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The Multispecies Ovary Tissue Histology Electronic Repository (MOTHER) is a publicly accessible repository of ovary histology images. MOTHER includes hundreds of images from nonhuman primates, as well as ovary histology images from an expanding range of other species. Along with an image, MOTHER provides metadata about the image, and for selected species, follicle identification annotations. Ongoing work includes assisting scientists with contributing their histology images, creation of manual and automated (via machine learning) processing pipelines to identify and count ovarian follicles in different stages of development, and the incorporation of that data into the MOTHER database (MOTHER-DB). MOTHER will be a critical data repository storing and disseminating high-value histology images that are essential for research into ovarian function, fertility, and intra-species variability., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for the Study of Reproduction. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The goal of the Multispecies Ovary Tissue Histology Electronic Repository (MOTHER) project is to establish a collection of nonhuman ovary histology images for multiple species as a resource for researchers and educators. An important component of sharing scientific data is the inclusion of the contextual metadata that describes the data. MOTHER extends the Ecological Metadata Language (EML) for documenting research data, leveraging its data provenance and usage license with the inclusion of metadata for ovary histology images. The design of the MOTHER metadata includes information on the donor animal, including reproductive cycle status, the slide and its preparation. MOTHER also extends the ezEML tool, called ezEML+MOTHER, for the specification of the metadata. The design of the MOTHER database (MOTHERDB) captures the metadata about the histology images, providing a searchable resource for discovering relevant images. MOTHER also defines a curation process for the ingestion of a collection of images and its metadata, verifying the validity of the metadata before its inclusion in the MOTHER collection. A Web search provides the ability to identify relevant images based on various characteristics in the metadata itself, such as genus and species, using filters.

Several adverse outcome pathways (AOPs) have linked molecular initiating events like aromatase inhibition, androgen receptor (AR) agonism, and estrogen receptor (ER) antagonism to reproductive impairment in adult fish. Estrogen receptor agonists can also cause adverse reproductive effects, however, the early key events (KEs) in an AOP leading to this are mostly unknown. The primary aim of this study was to develop hypotheses regarding the potential mechanisms through which exposure to ER agonists might lead to reproductive impairment in female fish. Mature fathead minnows were exposed to 1 or 10 ng 17α-ethynylestradiol (EE2)/L or 10 or 100 µg bisphenol A (BPA)/L for 14 d. The response to EE2 and BPA was contrasted with the effects of 500 ng/L of 17β-trenbolone (TRB), an AR agonist, as well as TRB combined with the low and high concentrations of EE2 or BPA tested individually. Exposure to 10 ng EE2/L, 100 µg BPA/L, TRB, or the various mixtures with TRB caused significant decreases in plasma concentrations of 17β-estradiol. Exposure to TRB alone caused a significant reduction in plasma vitellogenin (VTG), but VTG was unaffected or even increased in females exposed to EE2 or BPA alone or, in most cases, in mixtures with TRB. Over the course of the 14-d exposure, the only treatments that clearly did not affect egg production were 1 ng EE2/L and 10 µg BPA/L. Based on these results and knowledge of hypothalamic-pituitary-gonadal axis function, we hypothesize an AOP whereby decreased production of maturation-inducing steroid leading to impaired oocyte maturation and ovulation, possibly due to negative feedback or direct inhibitory effects of membrane ER activation, could be responsible for causing adverse reproductive impacts in female fish exposed to ER agonists., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier B.V.)

To reduce the use of intact animals for chemical safety testing, while ensuring protection of ecosystems and human health, there is a demand for new approach methodologies (NAMs) that provide relevant scientific information at a quality equivalent to or better than traditional approaches. The present case study examined whether bioactivity and associated potency measured in an in vitro screening assay for aromatase inhibition could be used together with an adverse outcome pathway (AOP) and mechanistically based computational models to predict previously uncharacterized in vivo effects. Model simulations were used to inform designs of 60-h and 10-21-day in vivo exposures of adult fathead minnows (Pimephales promelas) to three or four test concentrations of the in vitro aromatase inhibitor imazalil ranging from 0.12 to 260 µg/L water. Consistent with an AOP linking aromatase inhibition to reproductive impairment in fish, exposure to the fungicide resulted in significant reductions in ex vivo production of 17β-estradiol (E2) by ovary tissue (≥165 µg imazalil/L), plasma E2 concentrations (≥74 µg imazalil/L), vitellogenin (Vtg) messenger RNA expression (≥165 µg imazalil/L), Vtg plasma concentrations (≥74 µg imazalil/L), uptake of Vtg into oocytes (≥260 µg imazalil/L), and overall reproductive output in terms of cumulative fecundity, number of spawning events, and eggs per spawning event (≥24 µg imazalil/L). Despite many potential sources of uncertainty in potency and efficacy estimates based on model simulations, observed magnitudes of apical effects were quite consistent with model predictions, and in vivo potency was within an order of magnitude of that predicted based on in vitro relative potency. Overall, our study suggests that NAMs and AOP-based approaches can support meaningful reduction and refinement of animal testing. Environ Toxicol Chem 2023;42:100-116. © 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA., (© 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Sinitsyn, Garcia-Reyero and Watanabe.)

