A Network-based Approach to Associate HDL Subspeciation with Function

基于网络的 HDL 亚种与功能关联方法

• 批准号: 8372601
• 负责人: Long Lu
• 金额: $ 55.12万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8372601
• 关键词: Activities of Daily Living; Agonist; Apolipoprotein A-I; Biological Assay; Biological Markers; Blood; Blood Vessels; Cardiovascular Diseases; Characteristics; Cholesterol; Classification; Clinical; Complement; Complement Activation; Disease; Future; Genetic; Goals; Heterogeneity; High Density Lipoproteins; Human; Immunoprecipitation; Individual; Knockout Mice; Knowledge; Laboratories; Link; Lipids; Low Density Lipoprotein oxidation; Mass Spectrum Analysis; Measures; Mediating; Metabolism; Minor; Molecular; Natural Immunity; Network-based; Pathology; Pattern; Peptide Hydrolases; Phylogenetic Analysis; Physiological; Plasma; Platelet aggregation; Play; Property; Proteins; Proteomics; Relative (related person); Reproducibility; Research; Role; Signal Transduction; Staging; Techniques; Test Result; Therapeutic; United States; Variant; Work; base; cardiovascular disorder prevention; cardiovascular disorder risk; computer framework; design; human NOS3 protein; improved; innate immune function; innovation; lipid transport; macrophage; migration; mortality; mouse model; novel; novel strategies; particle; prevent; protective effect; protein complex; protein profiling; research study; small molecule; ward

DESCRIPTION (provided by applicant): High density lipoproteins (HDL) are blood-borne complexes of protein and lipid that play critical roles in the prevention of cardiovascular disease (CVD), the major cause of mortality in the U.S. Despite its compositional heterogeneity and functional diversity, in a clinical setting, HDL is still commonly thought of as a single entity tht primarily functions in lipid transport. Recently, a growing body of evidence, including our research, has suggested there are numerous separate functions mediated by distinct stable subspecies which happen to cofractionate with classically defined "HDL". Unfortunately, little is understood about the HDL subspeciation in either basal or diseased states. The long-term goal of our laboratories is to understand the molecular basis of HDL's protection against CVD. The overall objective of this application is to develop, validate and standardize a novel approach which combines advanced proteomic analysis, functional assays and a network-based computational framework to identify new HDL species in normal human plasma and associate them with known HDL functions. Our hypothesis is that HDL is composed of numerous distinct particle subpopulations, each containing a unique protein make-up, which plays distinct physiological roles ranging from cholesterol transport to vascular signaling to innate immune function. We will pursue the following three specific aims: 1. Proteomic characterization and functional profiling of HDL sub-fractions. We hypothesize that variation in the proteomic composition of HDL particles results in different functional capacities for each subspecies. In our preliminary study, we have developed four orthogonal separation techniques. We will use mass spectrometry to profile the protein abundance level in 10-20 fractions derived from each separation technique, and examine potential co-migration patterns among protein pairs. The fractions will simultaneously be subjected to a panel of four functional assays: (a) ability to prevent oxidation of low density lipoprotein (LDL) particles; (b) ability to promote cholesterol efflux from macrophages; (c) effects on vascular function, measured as activation of endothelial nitric oxide synthase (eNOS); and (d) inhibition of agonist induced platelet aggregation. 2. Prediction of HDL interactome network using an integrative approach. Interacting proteins are often found to share common properties, e.g., similar phylogenetic profiles and co-expression patterns. These common characteristics have been shown to be predictive of protein interactions as features. We hypothesize that the interactions among HDL proteins can be accurately predicted by integrating their co-migration patterns and four most relevant features. These interacting protein pairs may co-exist in functionally synergistic HDL subparticles. Potential interacting proteins will be verified by immunoprecipitation experiments and the testing results will be used to further improve the accuracy of predictions by adjusting parameters. 3. Identification of functional modules responsible for known HDL functions from HDL interactome network. We hypothesize that, using a network-based classification, functional modules that optimally correlate with functional activity profiles can be identified from the HDL interactome network. A module, consisting of a group of HDL proteins, may correspond to the entirety or part of an HDL particle that carries out a given HDL function. We expect these network-based modules will outperform individual proteins as markers for HDL functions in both reproducibility and accuracy. This will set the stage for a future study where genetic knockout mouse models will be used to verify this particle-function relationship. This application is highly innovative because the integration of computational and experimental approaches will uncover the relationship between HDL subspeciation and function in a way that has not been attempted previously. As such, it will fill a major gap in our understanding of the compositional and functional heterogeneity of HDL particles. This work will have significant impacts on several fronts: First, the project will facilitate our molecular understanding of HDL functions by simultaneously identifying new HDL subspecies and linking them with known functions. Second, studying the HDL interactome network may reveal novel HDL functions. Third, the validated network-based approach can also be applicable to correlate HDL subspecies with CVD status, resulting in effective disease biomarkers. Finally, in the long term, therapeutic strategies can be designed to modify certain HDL subparticles or mimic their effects with the goal of reducing CVD. PUBLIC HEALTH RELEVANCE: High density lipoproteins (HDLs) play critical roles in the prevention of cardiovascular disease (CVD), the major cause of mortality in the U.S. Despite its compositional heterogeneity and functional diversity, in a clinical set- ting, HDL is still commonl thought of as a single entity that primarily functions in lipid transport. Recently, a growing body of evidence, including our research, has suggested there are numerous separate functions mediated by distinct subspecies which happen to cofractionate with classically defined "HDL". Unfortunately, little is understood about the HDL subspeciation in either basal or diseased states. Thus the overall objective of this application is to develop, validate and standardize a novel approach which combines advanced proteomic analysis, functional assays and a network-based computational framework to identify new HDL species in normal human plasma and associate them with known HDL functions. With such knowledge, more targeted therapies can be explored to boost the cardio-protective HDL particles. In addition, small molecule therapies could be explored that mimic the cardio-protective effects of identified beneficial HDL subspecies. Further- more, these HDL subparticles may serve as predictive biomarkers for individuals at risk for CVD.

