/pathways have a variety of profound impacts on such cellular functions as stress response, cell cycle, and apoptosis.

Exposure science and the U.S. EPA National Center for Computational Toxicology

The emerging field of computational toxicology applies mathematical and computer models and molecular biological and chemical approaches to explore both qualitative and quantitative relationships between sources of environmental pollutant exposure and adverse health outcomes. The integration of modern computing with molecular biology and chemistry will allow scientists to better prioritize data, inform decision makers on chemical risk assessments and understand a chemicals progression from the environment to the target tissue within an organism and ultimately to the key steps that trigger an adverse health effect. In this paper, several of the major research activities being sponsored by Environmental Protection Agencys National Center for Computational Toxicology are highlighted. Potential links between research in computational toxicology and human exposure science are identified. As with the traditional approaches for toxicity testing and hazard assessment, exposure science is required to inform design and interpretation of high-throughput assays. In addition, common themes inherent throughout National Center for Computational Toxicology research activities are highlighted for emphasis as exposure science advances into the 21st century.

Uses of NHANES Biomarker Data for Chemical Risk Assessment: Trends, Challenges, and Opportunities

Background Each year, the U.S. NHANES measures hundreds of chemical biomarkers in samples from thousands of study participants. These biomarker measurements are used to establish population reference ranges, track exposure trends, identify population subsets with elevated exposures, and prioritize research needs. There is now interest in further utilizing the NHANES data to inform chemical risk assessments. Objectives This article highlights a) the extent to which U.S. NHANES chemical biomarker data have been evaluated, b) groups of chemicals that have been studied, c) data analysis approaches and challenges, and d) opportunities for using these data to inform risk assessments. Methods A literature search (1999–2013) was performed to identify publications in which U.S. NHANES data were reported. Manual curation identified only the subset of publications that clearly utilized chemical biomarker data. This subset was evaluated for chemical groupings, data analysis approaches, and overall trends. Results A small percentage of the sampled NHANES-related publications reported on chemical biomarkers (8% yearly average). Of 11 chemical groups, metals/metalloids were most frequently evaluated (49%), followed by pesticides (9%) and environmental phenols (7%). Studies of multiple chemical groups were also common (8%). Publications linking chemical biomarkers to health metrics have increased dramatically in recent years. New studies are addressing challenges related to NHANES data interpretation in health risk contexts. Conclusions This article demonstrates growing use of NHANES chemical biomarker data in studies that can impact risk assessments. Best practices for analysis and interpretation must be defined and adopted to allow the full potential of NHANES to be realized. Citation Sobus JR, DeWoskin RS, Tan YM, Pleil JD, Phillips MB, George BJ, Christensen K, Schreinemachers DM, Williams MA, Cohen Hubal EA, Edwards SW. 2015. Uses of NHANES biomarker data for chemical risk assessment: trends, challenges, and opportunities. Environ Health Perspect 123:919–927; http://dx.doi.org/10.1289/ehp.1409177

Advancing the next generation of health risk assessment.

Background: Over the past 20 years, knowledge of the genome and its function has increased dramatically, but risk assessment methodologies using such knowledge have not advanced accordingly. Objective: This commentary describes a collaborative effort among several federal and state agencies to advance the next generation of risk assessment. The objective of the NexGen program is to begin to incorporate recent progress in molecular and systems biology into risk assessment practice. The ultimate success of this program will be based on the incorporation of new practices that facilitate faster, cheaper, and/or more accurate assessments of public health risks. Methods: We are developing prototype risk assessments that compare the results of traditional, data-rich risk assessments with insights gained from new types of molecular and systems biology data. In this manner, new approaches can be validated, traditional approaches improved, and the value of different types of new scientific information better understood. Discussion and Conclusions: We anticipate that these new approaches will have a variety of applications, such as assessment of new and existing chemicals in commerce and the design of chemical products and processes that reduce or eliminate the use or generation of hazardous substances. Additionally, results of the effort are likely to spur further research and test methods development. Full implementation of new approaches is likely to take 10–20 years.

