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.
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.
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