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 ).

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., (© 2018 SETAC.)

Background: A challenge of in vitro to in vivo extrapolation (IVIVE) is to predict the physical state of organisms exposed to chemicals in the environment from in vitro exposure assay data. Although toxicokinetic modeling approaches promise to bridge in vitro screening data with in vivo effects, they are often encumbered by a need for redesign or re-parameterization when applied to different tissues or chemicals., Results: We demonstrate a parameterization of reverse toxicokinetic (rTK) models developed for the adult zebrafish (Danio rerio) based upon particle swarm optimizations (PSO) of the chemical uptake and degradation rates that predict bioconcentration factors (BCF) for a broad range of chemicals. PSO reveals a relationship between chemical uptake and decomposition parameter values that predicts chemical-specific BCF values with moderate statistical agreement to a limited yet diverse chemical dataset, and all without a need to retrain the model to new data., Conclusions: The presented model requires only the octanol-water partitioning ratio to predict BCFs to a fidelity consistent with existing QSAR models. This success begs re-evaluation of the modeling assumptions; specifically, it suggests that chemical uptake into arterial blood may be limited by transport across gill membranes (diffusion) rather than by counter-current flow between gill lamellae (convection). Therefore, more detailed molecular modeling of aquatic respiration may further improve predictive accuracy of the rTK approach.

Despite its importance to reproduction, certain mechanisms of early ovarian development remain a mystery. To improve our understanding, we constructed the first cell-based computational model of ovarian development in mice that is divided into two phases: Phase I spans embryonic day 5.5 (E5.5) to E12.5; and Phase II spans E12.5 to postnatal day 2. We used the model to investigate four mechanisms: in Phase I, (i) whether primordial germ cells (PGCs) undergo mitosis during migration; and (ii) if the mechanism for secretion of KIT ligand from the hindgut resembles inductive cell-cell signaling or is secreted in a static manner; and in Phase II, (iii) that changes in cellular adhesion produce germ cell nest breakdown; and (iv) whether localization of primordial follicles in the cortex of the ovary is due to proliferation of granulosa cells. We found that the combination of the first three hypotheses produced results that aligned with experimental images and PGC abundance data. Results from the fourth hypothesis did not match experimental images, which suggests that more detailed processes are involved in follicle localization. Phase I and Phase II of the model reproduce experimentally observed cell counts and morphology well. A sensitivity analysis identified contact energies, mitotic rates, KIT chemotaxis strength, and diffusion rate in Phase I and oocyte death rate in Phase II as parameters with the greatest impact on model predictions. The results demonstrate that the computational model can be used to understand unknown mechanisms, generate new hypotheses, and serve as an educational tool., (© The Author 2017. Published by Oxford University Press Society for the Study of Reproduction.)

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.

Background: Normal development of reproductive organs is crucial for successful reproduction. In mice the early ovarian developmental process occurs during the embryonic and postnatal period and is regulated through a series of molecular signaling events. Early ovarian development in mice is a seventeen-day process that begins with the rise of six primordial germ cells on embryonic day five (E5) and ends with the formation of primordial follicles on postnatal day two (P2)., Results: We reviewed the current literature and created a visual representation of early ovarian development that depicts the important molecular events and associated phenotypic outcomes based on primary data. The visual representation shows the timeline of key signaling interactions and regulation of protein expression in different cells involved in ovarian development. The major developmental events were divided into five phases: 1) origin of germ cells and maintenance of pluripotency; 2) primordial germ cell migration; 3) sex differentiation; 4) formation of germ cell nests; and 5) germ cell nest breakdown and primordial follicle formation., Conclusions: This review and visual representation provide a summary of the current scientific understanding of the key regulation and signaling during ovarian development and highlights areas needing further study. The visual representation can be used as an educational resource to link molecular events with phenotypic outcomes; serves as a tool to generate new hypotheses and predictions of adverse reproductive outcomes due to perturbations at the molecular and cellular levels; and provides a comprehendible foundation for computational model development and hypothesis testing.

There is international concern about chemicals that alter endocrine system function in humans and/or wildlife and subsequently cause adverse effects. We previously developed a mechanistic computational model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows exposed to a model aromatase inhibitor, fadrozole (FAD), to predict dose-response and time-course behaviors for apical reproductive endpoints. Initial efforts to develop a computational model describing adaptive responses to endocrine stress providing good fits to empirical plasma 17β-estradiol (E2) data in exposed fish were only partially successful, which suggests that additional regulatory biology processes need to be considered. In this study, we addressed short-comings of the previous model by incorporating additional details concerning CYP19A (aromatase) protein synthesis. Predictions based on the revised model were evaluated using plasma E2 concentrations and ovarian cytochrome P450 (CYP) 19A aromatase mRNA data from two fathead minnow time-course experiments with FAD, as well as from a third 4-day study. The extended model provides better fits to measured E2 time-course concentrations, and the model accurately predicts CYP19A mRNA fold changes and plasma E2 dose-response from the 4-d concentration-response study. This study suggests that aromatase protein synthesis is an important process in the biological system to model the effects of FAD exposure., (Copyright © 2016. Published by Elsevier Inc.)

