Bayesian Modeling of Mass-Spec Proteomics Data to Advance Studies of the Genetic Regulation of Proteins

质谱蛋白质组数据的贝叶斯建模推进蛋白质遗传调控的研究

• 批准号: 10391171
• 负责人: Gregory R Keele
• 金额: $ 0.25万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10391171
• 关键词: Address; Aging; Animal Model; Automobile Driving; Bayesian Method; Bayesian Modeling; Biochemistry; Biological; Biological Process; Biology; Cells; Code; Communities; Complex; Computer software; Data; Data Set; Disease; Disease Progression; Environmental Exposure; Error Sources; Experimental Designs; Foundations; Genetic; Genetic Research; Genetic study; Goals; Health; Human; Individual; Interest Group; Intuition; Islet Cell; Islets of Langerhans; Knowledge; Label; Maps; Mass Spectrum Analysis; Measurement; Measures; Metabolic Pathway; Methods; Modeling; Modernization; Modification; Molecular Analysis; Motivation; Mus; Outcome; Pattern; Peptide Fragments; Peptides; Phenotype; Play; Population; Predisposition; Prevention; Procedures; Process; Protein Dynamics; Protein Isoforms; Proteins; Proteome; Proteomics; Proxy; Regulation; Role; Sampling; Scientist; Shotguns; Signal Transduction; Source; Statistical Algorithm; Statistical Data Interpretation; Statistical Methods; Statistical Models; Structure; System; Systematic Bias; Technology; Transcript; Transport Process; Uncertainty; Variant; Work; analytical tool; base; biological systems; data resource; design; disease phenotype; experimental study; extracellular; flexibility; genetic analysis; genetic makeup; glucose metabolism; heart metabolism; human disease; improved; insight; kidney metabolism; mouse model; novel; programs; protein complex; protein protein interaction; success; tool; transcriptome sequencing; transcriptomics

PROJECT SUMMARY / ABSTRACT Proteins play vital functional roles in essentially all biological systems, factoring into the complex expression of phenotypes and diseases observed in human populations. The quantitative study of all proteins, i.e. proteomics, has the potential to directly assess how protein dynamics vary across individuals, treatments, and exposures, ideally in an unbiased fashion not requiring pre-formed and targeted candidates. Historically a proteomics approach has been constrained due to limitations of the original mass spectrometry (MS) technology available. Transcriptomics has often been used in place of proteomics, though notably, the regulation of proteins can be decoupled from their transcripts, rendering them imperfect proxies. The feasibility of accurate and reliable proteomics has been aided by rapid advancement in MS technology. Currently the statistical tools for proteomics lag behind and present an impediment to the full use of these rich data resources. MS proteomics data possess a number of unique and challenging features that need to be addressed in their statistical analysis. Proteins are not directly measured, but instead pre-fragmented into smaller peptides. A protein's abundance must then be reconstructed from its component peptides. Complications to this process includes peptides that possess coding variants (~10% of peptides in one of our data sets), peptides that map to multiple proteins (~50%) and high levels of peptides that are unobserved in at least one of the samples (~50%). Desing features of the MS experiment, such as the use of isobaric labels, can influence the observed pattern of missing data as well as the extent of technical sources of variation, motivating the need for flexible analytical tools. To accomplish this, I will use Bayesian approaches to model MS proteomics data to flexibly incorporate multiple sources of error, as well as address these challenging features of the MS experimental procedure. The resulting statistical software will be employed on multiple large proteomics data sets from genetically diverse mouse populations that possess similar levels of genetic variability as human populations. With the improved protein abundance estimates from my software, I will then perform genetic analyses to identify novel genetic regulators of the abundance of proteins, their complexes, and their interaction networks. Specific the experimental context of each data set, I will connect these regulatory signatures to important biological processes, such as aging in the kidney and heart and glucose metabolism in pancreatic islet cells. This project will produce new statistical tools that will increase the utility of MS proteomics data and the power of downstream genetic analyses, which will be demonstrated in real data. Novel genetic regulatory relationships underlying protein dynamics and functional networks will be identified. These tools and approaches will be relevant across diverse interest groups, spanning humans, model organism systems, and various disease-focused communities.

项目摘要 /摘要 蛋白质在所有生物系统中都起着重要的功能作用，并考虑到复杂的表达 在人群中观察到的表型和疾病。所有蛋白质的定量研究，即 蛋白质组学有潜力直接评估蛋白质动力学在个体，治疗和 理想情况下，以公正的方式进行曝光，不需要预先形成和有针对性的候选人。历史上 由于原始质谱法（MS）的局限性，蛋白质组学方法已受到限制。 可用的技术。转录组学经常被代替蛋白质组学，尽管尤其是 可以使蛋白质的调节与成绩单分解，从而使它们不完善。可行性 MS技术的快速发展得到了准确可靠的蛋白质组学。目前 蛋白质组学的统计工具落后并妨碍了这些丰富数据的全部使用 资源。 MS蛋白质组学数据具有许多独特且具有挑战性的功能，需要在其中解决 统计分析。蛋白质不是直接测量的，而是预先碎裂成较小的肽。一个 然后必须从其成分肽中重建蛋白质的丰度。这个过程的并发症 包括具有编码变体的肽（我们的一个数据集中约有10％的肽），映射的肽 至少一种样品中未观察到的多种蛋白质（〜50％）和高水平的肽 （〜50％）。 MS实验的降低特征，例如使用同荷标签，可能会影响观察到的 缺少数据的模式以及技术来源的范围，激发了灵活的需求 分析工具。为此，我将使用贝叶斯方法来对MS蛋白质组学数据进行建模 结合了多个错误来源，并解决了MS实验的这些挑战性特征 程序。最终的统计软件将用于多个大型蛋白质组学数据集中 具有与人类种群相似的遗传变异水平的遗传多样性小鼠种群。 随着我的软件的蛋白质丰度估算的提高，我将进行遗传分析 确定丰富的蛋白质，它们的复合物及其相互作用网络的新型遗传调节剂。 具体的每个数据集的实验环境，我将将这些调节签名连接到重要 生物学过程，例如肾脏和心脏的衰老以及胰岛细​​胞中的葡萄糖代谢。 该项目将生产新的统计工具，以增加MS蛋白质组学数据和功率的实用性 下游遗传分析将在实际数据中证明。新型遗传调节 将确定蛋白质动力学和功能网络的基础关系。这些工具和 方法将在各种利益群体，人类，模型有机体系统以及 各种以疾病为中心的社区。

项目成果

Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.

• DOI: 10.15252/msb.202110240
• 发表时间: 2021-08
• 期刊: Molecular systems biology
• 影响因子: 9.9
• 作者: Čuklina J;Lee CH;Williams EG;Sajic T;Collins BC;Rodríguez Martínez M;Sharma VS;Wendt F;Goetze S;Keele GR;Wollscheid B;Aebersold R;Pedrioli PGA
• 通讯作者: Pedrioli PGA