Role of SREBP Network in Surfactant Lipid Homeostasis and Lung Maturation

描述(由申请人提供)：肺表面活性物质是气体交换所必需的，是适应出生时和出生后的空气呼吸所必需的。已知表面活性物质系统的缺陷与包括新生儿呼吸窘迫综合征在内的常见肺部疾病有关，新生儿呼吸窘迫综合征是早产儿死亡和发病的主要原因。关于表面活性物质系统的遗传调控知之甚少，尤其是基因和相关的转录网络在出生时起到诱导关键生理程序的作用。我们的长期目标是预防和控制与表面活性物质缺乏相关的常见肺部疾病，包括儿童和成人的新生儿呼吸窘迫综合征和急性呼吸窘迫综合征。这项应用的目的是整合计算和实验方法，以确定肺成熟过程中表面活性物质脂平衡的关键调节因素。这项应用旨在检验中心假设，即SREBP(类固醇调节元件结合蛋白)信号是转录网络中的关键组成部分，感知和调节肺成熟期间表面活性物质稳态的关键基因和过程。我们计划通过追求以下三个具体目标来实现我们的目标：1)。确定控制围产期表面活性物质脂平衡的转录网络；2)。确定SREBP影响围产期肺脂平衡的机制；以及3)确定SREBP网络中关键的围产期调节成分。在目标1下，我们将开发和完善一个转录网络，控制肺成熟过程中表面活性物质的脂质稳态。在目的2中，我们将利用SREBP缺失/激活转基因小鼠模型来确定和评价SREBP在活体围产期肺组织中的直接和代偿作用。在目标3中，我们将在体内和体外验证以SREBP为中心的信号通路上和下游的生物学相关基因。实验结果将用于进一步完善转录网络。该项目具有很高的创新性，将开发新的算法来进行数据集成和网络模型完善；将使用新开发的转基因小鼠模型和具有不同肺成熟程序的小鼠品系的高度精炼的肺mRNA微阵列数据，由此将在表面活性物质生物学中首次构建控制围产期肺表面活性物质脂质平衡的动态转录网络。这项研究意义重大，全面了解控制围产期肺表面活性物质水平的转录程序将为开发诊断试剂、疾病标志物和治疗干预措施提供科学依据，旨在预防或改善与肺表面活性物质缺乏相关的肺死亡和发病率。在这一应用中开发的计算和实验方法的协同整合将与与许多其他研究领域相关的研究高度相关。我们开发的微阵列数据集和网络模型将使整个科学界受益。

DESCRIPTION (provided by applicant): Pulmonary surfactant is essential for gas exchange and required for adaptation to air breathing at birth and thereafter. Defects in the surfactant system are known to be associated with common pulmonary disorders including neonatal respiratory distress syndrome, a major cause of mortality and morbidity in preterm infants. Little is known regarding the genetic regulation of the surfactant system, in particular the genes and associated transcriptional networks serve to induce the critical physiological program at birth. Our long-term goal is to prevent and control common pulmonary disorders associated with surfactant deficiency including neonatal respiratory distress syndrome and acute respiratory distress syndrome in children and adults. The objective of this application is to integrate computational and experimental approaches to identify critical regulators of surfactant lipid homeostasis during lung maturation. This application seek to test the central hypothesis that SREBP (sterol regulatory element binding protein) signaling is a key component in the transcriptional network sensing and regulating genes and processes critical for surfactant homeostasis during lung maturation. We plan to accomplish our objective by pursuing the following three Specific Aims: 1). Identify a transcriptional network controlling perinatal surfactant lipid homeostasis; 2). Determine the mechanisms by which SREBP influences perinatal lung lipid homeostasis; and 3) Determine the critical perinatal regulatory components in the SREBP network. Under Aim 1, we will develop and refine a transcriptional network controlling surfactant lipid homeostasis during lung maturation. In Aim 2, we will determine and evaluate the direct vs. compensatory roles of SREBP in perinatal lung in vivo using SREBP deletion/activation transgenic mouse models. In Aim 3, we will validate biological relevant upstream and downstream genes in SREBP centered signaling pathway in vivo and in vitro. The experimental results will be used to further refine the transcriptional network. The project is highly innovative, new algorithms will be developed for data integration and network model refinement; newly developed transgenic mouse models and highly refined lung mRNA microarray data from mouse strains with distinct lung maturation programs will be used, from which, a dynamic transcriptional network controlling perinatal lung surfactant lipid homeostasis will be constructed for the first time in surfactant biology. The proposed research is significant, a comprehensive understanding of the transcriptional program controlling perinatal lung surfactant level will provide scientific basis for development of diagnostic reagents, disease markers and therapeutic interventions aimed at preventing or ameliorating pulmonary mortality and morbidity associated with surfactant deficiency. The synergistic integration of computational and experimental approaches developed in this application will be highly relevant to studies related to many other research areas. The microarray datasets and network models we developed will benefit the scientific community at large.