Fish spawning is often used as an integrated measure of reproductive toxicity, and an indicator of aquatic ecosystem health in the context of forecasting potential population-level effects considered important for ecological risk assessment. Consequently, there is a need for flexible, widely-applicable, biologically-based models that can predict changes in fecundity in response to chemical exposures, based on readily measured biochemical endpoints, such as plasma vitellogenin (VTG) concentrations, as input parameters. Herein we describe a MATLAB® version of an oocyte growth dynamics model for fathead minnows (Pimephales promelas) with a graphical user interface based upon a previously published model developed with MCSim software and evaluated with data from fathead minnows exposed to an androgenic chemical, 17β-trenbolone. We extended the evaluation of our new model to include six chemicals that inhibit enzymes involved in steroid biosynthesis: fadrozole, ketoconazole, propiconazole, prochloraz, fenarimol, and trilostane. In addition, for unexposed fathead minnows from group spawning design studies, and those exposed to the six chemicals, we evaluated whether the model is capable of predicting the average number of eggs per spawn and the average number of spawns per female, which was not evaluated previously. The new model is significantly improved in terms of ease of use, platform independence, and utility for providing output in a format that can be used as input into a population dynamics model. Model-predicted minimum and maximum cumulative fecundity over time encompassed the observed data for fadrozole and most propiconazole, prochloraz, fenarimol and trilostane treatments, but did not consistently replicate results from ketoconazole treatments. For average fecundity (eggs•female(-1)•day(-1)), eggs per spawn, and the number of spawns per female, the range of model-predicted values generally encompassed the experimentally observed values. Overall, we found that the model predicts reproduction metrics robustly and its predictions capture the variability in the experimentally observed data.

Adverse outcome pathways (AOPs) organize knowledge on the progression of toxicity through levels of biological organization. By determining the linkages between toxicity events at different levels, AOPs lay the foundation for mechanism-based alternative testing approaches to hazard assessment. Here, we focus on growth impairment in fish to illustrate the initial stages in the process of AOP development for chronic toxicity outcomes. Growth is an apical endpoint commonly assessed in chronic toxicity tests for which a replacement is desirable. Based on several criteria, we identified reduction in food intake to be a suitable key event for initiation of middle-out AOP development. To start exploring the upstream and downstream links of this key event, we developed three AOP case studies, for pyrethroids, selective serotonin reuptake inhibitors (SSRIs) and cadmium. Our analysis showed that the effect of pyrethroids and SSRIs on food intake is strongly linked to growth impairment, while cadmium causes a reduction in growth due to increased metabolic demands rather than changes in food intake. Locomotion impairment by pyrethroids is strongly linked to their effects on food intake and growth, while for SSRIs their direct influence on appetite may play a more important role. We further discuss which alternative tests could be used to inform on the predictive key events identified in the case studies. In conclusion, our work demonstrates how the AOP concept can be used in practice to assess critically the knowledge available for specific chronic toxicity cases and to identify existing knowledge gaps and potential alternative tests., (Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.)

To elucidate the effects of chemicals on populations of different species in the environment, efficient testing and modeling approaches are needed that consider multiple stressors and allow reliable extrapolation of responses across species. An adverse outcome pathway (AOP) is a concept that provides a framework for organizing knowledge about the progression of toxicity events across scales of biological organization that lead to adverse outcomes relevant for risk assessment. In this paper, we focus on exploring how the AOP concept can be used to guide research aimed at improving both our understanding of chronic toxicity, including delayed toxicity as well as epigenetic and transgenerational effects of chemicals, and our ability to predict adverse outcomes. A better understanding of the influence of subtle toxicity on individual and population fitness would support a broader integration of sublethal endpoints into risk assessment frameworks. Detailed mechanistic knowledge would facilitate the development of alternative testing methods as well as help prioritize higher tier toxicity testing. We argue that targeted development of AOPs supports both of these aspects by promoting the elucidation of molecular mechanisms and their contribution to relevant toxicity outcomes across biological scales. We further discuss information requirements and challenges in application of AOPs for chemical- and site-specific risk assessment and for extrapolation across species. We provide recommendations for potential extension of the AOP framework to incorporate information on exposure, toxicokinetics and situation-specific ecological contexts, and discuss common interfaces that can be employed to couple AOPs with computational modeling approaches and with evolutionary life history theory. The extended AOP framework can serve as a venue for integration of knowledge derived from various sources, including empirical data as well as molecular, quantitative and evolutionary-based models describing species responses to toxicants. This will allow a more efficient application of AOP knowledge for quantitative chemical- and site-specific risk assessment as well as for extrapolation across species in the future., (Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Endocrine-disrupting chemicals can affect reproduction and development in humans and wildlife. We developed a computational model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict dose-response and time-course (DRTC) behaviors for endocrine effects of the aromatase inhibitor, fadrozole (FAD). The model describes adaptive responses to endocrine stress involving regulated secretion of a generic gonadotropin (LH/FSH) from the hypothalamic-pituitary complex. For model development, we used plasma 17β-estradiol (E2) concentrations and ovarian cytochrome P450 (CYP) 19A aromatase mRNA data from two time-course experiments, each of which included both an exposure and a depuration phase, and plasma E2 data from a third 4-day study. Model parameters were estimated using E2 concentrations for 0, 0.5, and 3 µg/l FAD exposure concentrations, and good fits to these data were obtained. The model accurately predicted CYP19A mRNA fold changes for controls and three FAD doses (0, 0.5, and 3 µg/l) and plasma E2 dose response from the 4-day study. Comparing the model-predicted DRTC with experimental data provided insight into how the feedback control mechanisms in the HPG axis mediate these changes: specifically, adaptive changes in plasma E2 levels occurring during exposure and "overshoot" occurring postexposure. This study demonstrates the value of mechanistic modeling to examine and predict dynamic behaviors in perturbed systems. As this work progresses, we will obtain a refined understanding of how adaptive responses within the vertebrate HPG axis affect DRTC behaviors for aromatase inhibitors and other types of endocrine-active chemicals and apply that knowledge in support of risk assessments.

