Leveraging comparative proteomics to improve human disease models

项目总结 每一种人类遗传病的根源在于生物过程或蛋白质的分子功能障碍 很复杂。相反，在同一生化复合体中相互作用的蛋白质通常与相似的基因相关联。 特征。尽管高通量生物学发生了革命，但遗传基因背后的分子机制 人们对疾病的了解还只有一部分。先前的研究表明，高度保守的(古老的)蛋白质 在人类细胞类型和组织中含量丰富，并富含用于疾病关联的物质。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， 基于对可用的大规模蛋白质组学数据的分析，并利用这些信息提出新的 人类多种疾病的候选基因。 这些高度保守的疾病相关蛋白中的很大一部分可以追溯到最后一个真核生物 共同祖先(LECA)，生活在大约20亿年前的祖先有机体。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，这些深刻地 保守基因是导致人类主要疾病的一个大而多样的子集，跨越 发育障碍(如Noonan综合征、Leigh综合征、小头畸形、神经管缺陷)、 癌症(如白血病、乳腺癌、结直肠癌)、慢性呼吸道疾病(如睫状肌运动障碍、 哮喘)、神经疾病(如夏科-玛丽-牙病、脑病、精神分裂症、自闭症) 以及运动问题(如肌张力障碍、痉挛截瘫)。 我的实验室已经收集和组装了大约30种进化上多样化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数以万计的蛋白质相互作用。我建议 绘制一张可追溯到最后一个真核生物共同祖先的多蛋白组合草图。 这一史无前例的努力代表着20,000个质谱学实验的综合，因此将 需要大量的编程技能、统计知识和计算资源。利用内疚- 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 将同时验证深层蛋白质复合体保守作为一种将基因与 通过对我们之前观察到的与DNAI2相互作用的两种新蛋白质的功能特性进行研究。 DNAI2基因的缺陷已知会导致原发性纤毛运动障碍，这是一种以DNAI2基因缺陷为特征的纤毛病的亚型 运动纤毛；因此，这两个新蛋白也可能是纤毛病变基因，并可能对原发纤毛起作用。 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.