A Computational Model of the Rainbow Trout Hypothalamus-Pituitary-Ovary-Liver Axis

From Qualitative to Quantitative AOP: A Case Study of Neurodegeneration

Adverse outcome pathways (AOPs) include a sequence of events that connect a molecular-level initiating event with an adverse outcome at the cellular level for human health endpoints, or at the population level for ecological endpoints. When there is enough quantitative understanding of the relationships between key events in an AOP, a mathematical model may be developed to connect key events in a quantitative AOP (qAOP). Ideally, a qAOP will reduce the time and resources spent for chemical toxicity testing and risk assessment and enable the extrapolation of data collected at the molecular-level by in vitro assays, for example, to predict whether an adverse outcome may occur. Here, we review AOPs in the AOPWiki, an AOP repository, to determine best practices that would facilitate conversion from AOP to qAOP. Then, focusing on a particular case study, acetylcholinesterase inhibition leading to neurodegeneration, we describe specific methods and challenges. Examples of challenges include the availability and collection of quantitative data amenable to model development, the lack of studies that measure multiple key events, and model accessibility or transferability across platforms. We conclude with recommendations for improving key event and key event relationship descriptions in the AOPWiki that facilitate the transition of qualitative AOPs to qAOPs.

Reference gene validation for quantification of gene expression during ovarian development of turbot (Scophthalmus maximus)

Quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) is a powerful and sensitive method used in gene expression analysis. Suitable reference genes, which are stable under all experimental circumstances and tissues significantly improve the accuracy of qRT-PCR data. In this study, the stability of six genes, namely, 18S ribosomal RNA (18s), beta-actin (actb), elongation factor 1-alpha (ef1α), glyceraldehyde-3-phosphate-dehydrogenase (gapdh), cathepsin D (ctsd), and beta-2-microglobulin (b2m) were evaluated as potential references for qRT-PCR analysis. The genes were examined in the hypothalamus-pituitary-ovary-liver (HPOL) axis throughout turbot ovarian development via using the geNorm, NormFinder and BestKeeper algorithms. Results showed that the most stable reference genes were ef1α, actb, and ctsd in the hypothalamus, pituitary, ovary and liver, respectively. The best-suited gene combinations for normalization were 18s, ef1α, and ctsd in the hypothalamus; actb, ctsd, and 18s in the pituitary; actb, and ctsd in the ovary; gapdh and ctsd in the liver. Moreover, the expression profile of estrogen receptor α (erα) manifested no significant difference normalization to the aforementioned best-suited gene during turbot ovarian development. However, no single gene or pair of genes is suitable as an internal control and account for the amplification differences among the four tissues during ovarian development. In summary, these results provide a basic data for the optimal reference gene selection and obtain highly accurate normalization of qRT-PCR data in HPOL axis-related gene expression analysis during turbot ovarian development.

In vitro exposure of vitellogenic rainbow trout ovarian follicles to endocrine disrupting chemicals can alter basal estradiol-17β production and responsiveness to a gonadotropin challenge

Endogenous estrogens play major roles in many aspects of female reproductive development in fish. In order to develop a relatively high-throughput assay to determine the potential impact on reproductive development, vitellogenic rainbow trout ovarian follicles were exposed to a suite of contaminants in vitro and then assessed for the ability to produce estradiol-17β (E2) after a 500 ng/ml salmon gonadotropin (sGTH) challenge. There was a positive correlation between ovarian follicle size and E2 production, but an inverse correlation between size and responsiveness to sGTH. Significant impacts on E2 levels were observed following treatment with different endocrine disrupting chemicals, such as 17α-ethinylestradiol (EE2), prochloraz, or trenbolone. EE2 was remarkably potent and significantly reduced ovarian follicle responsiveness to sGTH at concentrations as low as 0.1 nM. Of the other contaminants tested, only tamoxifen impacted E2 levels, and only at concentrations near the limits of solubility. Flutamide, fluoxetine, 4-hydroxy tamoxifen, hydroxyflutamide, and norfluoxetine had little or no impact. Quantitative PCR analyses of steroidogenesis-related genes were carried out on EE2 treated ovarian follicles, but significant transcriptional responses to EE2 were not observed. Overall, this study suggests that xenoestrogens and anti-estrogens are more likely to interfere with ovarian E2 synthesis than other classes of EDCs. This also provides a template for further testing of the effects of EDCs on ovarian function.

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.

