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

• 批准号: 8310560
• 负责人: DOUGLAS Scott JONES
• 金额: $ 4.92万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 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/8310560
• 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 Delivery Systems; 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

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(申请人提供)：从慢性炎症到纤维性疾病和癌症，人类疾病的特征是细胞信号通路的失调，针对这些通路的疗法在治疗癌症和关节炎方面显示出希望。关于单个信号蛋白和“典型”途径的大量分子数据是可用的，但不同类型细胞之间信号网络的差异还不是很清楚。了解与疾病状态相关的网络水平差异将极大地推动新疗法的发展。在类风湿性关节炎(RA)中，新的证据表明常驻成纤维细胞样滑膜细胞(FLS)在疾病进展中起关键作用，但对其调节失调背后的信号网络的研究一直有限。这项建议概述了一种综合的实验和计算方法，以系统地评估来自正常和疾病个体的原代FLS细胞。我将构建FLS信令的预测性数据驱动模型，并将信令网络活动与由此产生的细胞响应联系起来。我的具体目标有三个：(I)增加我们对FLS细胞在疾病中如何出错的了解，(Ii)确定标准的RA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靶点。在我们的初步研究中，我们收集了一个大约15,000个数据点的概要，描述了正常或RA原代细胞(在培养中)对不同环境刺激的反应。在目标1中，我将从多个维度扩展这一概要，以包括对来自正常和RA患者捐赠者的8个不同的原代人类FLS细胞分离的信号和细胞反应的研究。这将提供对疾病状态与患者之间差异产生的差异的洞察。我还将直接评估在存在和不存在临床治疗方法的情况下RA的信号和反应，以确定在存在标准治疗模式的情况下坚持存在的信号节点。在目标2中，我将执行多种数据驱动的建模方法，以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。多元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响应联系起来，基于逻辑的建模方法将分别用于生成正常和RA FLS的特定细胞信号网络模型。在目标3中，我将预测和测试用于RA治疗干预的新的蛋白质靶点。在AIM 2中生成的预测模型将用于电子计算机中的假设检验，并将对有希望的假设进行实验评估。总而言之，这种实验和计算分析将显著增加我们对类风湿性关节炎的理解，并产生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地点。