Personalized protein-protein interactomes and precision medicine in pulmonary arterial hypertension

项目摘要/摘要 肺动脉高压(PAH)是一种高度发病率的心肺疾病，其特征是 累及远端肺动脉导致右心衰竭的闭塞性血管病变。复杂和 整合的病理生物学信号通路驱动PAH的血管重塑：动脉病变包括 许多内表型(即特定的特征，如纤维化、细胞增殖等)出现在 不同患者的程度不同。在多环芳烃中也观察到蛋白质组和遗传图谱的广泛变异性， 这解释了表型的异质性和对药物治疗的不一致的临床反应 临床试验和临床点。总体而言，这些观察表明存在改善临床的机会。 通过个体化病理生物学-临床表型关系的结果。尽管如此，精准医学在 PAH仍未实现，我们推测这是由于传统分析方法固有的局限性 平均生物数据而忽略了功能上重要的信号通路。 我们实验室和其他实验室的工作已经证明了研究功能重要性的重要性 利用网络医学发现PAH中新的和可修改的治疗靶点的信号通路。这 这种方法不同于传统的基于转录本或蛋白质数量来推断功能的简化论方法 这可能会错误地将分子旁观者牵连到疾病的发病机制中。然而，我们有 创新了一种基于精确度的网络医疗策略，可生成患者特定的蛋白质-蛋白质相互作用 (PPI)网络(例如，患者级分子配线图)。我们的方法揭示了分子相互作用 区分(和分组)具有相同临床表型的个体患者。我们呈现的是小说 随附的申请中的初步数据支持中心假设：发展中的患者- 特定的PPI网络将使PAH的临床表型个性化，并优化预后。我们的发现 还将澄清多环芳烃遗传风险与个体-患者水平上的病理生物学之间的关系，以及 使用PPI网络提供基本原理和个性化的药物选择。为了检验我们的假设，我们将利用 来自英国PAH Phenome生物库的丰富数据集。此数据集包括全面的 遗传性特发性PAH的两个时间点(相隔一年)的蛋白质、基因组、临床和预后数据 PAH，PAH患者的无症状家庭成员，包括三名在 研究，以及健康志愿者对照组(N=500人)。目标是：(1)分析特定患者的PPI网络 使用蛋白质组和遗传学数据，并按患者组分析网络特征的时间差异，(2) 开发、测试和验证网络评分，以告知个别PAH患者的表型和结果。AS 作为一个探索性的目标，我们将使用PPI网络来预测患者特定的药物治疗。总体而言，来自 该项目将推进PAH的精准医学，与患者的临床管理直接相关。

Project Summary/Abstract Pulmonary arterial hypertension (PAH) is a highly morbid cardiopulmonary disease characterized by an obliterative vasculopathy involving distal pulmonary arterials that promotes right heart failure. Complex and integrated pathobiological signaling pathways drive vascular remodeling in PAH: the arteriopathy includes numerous endophenotypes (i.e., specific features, such as fibrosis, cellular proliferation, others) that occur to differing extent across patients. Wide variability in the proteomic and genetic profile is also observed in PAH, which accounts for phenotypic heterogeneity and inconsistent clinical response to drug therapies reported in clinical trials and at point-of-care. Overall, these observations suggest that opportunity exists to improve clinical outcome by individualizing the pathobiology-clinical phenotype relationship. Nonetheless, precision medicine in PAH remains unrealized, which we postulate is due to limitations inherent in conventional analytical methods that average biological data and overlook functionally important signaling pathways. Work from our laboratory and others has demonstrated the importance of studying functionally significant signaling pathways using network medicine to discover novel and modifiable therapeutic targets in PAH. This approach differs from classic reductionist methods that infer functionality based on transcript or protein quantity alone, which may erroneously implicate molecular bystanders in the pathogenesis of disease. However we have innovated a precision-based network medicine strategy that generates patient-specific protein-protein interaction (PPI) networks (e.g., patient-level molecular wiring map). Our approach unmasks molecular interactions that distinguish (and group together) individual patients with the same clinical phenotype. We present novel preliminary data in the accompanying application to support the central hypothesis: Developing patient- specific PPI networks will personalize clinical phenotyping and optimize prognosis in PAH. Our findings will also clarify the relationship between PAH genetic risk and pathobiology on an individual-patient level, and inform rationale and personalized drug selection using the PPI networks. To test our hypothesis, we will leverage a rich dataset from the United Kingdom PAH Phenome Biobank. This dataset includes comprehensive proteomic, genomic, clinical, and outcome data across two timepoints (1 yr apart) for idiopathic PAH, hereditary PAH, asymptomatic family members of patients with PAH, including three patients that developed PAH during the study, and healthy volunteer controls (N=500 total). The Aims are: (1) Profile patient-specific PPI networks using proteomic and genetic data, and analyze temporal differences in network features by patient group, (2) Develop, test, and validate a network score that informs phenotype and outcome of individual PAH patients. As an exploratory aim, we will use the PPI networks to predict patient-specific drug therapies. Overall, findings from this project will advance precision medicine in PAH with direct relevance to the clinical management of patients.