A computational model of the hypothalamic - pituitary - gonadal axis in female fathead minnows (Pimephales promelas) exposed to 17α-ethynylestradiol and 17β-trenbolone

Li, Zhenhong; Kroll, Kevin J.; Jensen, Kathleen; Villeneuve, Daniel L.; Ankley, Gerald T.; Brian, Jayne V.; Sepúlveda, Marı́a S.; Orlando, Edward F.; Lazorchak, James M.; Kostich, Mitchell S.; Armstrong, Brandon M.; Denslow, Nancy D.; Watanabe, Karen H.

doi:10.1186/1752-0509-5-63

Cited by 35 publications

(35 citation statements)

References 70 publications

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“…Thus, having a time course of plasma VTG concentrations would likely improve the model predictions. Measured plasma VTG concentrations would be optimal, but connecting the oocyte growth dynamics model to other computational models predicting plasma VTG concentrations as a function a time would also work (Murphy et al 2009;Li et al 2011).…”

Section: Discussionmentioning

confidence: 98%

A computational model for asynchronous oocyte growth dynamics in a batch-spawning fish

Villeneuve

Jensen

et al. 2011

Can. J. Fish. Aquat. Sci.

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A computational model of oocyte growth dynamics (i.e., oocyte recruitment, growth, and spawning) in a batchspawning fish, fathead minnow (FHM, Pimephales promelas), has been developed. The model provides a quantitative link between oocyte growth dynamics and biochemical processes in FHMs through the absorption of vitellogenin (a lipoprotein precursor of egg yolk proteins) into oocytes, which contributes significantly to oocyte growth in fish. The model simulates the number and volume of oocytes in different batches within a FHM ovary. Model-predicted clutch sizes and spawning intervals matched the experimental data well for both unexposed FHMs and FHMs exposed to 17b-trenbolone (a relatively stable metabolite of trenbolone acetate, a synthetic androgen used as a growth promoter in livestock). Overall, the model presents a novel approach to simulating oocyte growth dynamics in a batch-spawning fish and meets an urgent need in ecotoxicological studies to link the effects of endocrine disrupting chemicals at a biochemical level to adverse effects upon reproduction.Résumé : Nous mettons au point un modèle informatique de la dynamique de la croissance des oocytes (c'est-à-dire, du recrutement des oocytes, de leur croissance et de leur ponte) chez un poisson qui pond par masses, la tête-de-boule (FHM, Pimephales promelas). Le modèle fournit un lien quantitatif entre la dynamique de la croissance des oocytes et les processus biochimiques chez la FHM par l'absorption dans les oocytes de la vitellogénine (un précurseur lipoprotéinique des protéines du vitellus), qui contribue significativement à la croissance des oocytes chez les poissons. Le modèle simule le nombre et le volume des oocytes dans les différentes masses dans un ovaire de FHM. Les tailles des pontes et les intervalles entre les pontes prédits par le modèle correspondent bien aux données expérimentales, tant chez les FHM non exposés que chez les FHM exposés au 17b-trenbolone (un métabolite relativement stable de l'acétate de trenbolone, un androgène de synthèse utilisé pour promouvoir la croissance chez le bétail). Globalement, le modèle représente une approche nouvelle pour simuler la dynamique de la croissance des oocytes chez un poisson qui pond ses oeufs par masses et répond à un besoin pressant dans les études écotoxicologiques de pouvoir relier au niveau biochimique les effets des produits chimiques disruptifs du système endocrinien aux conséquences négatives sur la reproduction.[Traduit par la Rédaction]

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Section: Discussionmentioning

confidence: 98%

A computational model for asynchronous oocyte growth dynamics in a batch-spawning fish

Villeneuve

Jensen

et al. 2011

Can. J. Fish. Aquat. Sci.

