Robust statistical approaches for decoding protein and mRNA expression regulation

用于解码蛋白质和 mRNA 表达调控的稳健统计方法

• 批准号: 8894532
• 负责人: Christine Vogel
• 金额: $ 38.49万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8894532
• 关键词: Affect; Attenuated; Biological Markers; Data; Detection; Development; Disease Progression; Gene Expression; Gene Expression Profiling; Gene Expression Regulation; Genes; Health; Human Cell Line; Mammalian Cell; Maps; Messenger RNA; Modeling; Molecular; Monitor; Neurodegenerative Disorders; Output; Oxidative Stress; Post-Translational Protein Processing; Process; Proteins; Proteomics; Regulation; Regulatory Pathway; Statistical Methods; Statistical Models; Stress; System; Technology; Time; Translations; Ubiquitination; Validation; Work; biological adaptation to stress; carcinogenesis; insight; mRNA Expression; meetings; member; protein complex; protein expression; research study; therapeutic development; transcription factor; transcriptomics

DESCRIPTION (provided by applicant): Gene expression is coordinated by multiple processes that dynamically adjust the concentration of mRNAs and proteins. During environmental stress, for example, translation is generally attenuated, while activities of specifi transcription factors and proteasomal degradation increase. Since a given gene can be affected by multiple processes acting at different time points, resolving the exact dynamics and interactions of regulatory processes is crucial. To fully capture such dynamics from all angles, transcriptomic and proteomic data need to be collected simultaneously in time course experiments. To meet the current need for a rigorous statistical method to extract regulatory information from these concentration data, a statistical framework, called Protein Expression Control Analysis (PECA), was developed by our team. PECA provides a basic platform for significance analysis of regulation changes at the mRNA- and protein-levels in dynamic systems. Here, the development of several new statistical modules within the PECA framework is proposed, which will be readily usable for quantitative, multi-level gene expression studies. The work includes extensive experiments for output validation using cutting edge molecular technologies, and their application to the oxidative stress response in mammalian cells - a prominent environmental stress with relevance for carcinogenesis and neurodegenerative diseases. Aim 1 will integrate detailed transcriptomics and proteomics data from a human cell line subjected to oxidative stress with protein interaction network data to confer modularity to PECA that enables detection of concurrent or missing regulatory changes for members of protein complexes (PECA-N). Aim 2 will focus on estimating properly scaled rates of synthesis and degradation which are essential constituents of gene expression regulation (PECA-R). Protein translation and degradation rate changes under oxidative stress will be monitored to calibrate and validate the results. Aim 3 will simultaneously model post-translational modifications with concentration data (PECA-M) and map them to the specific regulatory pathway of protein ubiquitination affecting proteasomal degradation (UBICON). Thanks to expertise in proteomics, gene expression analysis, and statistical modeling, this team is ideal for these efforts.

描述（由申请人提供）：基因表达是由多个过程协调的，这些过程动态调节mrna和蛋白质的浓度。例如，在环境胁迫下，翻译通常会减弱，而特定转录因子的活性和蛋白酶体降解则会增加。由于一个给定的基因可以受到多个过程在不同时间点的影响，解决调控过程的确切动力学和相互作用是至关重要的。为了从各个角度充分捕捉这种动态，需要在时间过程实验中同时收集转录组学和蛋白质组学数据。为了满足当前对从这些浓度数据中提取调控信息的严格统计方法的需求，我们的团队开发了一个称为蛋白质表达控制分析（PECA）的统计框架。PECA为动态系统中mRNA和蛋白水平调控变化的显著性分析提供了基础平台。本文提出在PECA框架内开发几个新的统计模块，这将很容易用于定量的、多层次的基因表达研究。这项工作包括使用尖端分子技术进行广泛的输出验证实验，并将其应用于哺乳动物细胞的氧化应激反应——一种与致癌和神经退行性疾病相关的突出环境应激。目标1将整合来自氧化应激人类细胞系的详细转录组学和蛋白质组学数据与蛋白质相互作用网络数据，赋予PECA模块化，从而能够检测蛋白质复合物成员（PECA- n）的并发或缺失的调节变化。目标2将集中于估计适当比例的合成和降解率，这是基因表达调控（PECA-R）的重要组成部分。将监测氧化应激下蛋白质翻译和降解率的变化，以校准和验证结果。Aim 3将同时用浓度数据（PECA-M）模拟翻译后修饰，并将其映射到影响蛋白酶体降解的蛋白质泛素化的特定调控途径（UBICON）。由于在蛋白质组学，基因表达分析和统计建模方面的专业知识，这个团队是理想的