The Next Generation of Risk Assessment Multi-Year Study—Highlights of Findings, Applications to Risk Assessment, and Future Directions

Background: The Next Generation (NexGen) of Risk Assessment effort is a multi-year collaboration among several organizations evaluating new, potentially more efficient molecular, computational, and systems biology approaches to risk assessment. This article summarizes our findings, suggests applications to risk assessment, and identifies strategic research directions. Objective: Our specific objectives were to test whether advanced biological data and methods could better inform our understanding of public health risks posed by environmental exposures. Methods: New data and methods were applied and evaluated for use in hazard identification and dose–response assessment. Biomarkers of exposure and effect, and risk characterization were also examined. Consideration was given to various decision contexts with increasing regulatory and public health impacts. Data types included transcriptomics, genomics, and proteomics. Methods included molecular epidemiology and clinical studies, bioinformatic knowledge mining, pathway and network analyses, short-duration in vivo and in vitro bioassays, and quantitative structure activity relationship modeling. Discussion: NexGen has advanced our ability to apply new science by more rapidly identifying chemicals and exposures of potential concern, helping characterize mechanisms of action that influence conclusions about causality, exposure–response relationships, susceptibility and cumulative risk, and by elucidating new biomarkers of exposure and effects. Additionally, NexGen has fostered extensive discussion among risk scientists and managers and improved confidence in interpreting and applying new data streams. Conclusions: While considerable uncertainties remain, thoughtful application of new knowledge to risk assessment appears reasonable for augmenting major scope assessments, forming the basis for or augmenting limited scope assessments, and for prioritization and screening of very data limited chemicals. Citation: Cote I, Andersen ME, Ankley GT, Barone S, Birnbaum LS, Boekelheide K, Bois FY, Burgoon LD, Chiu WA, Crawford-Brown D, Crofton KM, DeVito M, Devlin RB, Edwards SW, Guyton KZ, Hattis D, Judson RS, Knight D, Krewski D, Lambert J, Maull EA, Mendrick D, Paoli GM, Patel CJ, Perkins EJ, Poje G, Portier CJ, Rusyn I, Schulte PA, Simeonov A, Smith MT, Thayer KA, Thomas RS, Thomas R, Tice RR, Vandenberg JJ, Villeneuve DL, Wesselkamper S, Whelan M, Whittaker C, White R, Xia M, Yauk C, Zeise L, Zhao J, DeWoskin RS. 2016. The Next Generation of Risk Assessment multiyear study—highlights of findings, applications to risk assessment, and future directions. Environ Health Perspect 124:1671–1682; http://dx.doi.org/10.1289/EHP233

Integrating Publicly Available Data to Generate Computationally Predicted Adverse Outcome Pathways for Fatty Liver.

Newin vitrotesting strategies make it possible to design testing batteries for large numbers of environmental chemicals. Full utilization of the results requires knowledge of the underlying biological networks and the adverse outcome pathways (AOPs) that describe the route from early molecular perturbations to an adverse outcome. Curation of a formal AOP is a time-intensive process and a rate-limiting step to designing these test batteries. Here, we describe a method for integrating publicly available data in order to generate computationally predicted AOP (cpAOP) scaffolds, which can be leveraged by domain experts to shorten the time for formal AOP development. A network-based workflow was used to facilitate the integration of multiple data types to generate cpAOPs. Edges between graph entities were identified through direct experimental or literature information, or computationally inferred using frequent itemset mining. Data from the TG-GATEs and ToxCast programs were used to channel large-scale toxicogenomics information into a cpAOP network (cpAOPnet) of over 20 000 relationships describing connections between chemical treatments, phenotypes, and perturbed pathways as measured by differential gene expression and high-throughput screening targets. The resulting fatty liver cpAOPnet is available as a resource to the community. Subnetworks of cpAOPs for a reference chemical (carbon tetrachloride, CCl4) and outcome (fatty liver) were compared with published mechanistic descriptions. In both cases, the computational approaches approximated the manually curated AOPs. The cpAOPnet can be used for accelerating expert-curated AOP development and to identify pathway targets that lack genomic markers or high-throughput screening tests. It can also facilitate identification of key events for designing test batteries and for classification and grouping of chemicals for follow up testing.

Adverse outcome pathways: From research to regulation scientific workshop report.

An adverse outcome pathway (AOP) helps to organize existing knowledge on chemical mode of action, starting with a molecular initiating event such as receptor binding, continuing through key events, and ending with an adverse outcome such as reproductive impairment. AOPs can help identify knowledge gaps where more research is needed to understand the underlying mechanisms, aid in chemical hazard characterization, and guide the development of new testing approaches that use fewer or no animals. A September 2014 workshop in Bethesda, Maryland considered how the AOP concept could improve regulatory assessments of chemical toxicity. Scientists from 21 countries, representing industry, academia, regulatory agencies, and special interest groups, attended the workshop, titled Adverse Outcome Pathways: From Research to Regulation. Workshop plenary presentations were followed by breakout sessions that considered regulatory acceptance of AOPs and AOP-based tools, criteria for building confidence in an AOP for regulatory use, and requirements to build quantitative AOPs and AOP networks. Discussions during the closing session emphasized a need to increase transparent and inclusive collaboration, especially with disciplines outside of toxicology. Additionally, to increase impact, working groups should be established to systematically prioritize and develop AOPs. Multiple collaborative projects and follow-up activities resulted from the workshop.