Oligonucleotide microarrays and other 'omics' approaches are powerful tools for unsupervised analysis of chemical impacts on biological systems. However, the lack of well annotated biological pathways for many aquatic organisms, including fish, and the limited power of microarray-based analyses to detect low level differential expression of individual genes can hinder the ability to infer and understand chemical effects based on transcriptomic data. Here we report on the supervised assembly of a series of tissue-specific functional gene sets intended to aid transcriptomic analysis of chemical impacts on the female teleost reproductive axis. Gene sets were defined based on an updated graphical systems model of the teleost brain-pituitary-gonadal-hepatic axis. Features depicted in the model were organized into gene sets and mapped to specific probes on three zebrafish (Danio rerio) and two fathead minnow (Pimephales promelas) microarray platforms. Coverage of target genes on the microarrays ranged from 48% for the fathead minnow arrays to 88% for the most current zebrafish platform. Additionally, extended fathead minnow gene sets, incorporating first degree neighbors identified from a Spearman correlation network derived from a large compendium of fathead minnow microarray data, were constructed. Overall, only 14% of the 78 genes queried were connected in the network. Among those, over half had less than five neighbors, while two genes, cyclin b1 and zona pellucida glycoprotein 3, had over 100 first degree neighbors, and were neighbors to one another. Gene set enrichment analyses were conducted using microarray data from a zebrafish hypoxia experiment and fathead minnow time-course experiments conducted with three different endocrine-active chemicals. Results of these analyses demonstrate the utility of the approach for supporting biological inference from ecotoxicogenomic data and comparisons across multiple toxicogenomic experiments. The graphical model, gene mapping, and gene sets described are now available to the scientific community as tools to support ecotoxicogenomic research., (Published by Elsevier B.V.)

Background: Endocrine disrupting chemicals (e.g., estrogens, androgens and their mimics) are known to affect reproduction in fish. 17α-ethynylestradiol is a synthetic estrogen used in birth control pills. 17β-trenbolone is a relatively stable metabolite of trenbolone acetate, a synthetic androgen used as a growth promoter in livestock. Both 17α-ethynylestradiol and 17β-trenbolone have been found in the aquatic environment and affect fish reproduction. In this study, we developed a physiologically-based computational model for female fathead minnows (FHM, Pimephales promelas), a small fish species used in ecotoxicology, to simulate how estrogens (i.e., 17α-ethynylestradiol) or androgens (i.e., 17β-trenbolone) affect reproductive endpoints such as plasma concentrations of steroid hormones (e.g., 17β-estradiol and testosterone) and vitellogenin (a precursor to egg yolk proteins)., Results: Using Markov Chain Monte Carlo simulations, the model was calibrated with data from unexposed, 17α-ethynylestradiol-exposed, and 17β-trenbolone-exposed FHMs. Four Markov chains were simulated, and the chains for each calibrated model parameter (26 in total) converged within 20,000 iterations. With the converged parameter values, we evaluated the model's predictive ability by simulating a variety of independent experimental data. The model predictions agreed with the experimental data well., Conclusions: The physiologically-based computational model represents the hypothalamic-pituitary-gonadal axis in adult female FHM robustly. The model is useful to estimate how estrogens (e.g., 17α-ethynylestradiol) or androgens (e.g., 17β-trenbolone) affect plasma concentrations of 17β-estradiol, testosterone and vitellogenin, which are important determinants of fecundity in fish.