REGULATION OF REPRODUCTIVE PROCESSES WITH DYNAMIC ENERGY BUDGETS

1. The simple bioenergetic models in the family of Dynamic Energy Budget (DEB) consist of a small number of state equations quantifying universal processes, such as feeding, maintenance, development, reproduction and growth. Linking these organismal level processes to underlying suborganismal mechanisms at the molecular, cellular and organ level constitutes a major challenge for predictive ecological risk assessments. 2. Motivated by the need for process-based models to evaluate the impact of endocrine disruptors on ecologically relevant endpoints, this paper develops and evaluates two general modeling modules describing demand-driven feedback mechanisms exerted by gonads on the allocation of resources to production of reproductive matter within the DEB modeling framework. 3. These modules describe iteroparous, semelparous and batch-mode reproductive strategies. The modules have a generic form with both positive and negative feedback components; species and sex specific attributes of endocrine regulation can be added without changing the core of the modules. 4. We demonstrate that these modules successfully describe time-resolved measurements of wet weight of body, ovaries and liver, egg diameter and plasma content of vitellogenin and estradiol in rainbow trout (Oncorynchus mykiss) by fitting these models to published and new data, which require the estimation of less than two parameters per data type. 5. We illustrate the general applicability of the concept of demand-driven allocation of resources to reproduction as worked out in this paper by evaluating one of the modules with data on growth and seed production of an annual plant, the common bean (Phaseolis vulgaris).

Incorporating Suborganismal Processes into Dynamic Energy Budget Models for Ecological Risk Assessment

A working group at the National Institute for Mathematical and Biological Synthesis (NIMBioS) explored the feasibility of integrating 2 complementary approaches relevant to ecological risk assessment. Adverse outcome pathway (AOP) models provide "bottom-up" mechanisms to predict specific toxicological effects that could affect an individual's ability to grow, reproduce, and/or survive from a molecular initiating event. Dynamic energy budget (DEB) models offer a "top-down" approach that reverse engineers stressor effects on growth, reproduction, and/or survival into modular characterizations related to the acquisition and processing of energy resources. Thus, AOP models quantify linkages between measurable molecular, cellular, or organ-level events, but they do not offer an explicit route to integratively characterize stressor effects at higher levels of organization. While DEB models provide the inherent basis to link effects on individuals to those at the population and ecosystem levels, their use of abstract variables obscures mechanistic connections to suborganismal biology. To take advantage of both approaches, we developed a conceptual model to link DEB and AOP models by interpreting AOP key events as measures of damage-inducing processes affecting DEB variables and rates. We report on the type and structure of data that are generated for AOP models that may also be useful for DEB models. We also report on case studies under development that merge information collected for AOPs with DEB models and highlight some of the challenges. Finally, we discuss how the linkage of these 2 approaches can improve ecological risk assessment, with possibilities for progress in predicting population responses to toxicant exposures within realistic environments. Integr Environ Assess Manag 2018;14:615-624. © 2018 SETAC.

In Silico Computational Transcriptomics Reveals Novel Endocrine Disruptors in Largemouth Bass ( Micropterus salmoides)

In recent years, decreases in fish populations have been attributed, in part, to the effect of environmental chemicals on ovarian development. To understand the underlying molecular events we developed a dynamic model of ovary development linking gene transcription to key physiological end points, such as gonadosomatic index (GSI), plasma levels of estradiol (E2) and vitellogenin (VTG), in largemouth bass ( Micropterus salmoides). We were able to identify specific clusters of genes, which are affected at different stages of ovarian development. A subnetwork was identified that closely linked gene expression and physiological end points and by interrogating the Comparative Toxicogenomic Database (CTD), quercetin and tretinoin (ATRA) were identified as two potential candidates that may perturb this system. Predictions were validated by investigation of reproductive associated transcripts using qPCR in ovary and in the liver of both male and female largemouth bass treated after a single injection of quercetin and tretinoin (10 and 100 μg/kg). Both compounds were found to significantly alter the expression of some of these genes. Our findings support the use of omics and online repositories for identification of novel, yet untested, compounds. This is the first study of a dynamic model that links gene expression patterns across stages of ovarian development.

Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology

A quantitative adverse outcome pathway (qAOP) consists of one or more biologically based, computational models describing key event relationships linking a molecular initiating event (MIE) to an adverse outcome. A qAOP provides quantitative, dose-response, and time-course predictions that can support regulatory decision-making. Herein we describe several facets of qAOPs, including (a) motivation for development, (b) technical considerations, (c) evaluation of confidence, and (d) potential applications. The qAOP used as an illustrative example for these points describes the linkage between inhibition of cytochrome P450 19A aromatase (the MIE) and population-level decreases in the fathead minnow (FHM; Pimephales promelas). The qAOP consists of three linked computational models for the following: (a) the hypothalamic-pitutitary-gonadal axis in female FHMs, where aromatase inhibition decreases the conversion of testosterone to 17β-estradiol (E2), thereby reducing E2-dependent vitellogenin (VTG; egg yolk protein precursor) synthesis, (b) VTG-dependent egg development and spawning (fecundity), and (c) fecundity-dependent population trajectory. While development of the example qAOP was based on experiments with FHMs exposed to the aromatase inhibitor fadrozole, we also show how a toxic equivalence (TEQ) calculation allows use of the qAOP to predict effects of another, untested aromatase inhibitor, iprodione. While qAOP development can be resource-intensive, the quantitative predictions obtained, and TEQ-based application to multiple chemicals, may be sufficient to justify the cost for some applications in regulatory decision-making.