Data-Driven Modeling of Signaling Dysregulation in Rheumatoid Arthritis

类风湿关节炎信号传导失调的数据驱动建模

基本信息

批准号：
8654301
负责人：
DOUGLAS Scott JONES
金额：
$ 5.7万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-04-02 至 2015-04-01
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8654301
关键词：
Advanced Development Angiogenic Factor Anti-Inflammatory Agents Anti-inflammatory Arthritis Automobile Driving Biology Cell physiology Cells Chronic Clinical Complex Computer Analysis Computer Simulation Coupled Cytokine Signaling Data Data Set Dexamethasone Dimensions Disease Disease Progression Drug Targeting Environment Evaluation Event Experimental Models FDA approved Fibroblasts Fibrosis Fuzzy Logic Generations Goals Growth Factor Health Heart Human Immune Individual Inflammation Inflammatory Intracellular Signaling Proteins Investigation Least-Squares Analysis Ligands Linear Regressions Link Logic Malignant Neoplasms Measurement Metalloproteases Methods Methotrexate Modality Modeling Molecular Molecular Models Normal Cell Pathologic Processes Pathway interactions Patients Peptide Hydrolases Pharmaceutical Preparations Play Process Protein Secretion Proteins Regression Analysis Rheumatoid Arthritis Role Signal Pathway Signal Transduction Signaling Protein Site Source Stimulus Testing Therapeutic Therapeutic Index Therapeutic Intervention Translating Validation Work adipokines base cell type clinical practice computer framework cytokine drug development experimental analysis human disease inhibitor/antagonist insight molecular modeling network models novel novel strategies novel therapeutics predictive modeling research study response signal processing small molecule standard care therapeutic target

项目摘要

DESCRIPTION (provided by applicant): Human diseases ranging from chronic inflammation to fibrotic disorders and cancer are characterized by dysregulation of cellular signaling pathways, and therapeutics targeting these pathways have shown promise in treating cancer and arthritis. Extensive molecular data is available on individual signaling proteins and on "canonical" pathways, but differences in signaling networks from one cell type to the next are less well understood. Understanding network-level differences associated with diseased-states would substantially advance the development of novel therapeutics. In rheumatoid arthritis (RA), emerging evidence points to the resident fibroblast-like synoviocytes (FLS) as key players in disease progression, but studies of the signaling networks underlying their dysregulation have been limited. This proposal outlines an integrated experimental and computational approach to systematically evaluate primary FLS cells from normal and diseased individuals. I will construct predictive data-driven models of FLS signaling, and link signaling network activity to the resulting cellular response. My specific goals are three-fold: (i) to increase our understanding into how FLS cells have gone awry in disease, (ii) to determine the effects of standard clinical therapeutics for RA on these cells, and (iii) to predict and test new drug targets with the potentil for high therapeutic index. In our preliminary studies we have colected a compendium of ~15,000 data points describing the signaling of FLS from normal or RA primary cells (in culture) in response to diverse environmental stimuli. In Aim 1 I will expand this compendium in multiple dimensions to include investigation of both signaling and cellular responses in eight different primary human FLS cell isolates from normal and RA patient donors. This will provide insights into differences arising from disease-state vs. patient-to-patient variability. I will also directl evaluate the signaling and responses in the presence and absence of clinical therapeutics for RA to identify signaling nodes that persist in the presence of standard treatment modalities. In Aim 2 I will perform multiple data-driven modeling approaches to infer meaningful insights from data collected in our preliminary studies and in Aim 1. Multilinear regression and partial least squares regression analyses will connect activities of specific signaling pathways with cellular responses, and logic-based modeling approaches will be used to generate cell-specific signaling network models for normal and RA FLS, respectively. In Aim 3 I will predict and test novel protein targets for therapeutic intervention in RA. The predictive models generated in Aim 2 will be used for hypothesis testing in silico, and promising hypotheses will be evaluated experimentally. Collectively, this experimental and computational analysis will significantly increase our understanding of rheumatoid arthritis and generate precise molecular models of events likely to underlie disease. Furthermore, it will create an integrated approach that can be used to identify novel sites for therapeutic intervention in a range of other human diseases.

描述（由申请人提供）：从慢性炎症到纤维化疾病和癌症的人类疾病的特征是细胞信号传导途径的失调，并且针对这些途径的治疗已显示出治疗癌症和关节炎的前景。关于单个信号蛋白和“规范”途径有大量的分子数据，但一种细胞类型与另一种细胞类型之间信号网络的差异尚不清楚。了解与疾病状态相关的网络水平差异将大大促进新疗法的开发。在类风湿性关节炎（RA）中，新出现的证据表明常驻成纤维细胞样滑膜细胞（FLS）是疾病进展的关键参与者，但对其失调背后的信号网络的研究有限。该提案概述了一种综合的实验和计算方法，用于系统地评估来自正常和患病个体的原代 FLS 细胞。我将构建 FLS 信号传导的预测数据驱动模型，并将信号传导网络活动与产生的细胞反应联系起来。我的具体目标有三个：(i) 加深我们对 FLS 细胞在疾病中如何出错的理解，(ii) 确定 RA 标准临床疗法对这些细胞的影响，以及 (iii) 预测和测试具有高治疗指数潜力的新药物靶点。在我们的初步研究中，我们收集了约 15,000 个数据点的概要，描述了正常或 RA 原代细胞（培养中）响应不同环境刺激的 FLS 信号传导。在目标 1 中，我将从多个维度扩展此纲要，包括对来自正常和 RA 患者供体的八种不同原代人 FLS 细胞分离物的信号传导和细胞反应进行研究。这将提供对疾病状态与患者之间变异性所产生的差异的见解。我还将直接评估在存在和不存在 RA 临床治疗的情况下的信号传导和反应，以确定在标准治疗方式存在下持续存在的信号传导节点。在目标 2 中，我将执行多种数据驱动的建模方法，从我们的初步研究和目标 1 中收集的数据中推断出有意义的见解。多线性回归和偏最小二乘回归分析将特定信号通路的活动与细胞反应联系起来，并逻辑-基于建模的方法将用于分别生成正常 FLS 和 RA FLS 的细胞特异性信号网络模型。在目标 3 中，我将预测和测试用于 RA 治疗干预的新蛋白质靶点。 Aim 2 中生成的预测模型将用于计算机假设检验，并且将通过实验评估有希望的假设。总的来说，这种实验和计算分析将显着增加我们对类风湿关节炎的理解，并生成可能导致疾病的事件的精确分子模型。此外，它将创建一种综合方法，可用于确定对一系列其他人类疾病进行治疗干预的新位点。