Endocrine disrupting chemicals (EDCs) are known to contaminate aquatic environments and alter the growth and reproduction of organisms. The objective of this study was to evaluate the sensitivity and utility of fathead minnow (Pimephales promelas) early life-stages as a model to measure effects of estrogenic and antiestrogenic EDCs on physiological and gene expression endpoints relative to growth and reproduction. Embryos (<24-h postfertilization, hpf) were exposed to a potent estrogen (17α-ethinyl estradiol, EE(2) , 2, 10, and 50 ng L(-1)); a weak estrogen (mycotoxin zearalenone, ZEAR, same concentrations as above); an antiestrogen (ZM 189, 154; 40, 250, and 1000 ng L(-1)); and to mixtures of EE(2) and ZM until swim-up stage (∼170 hpf). Exposure to all concentrations of ZEAR and to the lowest concentration of ZM resulted in increased body sizes, whereas high concentrations of EE(2) decreased body sizes. There was a significant increase in the frequency of abnormalities (mostly edema) in larvae exposed to all concentrations of EE(2), and high ZEAR, and EE(2) + ZM mixture groups. Expression of growth hormone was upregulated by most of the conditions tested. Exposure to 50 ng L(-1) ZEAR caused an induction of insulin-like growth factor 1, whereas exposure to 40 ng L(-1) ZM caused a downregulation of this gene. Expression of steroidogenic acute regulatory protein gene was significantly upregulated after exposure to all concentrations of EE(2) and luteinizing hormone expression increased significantly in response to all treatments tested. As expected, EE(2) induced vitellogenin expression; however, ZEAR also induced expression of this gene to similar levels compared to EE(2). Overall, exposure to EE(2) + ZM mixture resulted in a different expression pattern compared to single exposures. The results of this study suggest that an early life stage 7-day exposure is sufficient to recognize and evaluate effects of estrogenic compounds on gene expression in this fish model., (Copyright © 2009 Wiley Periodicals, Inc.)

An adverse outcome pathway (AOP) is a sequence of key events from a molecular-level initiating event and an ensuing cascade of steps to an adverse outcome with population-level significance. To implement a predictive strategy for ecotoxicology, the multiscale nature of an AOP requires computational models to link salient processes (e.g., in chemical uptake, toxicokinetics, toxicodynamics, and population dynamics). A case study with domoic acid was used to demonstrate strategies and enable generic recommendations for developing computational models in an effort to move toward a toxicity testing paradigm focused on toxicity pathway perturbations applicable to ecological risk assessment. Domoic acid, an algal toxin with adverse effects on both wildlife and humans, is a potent agonist for kainate receptors (ionotropic glutamate receptors whose activation leads to the influx of Na(+) and Ca²(+)). Increased Ca²(+) concentrations result in neuronal excitotoxicity and cell death, primarily in the hippocampus, which produces seizures, impairs learning and memory, and alters behavior in some species. Altered neuronal Ca²(+) is a key process in domoic acid toxicity, which can be evaluated in vitro. Furthermore, results of these assays would be amenable to mechanistic modeling for identifying domoic acid concentrations and Ca²(+) perturbations that are normal, adaptive, or clearly toxic. In vitro assays with outputs amenable to measurement in exposed populations can link in vitro to in vivo conditions, and toxicokinetic information will aid in linking in vitro results to the individual organism. Development of an AOP required an iterative process with three important outcomes: a critically reviewed, stressor-specific AOP; identification of key processes suitable for evaluation with in vitro assays; and strategies for model development., (© 2010 SETAC.)

The manner in which humans smoke cigarettes is an important determinant of smoking risks. Of the few investigators that have predicted cancer risks from smoking on a chemical-specific basis, most used mainstream cigarette smoke (MCS) carcinogen emissions obtained via machine smoking protocols that only approximate human smoking conditions. Here we use data of Djordjevic et al. [Djordjevic, M.V., Stellman, S.D., Zang, E., 2000. Doses of nicotine and lung carcinogens delivered to cigarette smokers. J. Natl. Cancer Inst. 92, 106-111] for MCS emissions of three carcinogens measured under human smoking conditions to compute probability distributions of incremental lifetime cancer risk (ILCR) values using Monte Carlo simulations. The three carcinogens considered are benzo[a]pyrene, N'-nitrosonornicotine (NNN), and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). Computed NNK ILCR values were compared with lifetime risks of lung cancer (ILCR(CMD)(obsSigma-lung)) derived from American Cancer Society Cancer Prevention Studies (CPS) I and II. Within the Monte Carlo simulation results, NNK was responsible for the greatest ILCR values for all cancer endpoints: median ILCR values for NNK were approximately 18-fold and 120-fold higher than medians for NNN and benzo[a]pyrene, respectively. For "regular" cigarettes, the NNK median ILCR for lung cancer was lower than ILCR(CMD)(obsSigma-lung) from CPS-I and II by >90-fold for men and >4-fold for women. Given what is known about chemical carcinogens in MCS, this study shows that there is a higher incidence of lung cancer from exposure to MCS than can be predicted with current risk assessment methods using available toxicity and emission data.

