Robust statistical approaches for decoding protein and mRNA expression regulation

描述（由申请人提供）：基因表达由动态调节mRNA和蛋白质浓度的多个过程协调。例如，在环境胁迫期间，翻译通常减弱，而特异性转录因子和蛋白酶体降解的活性增加。由于一个给定的基因可能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。为了满足目前对严格的统计方法的需求，从这些浓度数据中提取监管信息，我们的团队开发了一种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和蛋白质水平的调控变化的显著性分析提供了一个基本平台。在这里，提出了几个新的统计模块内的PECA框架的发展，这将是很容易使用的定量，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输出验证的广泛实验，以及将其应用于哺乳动物细胞中的氧化应激反应-一种与致癌和神经退行性疾病相关的突出环境压力。目标1将整合详细的转录组学和蛋白质组学数据，从人类细胞系受到氧化应激与蛋白质相互作用网络数据赋予模块化的PECA，使蛋白质复合物（PECA-N）的成员的并发或缺失的监管变化的检测。目标2将集中于估计适当缩放的合成和降解速率，这是基因表达调控（PECA-R）的基本组成部分。将监测氧化强制降解下的蛋白质翻译和降解速率变化，以校准和验证结果。目标3将同时模拟翻译后修饰与浓度数据（PECA-M），并将其映射到蛋白质泛素化影响蛋白酶体降解的特定调控途径（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，该团队是理想的 这些努力

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.