You are here:
Predicting fecundity of fathead minnows (Pimephales promelas) exposed toeEndocrine-disrupting chemicals using a MATLAB®-based model of oocyte growth dynamics
Citation:
Watanabe, K., M. Mayo, K. Jensen, Dan Villeneuve, G. Ankley, AND E. Perkins. Predicting fecundity of fathead minnows (Pimephales promelas) exposed toeEndocrine-disrupting chemicals using a MATLAB®-based model of oocyte growth dynamics. PLOS ONE . Public Library of Science, San Francisco, CA, 11(1):26 pgs., (2016).
Impact/Purpose:
One of the key goals of toxicity testing in the 21st century is to be able to make scientifically credible, quantitative, predictions of apical toxicological outcomes based on measures of pathway perturbation that can be made efficiently and cost-effectively through high throughput, predominantly in vitro, approaches. The adverse outcome pathway discovery and development project within ORD’s CSS research program has been developing case studies related to the development of quantitative AOP (qAOP) constructs that can facilitate such predictions. One of those case studies, focuses on translating measures of endocrine activity into predicted reproductive impacts in fish. The present paper details further development and testing of an oocyte growth dynamics model that can take measured or predicted concentrations of plasma vitellogenin (an egg yolk precursor protein important to fish reproduction) and predict cumulative fecundity and spawning (an endpoint of regulatory significance). Coupled with other computational models of the hypothalamic-pituitary-gonadal axis being developed by ORD scientists the model described in the present paper in an important component of one of the first qAOP construct developed. As a result, this work contributes substantively to demonstration and testing of the type of transformational approach to toxicity testing that was advocated by the US National Research Council and has been embraced by ORD’s CSS program and its program office partners.
Description:
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, 17â-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 (eggsŸfemale-1Ÿday-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.
