Investigating the functional impact of genetic variants in the human proteome

项目总结 个体之间的基因组差异估计约为碱基对的0.5%，但这一微小的差异 多样性可以极大地影响对基因组和下游蛋白质组水平数据的解释。而当 大多数个体之间的生殖系变异是中性的，有数千种已知的遗传疾病 人类与特定的生殖系变异相关联。类似地，体细胞变异是癌症的中心标志， 突变可能在肿瘤中引起显著的表型后果，即所谓的“驱动突变”，但 肿瘤中的大多数遗传变异几乎或根本没有可观察到的影响。我们的实验室有兴趣了解 种系和体细胞变异在蛋白质组水平上的影响及变异丰度变化的研究 时间到了。我们相信，更好地了解哪些遗传变异具有分子表型将导致 提高了对疾病的临床可操作特征的理解。蛋白质组学的最新进展 产生了一种数据独立获取质谱学(DIA-MS)作为一种强有力的定量分析新工具 学习。我们已经证明，准确和一致地测量基因是可能的 含变异肽(GVP)的DIA-MS分析。在这项提案中，我们将开发新的生物信息学工具来 使用DIA-MS解释GVP既可以提高我们的敏感性和特异性。通过与临床医生合作 合作者研究淀粉样变性的基因变异，我们希望改善分型和预后 携带特定基因变异的患者。此外，关注甲状旁腺素，最常见的 对于淀粉样蛋白，我们将使用诱变文库来研究高通量的变异体。有了这些数据， 我们将观察到的大量未知意义的促甲状腺激素变异体归类为可能的 良性的或可能致病的。这种蛋白质是一个理想的演示候选蛋白，因为有几个转甲状腺激素 变种已经知道了临床结果，使我们能够验证我们的方法。这些加在一起， 生物信息学和分析工具，加上对已知和未知生物学疾病的验证 在基因变异的推动下，将帮助我们建立从新的蛋白质组研究遗传病的强大工具 视点。

PROJECT SUMMARY The genomic difference between individuals is estimated to be approximately 0.5% of base pairs, but this small diversity can drastically affect the interpretation of both genome- and downstream proteome-level data. While most germline variants between individuals are neutral, there are thousands of known genetic disorders in humans linked to specific germline variants. Similarly, somatic variations are a central hallmark of cancer, where mutations may cause marked phenotypic consequences in tumors as so-called “driver mutations”, but most genetic variants in tumors have little or no observable effect. Our lab is interested in understanding the effects of germline and somatic variants at the proteome level and studying changes in variant abundance over time. We believe a greater understanding of which genetic variants have molecular phenotypes will lead to improved understanding of clinically actionable features of diseases. Recent advances in proteomics have yielded a data independent acquisition mass spectrometry (DIA-MS) as a powerful new tool for quantitative studies. We have demonstrated that it is possible to accurately and consistently measure genetic variant-containing peptides (GVPs) with DIA-MS. In this proposal we will develop new bioinformatics tools to interpret GVPs using DIA-MS both to improve our sensitivity and specificity. By working with clinical collaborators to study genetic variants in amyloidosis, we hope to improve both typing and outcomes in patients carrying specific genetic variants. Furthermore, focusing on Transthyretin, the most common amyloidogenic protein, we will use mutagenized libraries to study variants in high-throughput. With this data, we will class the massive number of observed Transthyretin variants of unknown significance as either likely benign or likely pathogenic. This protein is an ideal candidate for demonstration because several Transthyretin variants have known clinical outcomes, allowing us to validate our approach. Taken together, these bioinformatic and analytical tools, coupled with validation in a disease with both known and unknown biology driven by genetic variants, will help us build powerful tools to study genetic diseases from a new proteomic viewpoint.