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

项目总结 动态平衡能力的丧失是从酵母到人类的老化有机体的一个基本和决定性的特性。 全球蛋白激酶-底物网络构成了动态平衡信号网络的重要骨干。 (HSN)允许细胞对动态环境和细胞需求做出适当反应。我们建议 这种衰老可以被模拟为HSN的一系列变化，这些变化直接影响到核心细胞功能。 这项建议的首要目标是在酵母中模拟HSN，并通过 HSN的降解会导致功能衰退，并随着年龄的增长而增加死亡风险。至 为了实现这一目标，我们将使用全球和定向质谱学方法的组合来绘制地图 在高分辨率下，构成HSN的激酶和底物。然后我们会将老化的酵母暴露在 并使用磷酸蛋白质组学测量信号的变化。最后，我们将结合这些 用单细胞微流体学测量进行种群水平分析，以确定外显率和 网络关键成分(激酶和底物)和细胞记者的时间动力学 在整个衰老过程中最容易退化的功能。这种方法将使我们能够确定 第一次，单个细胞经历不同衰老轨迹的程度。我们将使用这个 用于建立随年龄增长的网络退化模型并预测网络关键组件的信息 容易失败，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 通过在酵母菌株中设计它们并评估这些菌株是否真的 随着年龄的增长保持重要的网络结构，保持他们年轻时的细胞功能，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.