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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要每种人类遗传疾病的根源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基因有关。特征。尽管高通量生物学发生了革命，但遗传背后的分子机制人们对疾病的了解还只是部分。先前的研究表明，高度保守（古老）的蛋白质是丰富的人类细胞类型和组织，并且丰富的疾病关联。我的研究目的是通过确定真核生命树中最保守的蛋白质相互作用来利用这些趋势，基于对现有大规模蛋白质组学数据的分析，并利用这些信息提出新的建议多种人类疾病的候选基因。这些深度保守的疾病相关蛋白的很大一部分可以追溯到最后的真核生物共同祖先（LECA），一种生活在约 20 亿年前的祖先生物。我自己的初步数据表明人类基因组中约 9,700 个基因可以追溯到 LECA。重要的是，这些深深保守基因导致大量不同的主要人类疾病，涵盖发育障碍（例如努南综合征、利氏综合征、小头畸形、神经管缺陷），癌症（例如白血病、乳腺癌、结直肠癌）、慢性呼吸道疾病（例如纤毛运动障碍、哮喘）、神经系统疾病（例如腓骨肌萎缩症、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）。我的实验室收集并组装了约 30 种进化多样的真核生物的蛋白质相互作用数据有机体。这些数据直接测量每个物种中数以万计的蛋白质相互作用。我建议绘制可追溯到最后一个真核生物共同祖先的多蛋白组装图草图。这项史无前例的努力综合了超过 20,000 项质谱实验，因此将需要大量的编程技能、统计知识和计算资源。使用内疚感—— 关联，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。我同时将验证深层蛋白质复合物保守性作为将基因与通过对我们之前观察到的与 Dnai2 相互作用的两种新蛋白质进行功能表征来治疗疾病。已知 Dnai2 缺陷会导致原发性纤毛运动障碍，这是纤毛病的一种亚型，其特征是 Dnai2 缺陷运动纤毛；因此，这两种新蛋白也可能是纤毛病基因，并可能导致原发性纤毛病运动障碍。