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: 7466646
• 负责人: ROBERT E HUGHES
• 金额: $ 46.81万
• 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/7466646
• 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; Neurological Models; 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以下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一个大的蛋白质相互作用 包含人类蛋白质同源基因的网络，已知在模型系统中突变时可延长寿命 衰老的问题。这个使用高通量酵母双杂交方法产生的无尺度网络包括2,172个 在3,219个高度相互连接的独特双元体中与165个已知长寿蛋白相互作用的人类蛋白质 成对的。由于它们与已知的长寿蛋白相互作用，这2172种蛋白质被认为是新的 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， 它们高度富含在人类衰老过程中表达变化的基因(根据微阵列 从年轻和年老的人类肌肉组织产生的数据)。“长寿互动组”将使用 生物信息学方法和从网络中获得的新的长寿基因将用线虫进行验证 寿命分析。 发现与衰老有关的新蛋白质的补充方法将是使用基于质谱学的方法 蛋白质组学方法，发现随着时间的推移，在衰老和疾病中变得不能溶解的蛋白质。我们 将测定衰老模式生物中蛋白质组的十二烷基硫酸钠不溶部分的含量 从小鼠神经系统疾病模型中提取小鼠组织和大脑。发现的蛋白质可以分割成 衰老和/或疾病期间的不溶状态将进一步研究在这些状态中可能的功能作用 使用无脊椎动物和老鼠模型的过程。 具体目标1.利用衰老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的蛋白质 与衰老和长寿有关。候选蛋白质将使用信息学方法进行优先排序，与 年龄特定的微阵列数据集，并在衰老的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其在衰老中的作用。 具体目的2.开发蛋白质组比例图，研究蛋白质溶解度随年龄的变化。 我们将使用基于MS的方法来确定哪些蛋白质在年龄相关的方式下变得不能溶解。 这将在老化的酵母、线虫以及衰老和患病小鼠的组织中完成(例如，大脑和 肌肉)。蛋白质溶解度变化的动力学将在长寿突变酵母和线虫中进行测试 迟发性神经变性的遗传模型(例如：AD、HD和PD小鼠模型)也是如此。蛋白质 表现出易受年龄依赖和疾病依赖的不解性将在功能上 以适当的无脊椎动物、基于细胞的和小鼠衰老和疾病模型为特点。