描述（由申请人提供）：高密度脂蛋白（HDL）是血液中的蛋白质和脂质复合物，在预防心血管疾病中发挥着关键作用 (CVD)，美国死亡的主要原因，尽管其成分异质性和功能多样性，在临床环境中，HDL 仍然普遍被认为是主要在脂质运输中发挥作用的单一实体。最近，包括我们的研究在内的越来越多的证据表明，不同的稳定亚种介导了许多独立的功能，这些亚种恰好与经典定义的“HDL”共分解。不幸的是，人们对基础状态或患病状态下的 HDL 亚种知之甚少。我们实验室的长期目标是了解 HDL 预防 CVD 的分子基础。该应用的总体目标是开发、验证和标准化一种新方法，该方法结合了先进的蛋白质组分析、功能测定和基于网络的计算框架，以识别正常人血浆中的新 HDL 物种并将其与已知的 HDL 功能相关联。我们的假设是，HDL 由许多不同的颗粒亚群组成，每个亚群都含有独特的蛋白质组成，发挥着不同的生理作用，从胆固醇运输到血管信号传导再到先天免疫功能。我们将追求以下三个具体目标： 1. HDL 亚组分的蛋白质组学表征和功能分析。我们假设 HDL 颗粒蛋白质组组成的变化导致每个亚种的功能能力不同。在我们的 初步研究，我们开发了四种正交分离技术。我们将使用质谱分析来自每种分离技术的 10-20 个级分中的蛋白质丰度水平，并检查蛋白质对之间潜在的共迁移模式。这些级分将同时进行一组四项功能测定：（a）防止低密度脂蛋白（LDL）颗粒氧化的能力； (b) 促进胆固醇从巨噬细胞流出的能力； (c) 对血管功能的影响，通过内皮一氧化氮合酶（eNOS）的激活来测量； (d)抑制激动剂诱导的血小板聚集。 2. 使用综合方法预测 HDL 相互作用组网络。通常发现相互作用的蛋白质具有共同的特性，例如相似的系统发育谱和共表达模式。这些共同特征已被证明可以作为特征来预测蛋白质相互作用。我们假设 HDL 蛋白之间的相互作用可以通过整合 HDL 蛋白的共同迁移模式和四个最相关的特征来准确预测。这些相互作用的蛋白质对可能共存于功能协同的 HDL 亚颗粒中。潜在的相互作用蛋白将通过免疫沉淀实验进行验证，测试结果将用于通过调整参数进一步提高预测的准确性。 3.从HDL交互组网络中识别负责已知HDL功能的功能模块。我们假设，使用基于网络的分类，可以从 HDL 相互作用组网络中识别出与功能活动概况最佳相关的功能模块。由一组 HDL 蛋白组成的模块可以对应于执行给定 HDL 功能的 HDL 颗粒的整体或部分。我们预计这些基于网络的模块在可重复性和准确性方面将优于作为 HDL 功能标记的单个蛋白质。这将为未来的研究奠定基础，利用基因敲除小鼠模型来验证这种粒子功能关系。该应用具有高度创新性，因为计算和实验方法的集成将以前所未有的方式揭示 HDL 亚种和功能之间的关系。因此，它将填补我们对 HDL 颗粒的组成和功能异质性理解的重大空白。这项工作将在几个方面产生重大影响：首先，该项目将通过同时识别新的 HDL 亚种并将其与已知功能联系起来，促进我们对 HDL 功能的分子理解。其次，研究 HDL 相互作用网络可能会揭示新的 HDL 功能。第三，经过验证的基于网络的方法还可以适用于将 HDL 亚种与 CVD 状态相关联，从而产生有效的疾病生物标志物。最后，从长远来看，治疗策略可以是 旨在修改某些 HDL 亚颗粒或模仿其效果，以减少 CVD。 公共健康相关性：高密度脂蛋白 (HDL) 在预防心血管疾病 (CVD) 方面发挥着关键作用，而心血管疾病是美国死亡的主要原因。尽管其成分异质性和功能多样性，但在临床环境中，HDL 仍然普遍被认为是主要在脂质转运中发挥作用的单一实体。最近身体越来越大 包括我们的研究在内的大量证据表明，不同亚种介导了许多独立的功能，这些亚种恰好与经典定义的“HDL”共同分解。不幸的是，人们对基础状态或患病状态下的 HDL 亚种知之甚少。因此，该应用的总体目标是开发、验证和标准化一种新方法，该方法结合了先进的蛋白质组分析、功能测定和基于网络的计算框架，以识别正常人血浆中的新 HDL 物种并将其与已知的 HDL 功能相关联。有了这些知识，就可以探索更有针对性的疗法来增强具有心脏保护作用的高密度脂蛋白颗粒。此外，还可以探索模仿已确定的有益 HDL 亚种的心脏保护作用的小分子疗法。此外，这些 HDL 亚颗粒可以作为有 CVD 风险的个体的预测生物标志物。