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.

项目概要/摘要 蛋白质在几乎所有生物系统中都发挥着至关重要的功能作用，影响着复杂的表达 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，有可能直接评估蛋白质动力学如何随个体、治疗和治疗的变化而变化。 曝光，最好以公正的方式进行，不需要预先形成和有针对性的候选人。历史上有一个 由于原始质谱 (MS) 的限制，蛋白质组学方法受到限制 可用的技术。转录组学经常被用来代替蛋白质组学，但值得注意的是， 蛋白质的调控可以与其转录本脱钩，从而使它们成为不完美的代理。可行性 质谱技术的快速进步有助于准确可靠的蛋白质组学的发展。目前 蛋白质组学统计工具落后，阻碍了这些丰富数据的充分利用 资源。 MS 蛋白质组学数据具有许多独特且具有挑战性的特征，需要在其研究中加以解决。 统计分析。蛋白质不是直接测量的，而是预先断裂成更小的肽。一个 然后，蛋白质的丰度必须根据其组成肽来重建。此过程的复杂性 包括具有编码变体的肽（我们的数据集中约 10% 的肽）、映射的肽 至少一个样品中未观察到的多种蛋白质 (~50%) 和高水平肽 （~50%）。 MS 实验的设计特征（例如同量异位标记的使用）可能会影响观察到的结果 缺失数据的模式以及变化的技术来源的程度，激发了灵活的需求 分析工具。为了实现这一目标，我将使用贝叶斯方法对 MS 蛋白质组数据进行灵活建模 合并多个误差源，并解决 MS 实验的这些具有挑战性的特征 程序。由此产生的统计软件将用于多个大型蛋白质组数据集 具有与人类相似水平的遗传变异性的遗传多样性小鼠群体。 通过我的软件改进的蛋白质丰度估计，我将进行遗传分析 识别蛋白质丰度、其复合物及其相互作用网络的新型遗传调节因子。 具体每个数据集的实验背景，我将把这些监管签名与重要的 生物过程，例如肾脏和心脏的衰老以及胰岛细​​胞的葡萄糖代谢。 该项目将产生新的统计工具，提高 MS 蛋白质组数据的实用性和能力 下游遗传分析，这将在真实数据中得到证明。新型基因调控 将确定蛋白质动力学和功能网络之间的关系。这些工具和 方法将与不同的利益群体相关，涵盖人类、模型生物系统和 各种以疾病为重点的社区。

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