Introduction

The expansion of insulin-producing beta cells during pregnancy is critical to maintain glucose homeostasis in the face of increasing insulin resistance. Prolactin receptor (PRLR) signaling is one of the primary mediators of beta cell expansion during pregnancy, and loss of PRLR signaling results in reduced beta cell mass and gestational diabetes. Harnessing the proliferative potential of prolactin signaling to expand beta cell mass outside of the context of pregnancy requires quantitative understanding of the signaling at the molecular level.

Methods

A mechanistic computational model was constructed to describe prolactin-mediated JAK-STAT signaling in pancreatic beta cells. The effect of different regulatory modules was explored through ensemble modeling. A Bayesian approach for likelihood estimation was used to fit the model to experimental data from the literature.

Results

Including receptor upregulation, with either inhibition by SOCS proteins, receptor internalization, or both, allowed the model to match experimental results for INS-1 cells treated with prolactin. The model predicts that faster dimerization and nuclear import rates of STAT5B compared to STAT5A can explain the higher STAT5B nuclear translocation. The model was used to predict the dose response of STAT5B translocation in rat primary beta cells treated with prolactin and reveal possible strategies to modulate STAT5 signaling.

Conclusions

JAK-STAT signaling must be tightly controlled to obtain the biphasic response in STAT5 activation seen experimentally. Receptor up-regulation, combined with SOCS inhibition, receptor internalization, or both is required to match experimental data. Modulating reactions upstream in the signaling can enhance STAT5 activation to increase beta cell survival.

中文翻译：

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通过计算模型预测的催乳素受体对 JAK-STAT 信号传导的动态调节

介绍

面对增加的胰岛素抵抗，怀孕期间产生胰岛素的β细胞的扩增对于维持葡萄糖稳态至关重要。催乳素受体 (PRLR) 信号传导是妊娠期间 β 细胞扩增的主要介质之一，而 PRLR 信号传导的丧失会导致 β 细胞量减少和妊娠糖尿病。利用催乳素信号的增殖潜力来扩大妊娠范围之外的 β 细胞质量需要在分子水平上对信号进行定量了解。

方法

构建了一个机械计算模型来描述胰腺β细胞中催乳素介导的 JAK-STAT 信号传导。通过集成建模探索了不同监管模块的影响。用于似然估计的贝叶斯方法用于将模型拟合到文献中的实验数据。

结果

包括受体上调，通过 SOCS 蛋白抑制，受体内化，或两者兼而有之，使模型能够匹配用催乳素处理的 INS-1 细胞的实验结果。该模型预测，与 STAT5A 相比，STAT5B 更快的二聚化和核输入率可以解释更高的 STAT5B 核易位。该模型用于预测用催乳素处理的大鼠原代 β 细胞中 STAT5B 易位的剂量反应，并揭示调节 STAT5 信号传导的可能策略。

结论

必须严格控制 JAK-STAT 信号传导以获得实验上看到的 STAT5 激活的双相反应。需要受体上调、结合 SOCS 抑制、受体内化或两者来匹配实验数据。调节信号上游的反应可以增强 STAT5 激活以增加 β 细胞存活。