Investigating the functional impact of genetic variants in the human proteome

研究人类蛋白质组中遗传变异的功能影响

• 批准号: 10715585
• 负责人: Brian Chih-Seng Searle
• 金额: $ 39.38万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10715585
• 关键词: Affect; Amyloidosis; Base Pairing; Benign; Biology; Clinical; Coupled; DNA Sequence Alteration; Data; Disease; Genetic Diseases; Genetic Variation; Genetic study; Genome; Genomics; Germ-Line Mutation; Health; Human; Individual; Libraries; Link; Malignant Neoplasms; Mass Spectrum Analysis; Measures; Mutagens; Mutation; Outcome; Pathogenicity; Patients; Peptides; Phenotype; Prealbumin; Proteins; Proteome; Proteomics; Published Comment; Sensitivity and Specificity; Time; Validation; Variant; analytical tool; bioinformatics tool; clinically actionable; driver mutation; genetic variant; improved; interest; molecular phenotype; protein misfolding; tool; tumor; variant of unknown significance

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.

项目摘要 个体之间的基因组差异估计约占碱基对的0.5％，但这很小 多样性可以极大地影响基因组和下游蛋白质组级数据的解释。尽管 个体之间的大多数种系变异是中性的，有成千上万的已知遗传疾病 人类与特定种系变体相关。同样，体细胞变异是癌症的核心标志， 突变可能会导致肿瘤中明显的表型后果作为所谓的“驱动突变”，但 肿瘤中的大多数遗传变异几乎没有可观察到的作用。我们的实验室有兴趣了解 种系和躯体变体在蛋白质组水平上的影响，并研究变异丰度的变化 时间。我们认为，更了解哪些遗传变异具有分子表型将导致 对疾病的临床可行特征的了解得以提高。蛋白质组学的最新进展已有 产生了独立的数据采集质谱法（DIA-MS）作为定量的强大新工具 研究。我们已经证明，可以准确，始终如一地测量遗传 带有DIA-MS的含有变异的肽（GVP）。在此建议中，我们将开发新的生物信息学工具 使用DIA-MS解释GVP，以提高我们的敏感性和特异性。通过使用临床 合作者研究淀粉样变性中的遗传变异，我们希望改善键入和结果 携带特定遗传变异的患者。此外，专注于甲状腺素蛋白，最常见的 淀粉样蛋白，我们将使用诱变的文库研究高通量的变体。有了这些数据， 我们将对未知意义的大量观察到的经甲状腺激素变体进行分类为 良性或可能的致病性。该蛋白是演示的理想候选者，因为几种经甲状腺素蛋白 变体具有已知的临床结果，使我们能够验证我们的方法。总的来说，这些 生物信息学和分析工具，再加上已知和未知生物学的疾病验证 受遗传变异的驱动，将有助于我们建立强大的工具来研究新蛋白质组学的遗传疾病 观点。