Leveraging comparative proteomics to improve human disease models

项目摘要 每一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.相反，在同一生物化学复合物中相互作用的蛋白质通常与相似的遗传因子相关联。 性状尽管在高通量生物学的革命，遗传基础的分子机制， 疾病仍然只是部分已知。以前的研究表明，高度保守的（古老的）蛋白质是 在人类细胞类型和组织中是丰富的，并且富含疾病相关性。我的研究旨在 通过确定真核生物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 多种人类疾病的候选基因 这些高度保守的疾病相关蛋白中的很大一部分可追溯到最后一个真核生物。 共同祖先（LECA），一种生活在约20亿年前的祖先生物。我自己的初步数据 这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，这些深刻 保守基因是造成人类主要疾病的一个大而多样的子集， 发育障碍（例如，努南综合征、利综合征、小头畸形、神经管缺陷）， 癌症（例如，白血病，乳腺癌，结肠直肠癌），慢性呼吸道疾病（例如，纤毛运动障碍， 哮喘），神经障碍（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）。 我的实验室已经收集和组装了30种进化上不同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了每个物种中成千上万的蛋白质相互作用。我提议 绘制了一张多蛋白组装的草图，这些组装可以追溯到最后一个真核生物的共同祖先。 这一前所未有的努力代表了超过20，000个质谱实验的综合，因此将 需要大量的编程技能、统计知识和计算资源。使用guilt-by- 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 将同时验证使用深层蛋白质复合物保守作为将基因与 通过功能表征我们之前观察到的与Dnai 2相互作用的两种新型蛋白质来描述疾病。 已知Dnai 2的缺陷会引起原发性纤毛运动障碍，这是一种以Dnai 2缺陷为标志的纤毛病亚型。 运动纤毛;因此，这两种新的蛋白质也可能是纤毛病基因，并可能有助于初级纤毛 运动障碍

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.