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

Abstract

Reproduction in fishes and other vertebrates represents the timely coordination of many endocrine factors that culminate in the production of mature, viable gametes. In recent years there has been rapid growth in understanding fish reproductive biology, which has been motivated in part by recognition of the potential effects that climate change, habitat destruction and contaminant exposure can have on natural and cultured fish populations. New approaches to understanding the impacts of these stressors are being developed that require a systems biology approach with more biologically accurate and detailed mathematical models. We have developed a multi-scale mathematical model of the female rainbow trout hypothalamus-pituitary-ovary-liver axis to use as a tool to help understand the functioning of the system and for extrapolation of laboratory findings of stressor impacts on specific components of the axis. The model describes the essential endocrine components of the female rainbow trout reproductive axis. The model also describes the stage specific growth of maturing oocytes within the ovary and permits the presence of sub-populations of oocytes at different stages of development. Model formulation and parametrization was largely based on previously published in vivo and in vitro data in rainbow trout and new data on the synthesis of gonadotropins in the pituitary. Model predictions were validated against several previously published data sets for annual changes in gonadotropins and estradiol in rainbow trout. Estimates of select model parameters can be obtained from in vitro assays using either quantitative (direct estimation of rate constants) or qualitative (relative change from control values) approaches. This is an important aspect of mathematical models as in vitro, cell-based assays are expected to provide the bulk of experimental data for future risk assessments and will require quantitative physiological models to extrapolate across biological scales.

Regulation of reproductive processes with dynamic energy budgets

“…Three datasets about female rainbow trout ( O. mykiss ) were analysed to evaluate model performance. The most expansive set, referred to as main dataset, was from Nagler et al () with additional data from Gillies, Krone, Nagler, and Schultz (), and concerns a reproductively synchronized autumn‐spawning population obtained from a commercial supplier (Troutlodge, Inc., Sumner, WA, USA) and maintained in a temperature controlled flow‐through system under a natural lighting regime at the Battelle Marine Science Facility (Sequim, WA, USA). The main dataset included time‐resolved measurements of wet weight of body, ovaries and liver, egg diameter and plasma content of vitellogenin and oestradiol of 58 individuals.…”

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

“…We were fortunate to have access to a rich data set on rainbow trout hormones, egg production, weights, as well as a welldeveloped physiologically-based model (Gillies et al 2016). We were fortunate to have access to a rich data set on rainbow trout hormones, egg production, weights, as well as a welldeveloped physiologically-based model (Gillies et al 2016).…”

“…For larger organisms such as fish, organ-level data are available and in our NIMBioS working group, we are exploring mechanistic approaches to link qAOPs to DEBs via endocrine disruption as the stressor, and rainbow trout (Oncorhynchus mykiss) as the target species. We were fortunate to have access to a rich data set on rainbow trout hormones, egg production, weights, as well as a welldeveloped physiologically-based model (Gillies et al 2016). Endocrine disruption is perhaps the most studied and best understood system in ecotoxicology, and extensive qAOPs have been developed to link MIE to adverse outcomes such as egg production in a few fish species (Conolly et al 2017;Gillies et al 2016).…”

“…We were fortunate to have access to a rich data set on rainbow trout hormones, egg production, weights, as well as a welldeveloped physiologically-based model (Gillies et al 2016). Endocrine disruption is perhaps the most studied and best understood system in ecotoxicology, and extensive qAOPs have been developed to link MIE to adverse outcomes such as egg production in a few fish species (Conolly et al 2017;Gillies et al 2016). Therefore, we focused on endocrine disruption for these case studies, but ideally, the linkage between qAOPs that converge on maintenance and growth adverse outcomes, once developed, should also be explored as they connect to pMoA in DEB because then the full implication of energetic trade-offs could be realized.…”

“…Another approach is to add an egg module to the standard DEB model, allowing for hormone dynamics to control conversion of material in the DEB "reproduction buffer" into eggs (Figure 3). In this approach, well-developed physiologically-based models (e.g., Conolly et al 2017;Gillies et al 2016;Li et al 2011;Murphy et al 2009; Figure 2. Key event network (mode of action) translates to some measure of damage that would have to be related to key rates or allocations (indicated by red arrows) in dynamic energy budget models.…”