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：
8468914
负责人：
DOUGLAS Scott JONES
金额：
$ 5.22万
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/8468914
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 computerized data processing cytokine drug development experimental analysis human disease inhibitor/antagonist insight molecular modeling network models novel novel strategies novel therapeutics predictive modeling research study response 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失调，针对这些途径的治疗疗法在治疗癌症和关节炎方面表现出了希望。可以在单个信号蛋白和“典型”途径上获得广泛的分子数据，但是从一种单元格到另一种细胞类型的信号网络的差异却不太了解。了解与患病国家相关的网络级别差异将大大推动新型治疗剂的发展。在类风湿关节炎（RA）中，新兴证据表明，作为疾病进展的主要参与者，驻留的成纤维细胞样的滑膜细胞（FL），但对其失调的信号网络的研究受到限制。该建议概述了一种系统地评估正常和患病个体的原始FLS细胞的综合实验和计算方法。我将构建FLS信号传导的预测数据驱动模型，并将信号网络活动与所得的细胞响应联系起来。我的具体目标是三倍：（i）提高我们对FLS细胞在疾病中如何出现问题的理解，（ii）确定标准临床治疗剂对RA对这些细胞的影响，以及（iii）用高治疗指数预测和测试使用势力的新药物靶标。在我们的初步研究中，我们对约15,000个数据点进行了汇编，这些数据点描述了响应于多种环境刺激的正常或RA原代细胞（培养中）的FL信号。在AIM 1中，我将在多个维度上扩展该汇编，以包括对正常患者和RA患者供体的八种不同原代人FLS细胞分离株中的信号传导和细胞反应的研究。这将提供有关疾病状态与患者对患者变异性引起的差异的见解。我还将在存在和不存在RA的临床疗法的情况下评估信号传导和反应，以识别在存在标准治疗方式的情况下持续存在的信号传导节点。在AIM 2中，我将采用多种数据驱动的建模方法，从我们的初步研究和AIM 1中收集的数据中推断出有意义的见解。多线性回归和部分最小二乘回归分析将将特定信号途径的活动与蜂窝响应的活动联系起来，将基于逻辑的建模方法与基于逻辑的建模方法相对于正常和RA，将基于逻辑的建模方法相应地相应。在AIM 3中，我将预测和测试新的蛋白质靶标，以在RA中进行治疗干预。 AIM 2中产生的预测模型将用于计算机中的假设检验，并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提高我们对类风湿关节炎的理解，并产生可能构成疾病构成的事件的精确分子模型。此外，它将创建一种综合方法，该方法可用于鉴定新的其他人类疾病的治疗干预部位。