Background: Aquatic organisms are continuously exposed to complex mixtures of chemicals, many of which can interfere with their endocrine system, resulting in impaired reproduction, development or survival, among others. In order to analyze the effects and mechanisms of action of estrogen/anti-estrogen mixtures, we exposed male fathead minnows (Pimephales promelas) for 48 hours via the water to 2, 5, 10, and 50 ng 17alpha-ethinylestradiol (EE2)/L, 100 ng ZM 189,154/L (a potent antiestrogen known to block activity of estrogen receptors) or mixtures of 5 or 50 ng EE(2)/L with 100 ng ZM 189,154/L. We analyzed gene expression changes in the gonad, as well as hormone and vitellogenin plasma levels., Results: Steroidogenesis was down-regulated by EE(2) as reflected by the reduced plasma levels of testosterone in the exposed fish and down-regulation of genes in the steroidogenic pathway. Microarray analysis of testis of fathead minnows treated with 5 ng EE(2)/L or with the mixture of 5 ng EE(2)/L and 100 ng ZM 189,154/L indicated that some of the genes whose expression was changed by EE(2) were blocked by ZM 189,154, while others were either not blocked or enhanced by the mixture, generating two distinct expression patterns. Gene ontology and pathway analysis programs were used to determine categories of genes for each expression pattern., Conclusion: Our results suggest that response to estrogens occurs via multiple mechanisms, including canonical binding to soluble estrogen receptors, membrane estrogen receptors, and other mechanisms that are not blocked by pure antiestrogens.

Estrogenic chemicals in the aquatic environment have been shown to cause a variety of reproductive anomalies in fish including full sex reversal, intersex, and altered population sex ratios. Two estrogens found in the aquatic environment, 17alpha-ethinylestradiol (EE(2)) and 17beta-estradiol (E(2)), have been measured in wastewater treatment effluents and have been shown to cause adverse effects in fish. To further our understanding of how estrogen exposure affects reproductive endpoints in the male fathead minnow (FHM, Pimephales promelas), a physiologically based computational model was developed of the hypothalamic-pituitary-gonadal (HPG) axis. Apical reproductive endpoints in the model include plasma steroid hormone and vitellogenin concentrations. Using Markov chain Monte Carlo simulation, the model was calibrated with data from unexposed FHM, and FHM exposed to EE(2) and E(2). Independent experimental data sets were used to evaluate model predictions. We found good agreement between our model predictions and a variety of measured reproductive endpoints, although the model underpredicts unexposed FHM reproductive endpoint variances, and overpredicts variances in estrogen-exposed FHM. We conclude that this model provides a robust representation of the HPG axis in male FHM.

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.

Trenbolone, an anabolic androgen, and flutamide, an antiandrogen, are prototypical model compounds for agonism and antagonism of the androgen receptor. We hypothesized that 48 h exposures of female fathead minnows (Pimephales promelas) to environmentally relevant concentrations of these chemicals would alter genes regulated by the androgen receptor and that a mixture of the two compounds would block the effects. Gene expression in the ovaries was analyzed using a fathead minnow-specific 22,000-gene microarray. Flutamide altered abouttwicethe number of genes astrenbolone, most of which appeared to be through pathways not associated with the androgen receptor. A group of 70 genes, of which we could identify 37, were reciprocally regulated by trenbolone and flutamide. These are candidates for specific biomarkers for androgen receptor mediated gene expression. Four genes stand out as specifically related to reproduction: sperm associated antigen 8 (SPAG8), CASP8 and FADD-like apoptosis regulator (CFLAR), corticotropin releasing hormone (CRH), and 3beta-hydroxysteroid dehydrogenases (3beta-HSD). Three notable transcriptional regulators including myelocytomatosis viral oncogene homologue (MYC), Yin Yang 1 (YY1), and interferon regulator factor 1 (IRF1) may function as early molecular switches to control phenotypic changes in ovary tissue architecture and function in response to androgen or antiandrogen exposure.

Recently, researchers have begun looking at changes in gene expression in the fathead minnow (Pimephales promelas) after contaminant exposure as a way to develop biomarkers of exposure and effects. However, the bulk of this research has been conducted on adults, with few studies focusing on early life stages. Expression of selected genes important in growth, development, and reproduction in teleosts was quantified by quantitative polymerase chain reaction during different developmental time periods (from 0 to 28 d postfertilization [dpf]). Over the developmental period studied, there was a significant up-regulation of growth hormone mRNA and no significant changes in the expression of insulin-like growth factor 1. Thyroid hormone receptors A and B were detected in 4 dpf embryos and their expression stayed relatively constant. The variation in cytochrome P45019A mRNA expression was large during the first week of development, returning to 0 dpf expression levels thereafter. Estrogen receptor 2B was up-regulated during the first three weeks postfertilization, returning to prehatch values by 28 dpf. Expression of hydroxysteroid dehydrogenase 3B and steroidogenic acute regulatory protein increased after the third or fourth week postfertilization, respectively. Vitellogenin exhibited a large degree of variation within time points, especially after day 15, and a significant up-regulation for this gene was observed at 7 and 10 dpf. Knowledge of the normal changes in gene expression during embryo and larval development will allow for better experimental design and selection of suitable biomarkers when testing the potential toxicological effects of contaminants in this model fish species.

