Defining the landscape and mechanisms of redox regulation of metabolism during aging

项目概述：哺乳动物组织参与通过可逆性调节的专门生理学 通过活性氧（ROS）修饰蛋白质半胱氨酸残基。尽管长期以来 在ROS失调和衰老之间，技术限制导致了ROS的持续缺乏。 关于确切的蛋白质半胱氨酸被ROS修饰的信息解释了这种修饰的分子基础。 体内功能障碍。使用我开发的半胱氨酸磷酸化标签（CPT）蛋白质组学技术， 确定了蛋白质半胱氨酸氧化网络的基本重塑与热量限制一起发生 (CR)在衰老。在此基础上，我将确定新发现的蛋白质的氧化还原调节的功能作用 这些网络与CR在衰老中的寿命和健康益处有关。我还扩展了Oximouse 为了使远交（DO）小鼠种群多样化，以重现人类种群的遗传多样性， 对于具有高翻译电位的氧化还原信号靶标。这项工作的初步数据表明， 保守的氧化还原信号靶蛋白，可能对年龄依赖性衰老有重要影响。 产热导致与年龄相关的肥胖。我将研究长寿背后的代谢氧化还原信号节点- 修改干预措施，并阐明这些目标随着年龄的增长而进行氧化还原调节的机制 导致产热活动下降。蛋白质组学数据将为社区提供丰富的资源 探索活性氧和衰老机制研究将验证氧化还原信号节点， 根据国家老龄化研究所的使命， 目的：（1）明确线粒体半胱氨酸氧化机制的基础CR的健康效益。 (2)确定脂肪代谢氧化还原信号节点潜在的长寿修改干预。 (3)年龄相关性肥胖的氧化还原调控机制研究。 前两个目标将在K99阶段完成，最后一个目标将在 R00阶段。这项工作建立在我开发的氧化还原蛋白质组学技术的基础上， 半胱氨酸氧化还原修饰化学计量比在数量级更深的蛋白质组覆盖比以前 方法.从这些大数据中，我将机械地验证具有重要意义的单个氧化还原信号节点， 在新陈代谢和长寿中的作用。Chouchani博士和Gygi博士将指导我，他们是这一领域的专家 ROS生物学，代谢，动物生理学和基于质谱（MS）的蛋白质组学。我会 此外，我还向我的合作者/顾问Mair、Gladyshev、Banks、Gupta和Spiegelman博士学习，他们 在衰老、动物生理学和新陈代谢方面拥有丰富的专业知识。DFCI丰富的科学环境 HMS为我树立独立调查员的形象增添了动力。我独特的技能 允许我开发新的技术，以“大数据”驱动的方式研究衰老的生物学，然后选择 机制验证的目标，为未来的转化治疗发展提供见解。

PROJECT SUMMARY: Mammalian tissues engage in specialized physiology that is regulated through reversible modification of protein cysteine residues by reactive oxygen species (ROS). Despite the longstanding links between ROS dysregulation and aging, technological limitations have resulted in a persistent absence of information on the exact protein cysteines are modified by ROS that explain the molecular basis for this dysfunction in vivo. Using the cysteine-phospho tag (CPT) proteomics technology that I developed, I have determined that a fundamental remodeling of protein cysteine oxidation networks occurs with caloric restriction (CR) in aging. Building on this, I will determine the functional role of redox regulation of newfound protein networks that are linked to the lifespan and healthspan benefits of CR in aging. I have also extended Oximouse to diversity outbred (DO) mouse populations to recapitulate the genetic diversity of human population, in search for redox signaling targets that have high translational potential. Preliminary data from this effort has identified conserved redox signaling targets on proteins that may have critical implications in age-dependent decline of thermogenesis leading to age-related obesity. I will study metabolic redox signaling nodes underlying longevity- modifying interventions and delineate the mechanisms through which these targets are redox-regulated with age that lead to a decline in thermogenic activity. The proteomics data will provide a rich resource for the community to explore ROS and aging. The mechanistic studies will validate redox signaling nodes that can potentially be manipulated to extend lifespan and healthspan, in line with the mission of the National Institute of Aging. Objectives: (1) Defining mitochondrial cysteine oxidation mechanisms underlying the health benefits of CR. (2) Determining adipose metabolic redox signaling nodes underlying longevity-modifying interventions. (3) Investigating the mechanisms of redox control in age-related obesity. The first two objectives will be completed during the K99 phase, and the last objective will be carried out during the R00 phase. This work builds on a redox proteomics technology that I developed, which quantifies absolute cysteine redox modification stoichiometry at orders of magnitude deeper proteome coverage than previous methods. From this big data, I will mechanistically validate individual redox signaling nodes that have important roles in metabolism and longevity. I will be mentored by Drs. Chouchani and Gygi, who are experts in the fields of ROS biology, metabolism, animal physiology, and mass spectrometry (MS)-based proteomics. I will additionally learn from my collaborators/consultants, Drs. Mair, Gladyshev, Banks, Gupta, and Spiegelman, who have extensive expertise in aging, animal physiology, and metabolism. The rich scientific environment at DFCI and HMS adds fuel to my enthusiasm to establish myself as an independent investigator. My unique skillset will allow me to develop novel technologies to study the biology of aging in a “big-data” driven manner, then select targets for mechanistic validation to provide insights for future translational therapeutic development.