Bioinformatic analysis of molecular networks in peripheral artery disease

描述(申请人提供)：外周动脉疾病(PAD)是由于外周动脉血流受阻引起的，最常见的是供应腿部的动脉。这种阻塞减少了导致腿部疼痛和行走困难的活动所需的灌注量增加。在其最严重的形式下，垫限制了腿部的血流灌注，导致肢体截肢。在美国，PAD影响着大约800万至1200万人，特别是50岁以上的人，其患病率现在几乎与冠状动脉疾病相当。为了尝试增加阻塞周围的血液流动，使用药物和基因传递的临床试验旨在刺激血管生长和重塑(血管生成和动脉生成)，已经进行了十多年，但一直没有成功。目前对PAD的治疗方法和建议并不影响肢体的血流灌注，明确PAD的新靶点和开发新的治疗方法是一个明显未得到满足的医学需求。PAD涉及的已知信号通路的复杂性，包括各种生长因子和它们的串扰，表明高通量的实验数据和使用生物信息学和系统生物学方法进行分析可能导致对该病的新的理解水平，以及新的和迄今为止意想不到的潜在靶点。这样的生物信息学分析还没有系统地对PAD进行。因此，我们将使用生物信息学方法来分析现有的来自PAD患者和PAD小鼠模型的多个大规模高通量基因表达数据集，目的是识别和系统化重要的蛋白质和信号通路，以PAD交互组的形式发现蛋白质之间的联系，进行电子药物重新定位研究以预测潜在的治疗靶点，并使用先前积累的人类活检样本验证它们。对分子、途径、蛋白质-蛋白质相互作用和药物靶点的系统分析将为PAD的预防和治疗提供亟需的指导。

DESCRIPTION (provided by applicant): Peripheral artery disease (PAD) results from an obstruction of blood flow in the peripheral arteries, most commonly the arteries that supply the legs. The blockage reduces the increases in perfusion that are required with activity causing leg pain and difficulty walking. In its most severe form PAD limits leg perfusion resulting in limb amputation. PAD affects approximately 8 to 12 million persons in United States, especially those over the age of 50 and its prevalence is now almost comparable to that of coronary artery disease. With the goal of trying to increase blood flow around blockages clinical trials using drugs and gene delivery aimed at stimulating vascular growth and remodeling (angiogenesis and arteriogenesis) have been performed for more than a decade but have not been successful. Current treatments and recommendations for PAD do not affect limb perfusion and there is a clear unmet medical need to identify novel targets and develop new treatments for PAD. The complexity of the known signaling pathways involved in PAD, including various growth factors and their crosstalks, suggests that high-throughput experimental data and their analysis using bioinformatics and systems biology methods could lead to a new level of understanding of the disease as well as novel and hereunto unanticipated potential targets. Such bioinformatics analyses have not been systematically performed for PAD. Thus, we will use bioinformatic approaches to analyze existing multiple large-scale high-throughput gene expression datasets from PAD patients and mouse models of PAD with the goal of identifying and systematizing important proteins and signaling pathways, discover connections between the proteins in the form of a PAD interactome, performing in silico drug repositioning studies to predict potential therapeutic targets, and validating them using previously accumulated human biopsy samples. A systematic analysis of molecules, pathways, protein-protein interactions, and drug targets will provide much needed guidance for prevention and treatment of PAD.