Jensen et al. [Jensen, K.M., Korte, J.J., Kahl, M.D., Pasha, M.S., Ankley, G.T., 2001. Aspects of basic reproductive biology and endocrinology in the fathead minnow (Pimephales promelas). Comp. Biochem. Physiol. C 128, 127-141.] investigated aspects of the normal reproductive biology of the fathead minnow (FHM, P. promelas), and subsequent studies have generated a large amount of additional reproductive data for endpoints such as plasma steroid hormone and vitellogenin concentrations, spawn interval, and secondary sex characteristics (i.e., nuptial tubercle score and fat pad weight). These data were analyzed and fitted with statistical distributions to improve understanding of the variability in normal, unexposed, adult male (n=154) and female (n=186) FHM. Summary statistics for most endpoints were consistent with results from other more limited studies of FHMs. Male fat pad weight, and in both sexes gonad and liver weights were found to be proportional to body weight. Multiple statistical distributions were found to characterize each endpoint with the exception of spawn interval. Based on one of the largest datasets ever compiled for controlled studies, results presented herein provide a robust point of reference for the quantitative assessment of reproductive processes in the fathead minnow.

Toxicant deliveries (by machine smoking) are compiled and associated cancer risks are calculated for 13 carcinogens from 26 brands of conventional cigarettes categorized as "regular" (R), "light" (Lt), or "ultralight" (ULt), and for a reference cigarette. Eight "potentially reduced exposure product" (PREP) cigarettes are also considered. Because agency-to-agency differences exist in the cancer slope factor (CSF) values adopted for some carcinogens, two CSF sets were used in the calculations: set I [U.S. Environmental Protection Agency (EPA)-accepted values plus California EPA-accepted values as needed to fill data gaps] and set II (vice versa). The potential effects of human smoking patterns on cigarette deliveries are considered. Acetaldehyde, 1,3-butadiene, and acrylonitrile are associated with the largest calculated cancer risks for all 26 brands of conventional cigarettes. The calculated risks are proportional to the smoking dose z (pack-years). Using CSF set I and z = 1 pack-year (7,300 cigarettes), the calculated brand-average incremental lifetime cancer risk ILCR(1)(acetaldehyde) values are R, 6 x 10(-5); Lt, 5 x 10(-5); and ULt, 3 x 10(-5) (cf. typical U.S. EPA risk benchmark of 10(-6)). These values are similar, especially given the tendency of smokers to "compensate" when smoking Lt and ULt cigarettes. ILCR(1)(subSigma-lung) is the brand-average per pack-year subtotal risk for the measured human lung carcinogens. Using CSF set I, the ILCR(1)(subSigma-lung) values for R, Lt, and ULt cigarettes account for

Bioaccumulation models are used to describe chemical uptake and clearances by organisms. Averaged input parameter values are traditionally used and yield point estimates of model outputs. Hence, the uncertainty and variability of model predictions are ignored. Probabilistic modeling approaches, such as Monte Carlo simulation and the Bayesian method, have been recommended by the U.S. Environmental Protection Agency to provide a quantitative description of the degree of uncertainty and/or variability in risk estimates in ecological hazards and human health effects. In this study, a Bayesian analysis was conducted to account for the combined uncertainty and variability of model parameters in a crayfish bioaccumulation model. After a 5-d exposure in the LaBranche Wetlands (LA, USA), crayfish were analyzed for polycyclic aromatic hydrocarbon concentrations and lipid fractions. The posterior distribution of model parameters were derived from the joint posterior parameter distributions using a Markov chain Monte Carlo approach and the experimental data. The results were then used to predict the distribution of chrysene concentration versus time in the crayfish to compare the predicted ranges at the different study sites.

