Leveraging comparative proteomics to improve human disease models

利用比较蛋白质组学改善人类疾病模型

• 批准号: 10313929
• 负责人: Rachael M Cox
• 金额: $ 3.69万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10313929
• 关键词: Animals; Asthma; Automobile Driving; Back; Biochemical; Biological Process; Biology; Candidate Disease Gene; Charcot-Marie-Tooth Disease; Chronic; Cilia; Ciliary Motility Disorders; Colorectal Cancer; Complex; Confocal Microscopy; Data; Data Set; Defect; Diatoms; Disease; Disease model; Dynein ATPase; Dystonia; Embryo; Encephalopathies; Eukaryota; Fractionation; Functional disorder; Gene Family; Genes; Genetic; Genetic Diseases; Golgi Apparatus; Guilt; Hand; Human; Human Genetics; Human Genome; Kidney Diseases; Leigh Disease; Life; Link; Literature; Lung diseases; Malignant Neoplasms; Maps; Mass Spectrum Analysis; Measures; Microcephaly; Modeling; Molecular; Motor; Nerve Degeneration; Neural Tube Defects; Noonan Syndrome; Organelles; Organism; Paper; Phenotype; Plant Proteins; Plant Roots; Plants; Polydactyly; Primary Ciliary Dyskinesias; Proteins; Proteome; Proteomics; RNA-Protein Interaction; Rana; Reporting; Research; Retinal Degeneration; Role; Sampling; Schizophrenia; Spastic Paraplegia; Structure; Taxonomy; Techniques; Testing; Tissues; Trees; Validation; Xenopus; arm; autism spectrum disorder; base; bioinformatics pipeline; causal variant; cell motility; cell type; ciliopathy; cilium motility; comparative; computing resources; crosslink; developmental disease; experimental study; gene conservation; human disease; improved; knock-down; leukemia; malignant breast neoplasm; motor disorder; multicatalytic endopeptidase complex; nervous system disorder; novel; promoter; protein complex; protein protein interaction; skills; trait; trend

PROJECT SUMMARY At the root of every human genetic disease lies molecular dysfunction of a biological process or protein complex. Conversely, proteins interacting in the same biochemical complex are often linked to similar genetic traits. Despite the revolution in high throughput biology, the molecular mechanisms underlying genetic diseases remain only partly known. Previous studies have shown that highly conserved (ancient) proteins are abundant across human cell types and tissues and are enriched for disease associations. My research aims to exploit these trends by determining the most conserved protein interactions across the eukaryotic tree of life, based on an analysis of available large scale proteomics data, and using this information to suggest new candidate genes for diverse human diseases. A large portion of these deeply conserved disease-associated proteins are traceable to the last eukaryotic common ancestor (LECA), an ancestral organism that lived ~2 billion years ago. My own preliminary data suggests that ~9,700 genes in the human genome can be dated back to LECA. Importantly, these deeply conserved genes are responsible for a large and diverse subset of major human diseases, spanning developmental disorders (e.g., Noonan syndrome, Leigh syndrome, microcephaly, neural tube defects), cancers (e.g., leukemia, breast cancer, colorectal cancer), chronic respiratory diseases (e.g., ciliary dyskinesia, asthma), neurological disorders (e.g., Charcot-Marie-Tooth disease, encephalopathy, schizophrenia, autism) and motor problems (e.g., dystonia, spastic paraplegia). My lab has collected and assembled protein interaction data for ~30 evolutionarily diverse eukaryotic organisms. These data directly measure tens of thousands of protein interactions in each species. I propose developing a draft map of the multiprotein assemblies that date back to the last eukaryotic common ancestor. This unprecedented effort represents a synthesis of >20,000 mass spectrometry experiments, and thus will require significant programming skill, statistical know-how, and computational resources. Using guilt-by- association, I will then associate new candidate genes with diseases based on these conserved interactions. I will concurrently verify the use of deep protein complex conservation as a way to associate genes with diseases by functionally characterizing two novel proteins we previously observed to interact with Dnai2. Defects in Dnai2 are known to cause primary ciliary dyskinesia, a subtype of ciliopathy marked by defects in motile cilia; thus, these two novel proteins are also likely ciliopathy genes and may contribute to primary ciliary dyskinesia.

项目摘要 每个人类遗传疾病的根源都在于生物过程或蛋白质的分子功能障碍 复杂的。相反，在同一生化复合物中相互作用的蛋白质通常与相似的遗传有关 特质。尽管高通量生物学发生了革命，但遗传的分子机制 疾病仅部分知道。先前的研究表明，高度保守的（古代）蛋白是 在人类细胞类型和组织之间丰富，并富含疾病关联。我的研究旨在 通过确定整个真核生命树中最保守的蛋白质相互作用来利用这些趋势， 基于对可用大规模蛋白质组学数据的分析，并使用此信息建议新 多种人类疾病的候选基因。 这些深层保守疾病相关的蛋白质中的很大一部分可追溯到最后一个真核生物 共同祖先（LECA），一种约20亿年前的祖先生物体。我自己的初步数据 表明人类基因组中约有9,700个基因可以追溯到LECA。重要的是，这些 保守的基因负责大量的主要人类疾病子集 发育障碍（例如Noonan综合征，Leigh综合征，小头畸形，神经管缺陷）， 癌症（例如白血病，乳腺癌，大肠癌），慢性呼吸道疾病（例如纤毛运动障碍， 哮喘），神经系统疾病（例如charcot-marie-tooth病，脑病，精神分裂症，自闭症） 和运动问题（例如肌张力障碍，痉挛性截瘫）。 我的实验室已收集并组装了蛋白质相互作用的数据，以进化于30种多样性的真核生物 有机体。这些数据直接测量每个物种中成千上万的蛋白质相互作用。我建议 开发可追溯到最后一个真核共同祖先的多蛋白组件的草稿图。 这种前所未有的努力代表了> 20,000个质谱实验的合成，因此将 需要重要的编程技能，统计专有技术和计算资源。用内gui 然后，我将根据这些保守的相互作用将新的候选基因与疾病联系起来。我 将同时验证使用深蛋白复合物保护作为将基因与 通过功能表征两种新型蛋白质，我们先前观察到与DNAI2相互作用的疾病。 已知DNAI2缺陷会引起原发性睫状运动障碍，这是一种由缺陷在 纤毛纤毛；因此，这两种新型蛋白质也可能是纤毛病基因，可能有助于原发性睫状 运动障碍。