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建立雌性虹鳟鱼卵黄蛋白原产生的内分泌调控模型。

Modeling the endocrine control of vitellogenin production in female rainbow trout.

机构信息

Department of Pathology, University of Wisconsin Hospital and Clinics, Madison WI 53792, United States.

出版信息

Math Biosci Eng. 2014 Jun;11(3):621-39. doi: 10.3934/mbe.2014.11.621.

Abstract

The rainbow trout endocrine system is sensitive to changes in annual day length, which is likely the principal environmental cue controlling its reproductive cycle. This study focuses on the endocrine regulation of vitellogenin (Vg) protein synthesis, which is the major egg yolk precursor in this fish species. We present a model of Vg production in female rainbow trout which incorporates a biological pathway beginning with sex steroid estradiol-17β levels in the plasma and concluding with Vg secretion by the liver and sequestration in the oocytes. Numerical simulation results based on this model are compared with experimental data for estrogen receptor mRNA, Vg mRNA, and Vg in the plasma from female rainbow trout over a normal annual reproductive cycle. We also analyze the response of the model to parameter changes. The model is subsequently tested against experimental data from female trout under a compressed photoperiod regime. Comparison of numerical and experimental results suggests the possibility of a time-dependent change in oocyte Vg uptake rate. This model is part of a larger effort that is developing a mathematical description of the endocrine control of reproduction in female rainbow trout. We anticipate that these mathematical and computational models will play an important role in future regulatory toxicity assessments and in the prediction of ecological risk.

摘要

虹鳟鱼内分泌系统对年日照长度的变化敏感,这可能是控制其生殖周期的主要环境线索。本研究集中于卵黄蛋白原(Vg)蛋白合成的内分泌调节,这是该鱼类物种的主要卵黄前体。我们提出了一种虹鳟鱼雌性 Vg 产生的模型,该模型包含了一个从血浆中的性激素雌二醇-17β水平开始,最终以肝脏分泌 Vg 并在卵母细胞中隔离结束的生物学途径。基于该模型的数值模拟结果与雌性虹鳟鱼在正常年生殖周期中雌激素受体 mRNA、Vg mRNA 和血浆中 Vg 的实验数据进行了比较。我们还分析了模型对参数变化的响应。然后,该模型针对处于压缩光周期制度下的雌性鳜鱼的实验数据进行了测试。数值和实验结果的比较表明卵母细胞 Vg 摄取率可能存在时间依赖性变化。该模型是正在开发雌性虹鳟鱼内分泌生殖控制的数学描述的更大努力的一部分。我们预计这些数学和计算模型将在未来的监管毒性评估和生态风险预测中发挥重要作用。

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