Protein Interactions and Protein Conformation in Aging and Disease (6 of 11)

衰老和疾病中的蛋白质相互作用和蛋白质构象（11 中的 6）

• 批准号: 7872975
• 负责人: ROBERT E HUGHES
• 金额: $ 47.1万
• 依托单位: BUCK INSTITUTE FOR RESEARCH ON AGING
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-28 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7872975
• 关键词: Age; Aging; Animal Model; Bioinformatics; Biological Assay; Biological Models; Brain; Caenorhabditis elegans; Cells; Data; Data Set; Disease; Genes; Genetic Models; Human; Informatics; Invertebrates; Kinetics; Life; Longevity; Maps; Methods; Mining; Molecular Conformation; Mus; Muscle; Mutate; Nematoda; Nerve Degeneration; Orthologous Gene; Process; Protein Conformation; Proteins; Proteome; Proteomics; Role; Solubility; Testing; Time; Tissues; Two-Hybrid System Techniques; Yeasts; age related; base; brain tissue; longevity gene; mouse model; mutant; nervous system disorder; novel; protein protein interaction

We propose to define and characterize protein interactions and protein conformational states relevant to aging and disease processes. A starting point for the interaction studies will be a large protein interaction network involving human orthologs of proteins known to increase longevity when mutated in models systems of aging. This scale-free network, generated using high throughput yeast two-hybrid methods, includes 2,172 human proteins interacting with 165 known longevity proteins in 3,219 highly interconnected unique binary pairs. Because of their interaction with known longevity proteins, these 2,172 are considered to be novel candidate longevity proteins. Analysis of genes encoding known and candidate longevity proteins show that they are highly enriched for genes whose expression changes during human aging (according to microarray data generated from young and old human muscle tissue). The "longevity interactome" will be mined using bioinformatic methods and novel longevity genes derived from the network will be validated using C. elegans life span assays. A complementary approach to discovering novel proteins involved in aging will be to use mass spectrometrybased proteomic methods to discover proteins that become insoluble over time, in aging and disease. We will determine the content of the SDS insoluble fraction of the proteome in aging model organisms, aging mouse tissues and brains from mouse models of neurological disease. Proteins found to partition into insoluble states during aging and/or disease will be investigated further for possible functional roles in these processes using invertebrate and mouse models. Specific Aim 1. To discover and characterize novel genes relevant to longevity using an aging protein interaction network. We will mine an existing protein interaction network to identify novel protein involved in aging and longevity. Candidate proteins will be prioritized using informatic methods, compared to age-specific microarray datasets and validated experimentally in model organisms of aging. Proteins validated as having roles in aging will be studied further using MS-based proteomic methods. Specific Aim 2. To develop proteome-scale maps of age-dependent changes in protein solubility. We will use MS-based methods to determine which proteins become insoluble in an age-dependent manner. This will be done in aging yeast, nematodes and tissues from aging and diseased mice (e.g. brain and muscle). Kinetics of changes in protein solubility will be tested in long-lived mutant yeast and nematodes and also in genetic models of late-onset neurodegeneration (e:g. AD, HD and PD mouse models). Proteins shown to be susceptible to age-dependent and disease-dependent insolubility will be functionally characterized in appropriate invertebrate, cell-based and mouse models of aging and disease.

我们建议定义和表征与相关的蛋白质相互作用和蛋白质构象状态 衰老和疾病过程。相互作用研究的起点将是大的蛋白质相互作用 涉及已知在模型系统中突变时可延长寿命的人类蛋白质直系同源物的网络 的老化。这个无标度网络是使用高通量酵母双杂交方法生成的，包括 2,172 个 人类蛋白质与 3,219 个高度互连的独特二元中的 165 个已知长寿蛋白质相互作用 对。由于它们与已知的长寿蛋白相互作用，这 2,172 个被认为是新颖的 候选长寿蛋白。对编码已知和候选长寿蛋白的基因的分析表明 它们高度富集了在人类衰老过程中表达发生变化的基因（根据微阵列 从年轻和年老的人体肌肉组织生成的数据）。 “长寿相互作用组”将使用以下方式进行挖掘 生物信息学方法和源自网络的新型长寿基因将使用秀丽隐杆线虫进行验证 寿命测定。 发现与衰老有关的新蛋白质的一种补充方法是使用基于质谱的方法 蛋白质组学方法发现随着时间的推移、衰老和疾病而变得不溶的蛋白质。我们 将确定衰老模型生物体中蛋白质组的 SDS 不溶部分的含量，衰老 来自神经疾病小鼠模型的小鼠组织和大脑。发现蛋白质可分为 将进一步研究衰老和/或疾病期间的不溶状态，以了解这些状态中可能的功能作用 使用无脊椎动物和小鼠模型进行过程。 具体目标 1. 利用衰老过程发现和表征与长寿相关的新基因 蛋白质相互作用网络。我们将挖掘现有的蛋白质相互作用网络来识别新蛋白质 与衰老和长寿有关。与其他蛋白质相比，将使用信息学方法对候选蛋白质进​​行优先排序 特定年龄的微阵列数据集，并在衰老模型生物中进行了实验验证。蛋白质 将使用基于 MS 的蛋白质组学方法进一步研究被证实在衰老中发挥作用的物质。 具体目标 2. 绘制蛋白质溶解度随年龄变化的蛋白质组尺度图。 我们将使用基于 MS 的方法来确定哪些蛋白质以年龄依赖性方式变得不溶。 这将在衰老的酵母、线虫以及衰老和患病小鼠的组织（例如大脑和 肌肉）。将在长寿突变酵母和线虫中测试蛋白质溶解度变化的动力学， 也在迟发性神经变性的遗传模型中（例如 AD、HD 和 PD 小鼠模型）。蛋白质 显示出对年龄依赖性和疾病依赖性不溶性的敏感性将在功能上 在适当的无脊椎动物、细胞和小鼠衰老和疾病模型中进行表征。