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“…PBTK model development in ecotoxicology has thus far largely been focused on fish, e.g. (Nichols et al, 1990;Nichols and Fitzsimmons, 2004;Murphy et al, 2005Murphy et al, , 2009Watanabe et al, 2009;Li et al, 2011a;Stadnicka et al, 2012), with incorporation of biotransformation being an area of active research (Nichols et al, 2006(Nichols et al, , 2007(Nichols et al, , 2013Fay et al, 2014). The same concepts can certainly be applied to other organisms, such as invertebrates, e.g.…”

Section: Integration With Computational Modelsmentioning

confidence: 97%

“…Toxicokinetic-toxicodynamic (TKTD) models provide explicit quantitative links between exposure and effects (Watanabe et al, 2009;Li et al, 2011a;Ashauer and Brown, 2013). Moreover, TKTD models can be applied for modeling sublethal effects and have also proven particularly useful for estimating consequences of fluctuating exposures, as they allow taking into account organism recovery rates Galic et al, 2014).…”

Section: Integration With Computational Modelsmentioning

confidence: 99%

Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology

et al. 2015

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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.

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“…from the gene regulatory to the biochemical level. Towards this end, there has been great interest to create mathematical (or computational) models of the reproductive endocrine axis and steroidogenesis (Murphy et al, 2005;Breen et al, 2007;Villeneuve et al, 2007a;Ankley et al, 2009;Murphy et al, 2009;Li et al, 2011). The main motivator for such efforts is to predict the presence of diagnostic 'biomarkers' representative of a stressor scenario and/or mode of action, and the extrapolation of these effects to metabolic disruptions.…”

Section: Introductionmentioning

confidence: 99%

Constraints-based stoichiometric analysis of hypoxic stress on steroidogenesis in fathead minnows, Pimephales promelas

Hala

Petersen

Martinović

et al. 2012

Journal of Experimental Biology

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SUMMARYIn this study, an in silico genome-scale metabolic model of steroidogenesis was used to investigate the effects of hypoxic stress on steroid hormone productions in fish. Adult female fathead minnows (Pimephales promelas) were exposed to hypoxia for 7days with fish sub-sampled on days 1, 3 and 7 of exposure. At each time point, selected steroid enzyme gene expressions and steroid hormone productions were quantified in ovaries. Fold changes in steroid enzyme gene expressions were used to qualitatively scale transcript enzyme reaction constraints (akin to the range of an enzymeʼs catalytic activity) in the in silico model. Subsequently, in silico predicted steroid hormone productions were qualitatively compared with experimental results. Key findings were as follows. (1) In silico gene deletion analysis identified highly conserved ʻessentialʼ genes required for steroid hormone productions. These agreed well (75%) with literature-published genes downregulated in vertebrates (fish and mammal) exposed to hypoxia. (2) Quantification of steroid hormones produced ex vivo from ovaries showed a significant reduction for 17-estradiol and 17,20-dihydroxypregnenone production after 24h (day 1) of exposure. This lowered 17-estradiol production was concomitant with downregulation of cyp19a1a gene expression in ovaries. In silico predictions showed agreement with experimentation by predicting effects on estrogen (17-estradiol and estrone) production. (3) Stochastic sampling of in silico reactions indicated that cholesterol uptake and catalysis to pregnenolone along with estrogen methyltransferase and glucuronidation reactions were also impacted by hypoxia. Taken together, this in silico analysis introduces a powerful model for pathway analysis that can lend insights on the effects of various stressor scenarios on metabolic functions. Supplementary material available online at

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A computational model of the hypothalamic - pituitary - gonadal axis in female fathead minnows (Pimephales promelas) exposed to 17α-ethynylestradiol and 17β-trenbolone

Cited by 35 publications

References 70 publications

A computational model for asynchronous oocyte growth dynamics in a batch-spawning fish

A computational model for asynchronous oocyte growth dynamics in a batch-spawning fish

Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology

Constraints-based stoichiometric analysis of hypoxic stress on steroidogenesis in fathead minnows, Pimephales promelas

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