Novel and Robust Methods for Differential Protein Network Analysis of Proteomics Data in Schizophrenia Research

精神分裂症研究中蛋白质组数据差异蛋白质网络分析的新颖而稳健的方法

• 批准号: 9304868
• 负责人: Ying Ding
• 金额: $ 7.51万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9304868
• 关键词: Algorithms; Alzheimer' s Disease; Auditory area; Autopsy; Biological; Biological Models; Communities; Custom; Data; Data Analyses; Data Sources; Dependency; Detection; Development; Disease; Funding; Future; Gaussian model; Genes; Goals; Imagery; Joints; Mass Spectrum Analysis; Measures; Mental disorders; Methodology; Methods; Modeling; Normal tissue morphology; Pathology; Pathway Analysis; Patients; Peptides; Performance; Procedures; Proteins; Proteomics; Reproducibility; Research; Sample Size; Sampling; Schizophrenia; Statistical Methods; Structure; Synapses; Testing; Universities; Validation; Work; base; brain tissue; cloud based; conditioning; design; experimental study; improved; mouse model; neuropsychiatric disorder; novel; theories; tool

Abstract Biological networks such as protein networks provide an integrated perspective on how proteins work together and are becoming important tools to study neuropsychiatric disorders such as schizophrenia. Mass spectrometry (MS) based proteomics are rapidly advancing and are now capable of quantifying proteins with increased sensitivity and throughput, which provide critical data sources for protein networks and have been emerging as important application in the study of psychiatric diseases. For example, in our recent study, the synaptic protein co-expression network was found to be altered in the auditory cortex of schizophrenia patients. Whereas a variety of network analysis methods have now been developed for microarray data, methodologies customized to proteomic data are lagging far behind. In addition, these methods mainly focus on pairwise marginal correlations while ignoring the joint effects from other genes when constructing the network, failing to distinguish causal interactions from correlations via intermediate genes. Moreover, most existing methods for network testing are permutation based, from which the p-values could be invalid if the permutation-based null distribution is inaccurate. The probabilistic graphical model based differential network inference is more desirable as it infers conditional dependency by adjusting for the joint effects from all other proteins and guarantees to be valid and powerful when the distributional assumptions are satisfied. The objective of our proposed research is to develop, validate and apply novel and robust statistical methods to construct, analyze and infer protein networks from two popular proteomic platforms, namely, the targeted- MS and the unbiased differential-MS. The novel methodology will be immediately applied to the ongoing schizophrenia projects at the University of Pittsburgh, to facilitate novel analyses to identify protein alterations contributing to the disease pathology. First, we will develop novel network construction methodology based on a partial-correlation-based approach, which is under the Gaussian Graphical Model (GGM) framework and quantifies the correlation between two proteins after excluding the effects of other proteins, for protein network construction. Then, we will develop a novel differential network inference procedure, based on the recent development of GGM theory and associated inference, to formally test network differences. Finally, we will thoroughly validate the proposed methods using both statistically simulated data and the real data from a biological model with well characterized network interactions. Robustness of the networks will be assessed using rigorously designed replicate experiments with samples from post-mortem brain tissues of normal subjects. In summary, the novel methods and findings from this research will provide critical guidance for the design, analysis and validation of ongoing and future network studies that utilize proteomics approaches in psychiatric disorders, which will greatly improve the sensitivity and validity of the consequent scientific findings.

抽象的 蛋白质网络等生物网络提供了蛋白质如何协同工作的综合视角 并正在成为研究精神分裂症等神经精神疾病的重要工具。大量的 基于光谱 (MS) 的蛋白质组学正在迅速发展，现在能够用以下方法定量蛋白质： 提高的灵敏度和吞吐量，为蛋白质网络提供了关键的数据源，并已被 正在成为精神疾病研究中的重要应用。例如，在我们最近的研究中， 研究发现，精神分裂症患者的听觉皮层中的突触蛋白共表达网络发生了改变。 尽管现在已经针对微阵列数据开发了多种网络分析方法，但方法论 针对蛋白质组数据的定制远远落后。另外，这些方法主要关注pairwise 边际相关性，而在构建网络时忽略了其他基因的联合效应，未能 通过中间基因区分因果相互作用和相关性。此外，大多数现有方法 网络测试是基于排列的，如果基于排列的空值，则 p 值可能无效 分布不准确。基于概率图模型的差分网络推理更 理想的，因为它通过调整所有其他蛋白质的联合效应来推断条件依赖性 当满足分布假设时，保证是有效且有效的。 我们提出的研究的目标是开发、验证和应用新颖且稳健的统计方法 从两个流行的蛋白质组平台构建、分析和推断蛋白质网络，即靶向 MS 和无偏微分 MS。新方法将立即应用于正在进行的项目 匹兹堡大学的精神分裂症项目，旨在促进识别蛋白质改变的新分析 有助于疾病病理学。首先，我们将开发基于 基于偏相关的方法，在高斯图模型（GGM）框架下 对于蛋白质网络，在排除其他蛋白质的影响后量化两种蛋白质之间的相关性 建造。然后，我们将基于最近的研究开发一种新颖的差分网络推理程序 发展 GGM 理论和相关推理，以正式测试网络差异。最后，我们将 使用统计模拟数据和真实数据彻底验证所提出的方法 具有良好特征的网络相互作用的生物模型。将评估网络的稳健性 使用严格设计的重复实验，对正常人死后脑组织的样本进行研究 科目。总之，本研究的新颖方法和发现将为 设计、分析和验证正在进行的和未来的网络研究，这些研究利用蛋白质组学方法 精神疾病，这将大大提高后续科学发现的敏感性和有效性。