Emergent properties of signaling network degradation that mediate homeostatic failure during aging

项目摘要 丧失稳态能力是从酵母到人类的衰老生物体的基本和定义性质。 全球蛋白激酶底物网络形成了一个重要的骨干稳态信号网络 (HSN)它允许细胞对动态环境和细胞需要做出适当的反应。我们提出 衰老可以被建模为HSN的一系列变化，这些变化直接影响核心细胞功能。 该提案的总体目标是在酵母中模拟HSN，并通过以下方式了解其机制： HSN的降解导致功能下降和随年龄增长死亡的风险增加。到 为了实现这一目标，我们将使用全局和靶向质谱法相结合的方法来映射 在高分辨率下，包括HSN的激酶和底物。然后我们将老化酵母暴露于 干扰，并使用磷酸蛋白质组学测量信号传导的变化。最后，我们将联合收割机 用单细胞微流体测量进行群体水平分析，以确定细胞的迁移率， 网络的关键组成部分（激酶和底物）和细胞的报告分子的时间动态 在整个老化过程中最容易退化的功能。这种方法将使我们能够确定，因为 第一次，个体细胞经历不同老化轨迹的程度。我们将使用这个 开发网络老化模型和预测网络关键组件的信息 容易出现故障，这可能会得到加强，以建立一个更强大的网络。我们将测试这些 通过在酵母菌株中改造它们，并评估这些菌株是否确实 随着年龄的增长保持重要的网络结构，保持年轻状态的细胞功能，也许， 活得更久。

PROJECT SUMMARY Loss of homeostatic capacity is a fundamental and defining property of aged organisms from yeast to humans. The global protein kinase-substrate network forms an essential backbone of the homeostatic signaling network (HSN) that allows cells to respond appropriately to a dynamic environment and cellular needs. We propose that aging can be modeled as a series of changes to the HSN that directly impinge on core cellular functions. The overarching goal of this proposal is to model the HSN in yeast, and to understand the mechanisms by which degradation of the HSN results in functional declines and increasing risk of mortality with age. To accomplish this goal we will use a combination of global and targeted mass spectrometry approaches to map at high resolution the kinases and substrates that comprise the HSN. We will then expose aging yeast to perturbations, and measure changes in signaling using phosphoproteomics. Finally, we will combine these population level analyses with single-cell microfluidics measurements in order to define the penetrance and temporal dynamics of key components of the network (kinases and substrates) and reporters of cellular functions that are most prone to degradation throughout aging. This approach will allow us to determine, for the first time, the extent to which individual cells experience distinct aging trajectories. We will use this information to develop models for network degradation with age and to predict key components of the network prone to failure, which could potentially be strengthened to build a more robust network. We will test these predicted improvements by engineering them within yeast strains and assessing whether the strains indeed maintain important network structures with age, keep cellular functions of their youthful state, and, perhaps, live longer.