Between 1990 and 1994, samples of three shellfish species (i.e. blue crab, Callinectes sapidus;crayfish, Procambarus acutis; and river shrimp, Macrobrachium ohionii) and 16 fish species and were collected at six sites along the lower Mississippi River by the Louisiana Department of Environmental Quality, Office of Water Resources in coordination with the US Environmental Protection Agency. The fish species included: bigmouth buffalo (Ictiobus cyanellus); blue catfish (Ictalurus furcatus); carp (Cyprinus carpio); channel catfish (Ictalurus punctatus); cobia (Rachycentron canadum); flathead catfish (Pylodictis olivaris); freshwater drum (Aplodinotus grunniens); largemouth bass (Micropterus salmoides); long nose gar (Lepisosteus osseus); red drum (Sciaenops ocellatus); red snapper (Lutjanus campechanus); smallmouth buffalo (Ictiobus bubalus); spotted gar (Lepisosteus oculatus); striped bass (Morone saxatilis); white bass (Morone chrysops); and white crappie (Pomoxis annularis). Organic compound and heavy metal concentrations were measured in 161 composite fish tissue samples where each composite included three to 10 individual fish. Nineteen chemicals, found at measurable levels in sample tissues, were used in calculations of lifetime excess cancer and non-cancer risks due to fish consumption. We calculated: 574 chemical-specific cancer risks; 41 total cancer risks; and 697 margins of exposure based on a consumption rate of one 8-ounce meal per week (0.032 kg/day), a body weight of 70 kg and reported cancer potency factors and reference doses. We identified nine species of concern (blue catfish, carp, channel catfish, cobia, crayfish, flathead catfish, red drum, spotted gar and striped bass) based on total cancer risk greater than 10(-4) or margin of exposure greater than 1, and whether or not samples collected in subsequent years resulted in lower risks. The compounds primarily responsible for the elevated risks were aldrin, dieldrin, alpha-benzene hexachloride, gamma-benzene hexachloride, heptachlor epoxide, arsenic and mercury., (Copyright 2002 Elsevier Science B.V.)

Biologically-based cancer risk assessment relies on mathematical models that represent the toxicokinetics and toxicodynamics of the xenobiotic in the body. Physiologically-based toxicokinetic (PBTK) models are used as a tool for predicting the target tissue dose of a xenobiotic from different routes of exposure, extrapolating from high doses to low doses and extrapolating between species. This paper reviews the role of PBTK models and their limitations in biologically-based risk assessment.

Published data from SENCAR mice were analyzed to elucidate mechanisms underlying the formation of chemically induced skin papillomas. The experimental data followed a two-step protocol using 7,12-dimethylbenz[a]anthracene (DMBA) for initiation and 12-O-tetradecanoylphorbol-13-acetate (TPA) for promotion. The two-mutation model of Ellwein and Cohen was used to simulate the data. In this model, probabilities of stem cell birth and death along with initiation and increased cell division are allowed to vary in time. We incorporated a pulse dose with exponential decay to represent the effect of DMBA on the probability of initiation over time, and a summation of pulse doses to represent the repeated topical applications of TPA and its effect on cell division and cell death. We fitted the model to the papilloma and carcinoma data sequentially, and indirectly determined the dependence of model parameters on the administered dose of DMBA and TPA. The occurrence of chemically induced epidermal hyperplasia is a fundamental assumption in our fits of the papilloma data. We found a delay in the onset of normal and initiated cell death relative to the enhancement of cell division rates. In addition, the model parameters display a nonlinear dependence on the dose of DMBA or TPA administered.

Three methods (multiplicative, additive, and allometric) were developed to extrapolate physiological model parameter distributions across species, specifically from rats to humans. In the multiplicative approach, the rat model parameters are multiplied by the ratio of the mean values between humans and rats. Additive scaling of the distributions is defined by adding the difference between the average human value and the average rat value to each rat value. Finally, allometric scaling relies on established extrapolation relationships using power functions of body weight. A physiologically-based pharmacokinetic model was fitted independently to rat and human benzene disposition data. Human model parameters obtained by extrapolation and by fitting were used to predict the total bone marrow exposure to benzene and the quantity of metabolites produced in bone marrow. We found that extrapolations poorly predict the human data relative to the human model. In addition, the prediction performance depends largely on the quantity of interest. The extrapolated models underpredict bone marrow exposure to benzene relative to the human model. Yet, predictions of the quantity of metabolite produced in bone marrow are closer to the human model predictions. These results indicate that the multiplicative and allometric techniques were able to extrapolate the model parameter distributions, but also that rats do not provide a good kinetic model of benzene disposition in humans.

A three compartment physiologically based toxicokinetic model was fitted to human data on benzene disposition. Two separate groups of model parameter derivations were obtained, depending on which data sets were being fitted. The model was then used to simulate five environmental or occupational exposures. Predicted values of the total bone marrow exposure to benzene and cumulative quantity of metabolites produced by the bone marrow were generated for each scenario. The relation between cumulative quantity of metabolites produced by the bone marrow and continuous benzene exposure was also investigated in detail for simulated inhalation exposure concentrations ranging from 0.0039 ppm to 150 ppm. At the level of environmental exposures, no dose rate effect was found for either model. The occupational exposures led to only slight dose rate effects. A 32 ppm exposure for 15 minutes predicted consistently higher values than a 1 ppm exposure for eight hours for the total exposure of bone marrow to benzene and the cumulative quantity of metabolites produced by the bone marrow. The general relation between the cumulative quantity of metabolites produced by the bone marrow and the inhalation concentration of benzene is not linear. An inflection point exists in some cases leading to a slightly S shaped curve. At environmental levels (0.0039-10 ppm) the curve bends upward, and it saturates at high experimental exposures (greater than 100 ppm).