Investigating the functional impact of genetic variants in the human proteome

项目摘要 个体之间的基因组差异估计约为0.5%的碱基对，但这很小。 多样性可以极大地影响基因组和下游蛋白质组水平数据的解释。而 个体之间的大多数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有关。同样，体细胞变异是癌症的一个主要标志， 其中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没有或没有可观察到的影响。我们的实验室有兴趣了解 生殖系和体细胞变异体在蛋白质组水平的影响，并研究变异体丰度的变化， 时间我们相信，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 产生了数据独立采集质谱法（DIA-MS）作为定量分析的强大新工具， 问题研究我们已经证明，可以准确和一致地测量遗传 在本提案中，我们将开发新的生物信息学工具， 使用DIA-MS解释GVP以提高我们的灵敏度和特异性。通过与临床 合作者研究淀粉样变性病的遗传变异，我们希望改善淀粉样变性病的分型和预后。 携带特定遗传变异的患者。此外，关注甲状腺素运载蛋白，最常见的 对于淀粉样蛋白，我们将使用诱变文库以高通量研究变体。有了这些数据， 我们将大量观察到的意义不明的甲状腺素运载蛋白变体归类为 良性或可能致病。这种蛋白质是一个理想的候选人，因为几个转甲状腺素蛋白 变体具有已知的临床结果，使我们能够验证我们的方法。综上所述各项 生物信息学和分析工具，加上已知和未知生物学疾病的验证 由遗传变异驱动，将帮助我们建立强大的工具，从一个新的蛋白质组学研究遗传疾病， 观点

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.