Abstract 556: Structural Characterization of Distinct Subparticles of Human High-density Lipoproteins Isolated from Plasma

Abstract

High-density lipoproteins (HDL) play a critical role in lipid transport and the maintenance of vascular homeostasis. HDL consists of a heterogeneous population of particles that vary in size, composition, and functionality. HDL is proteomically diverse; it can host at least 95 different proteins and structure-function studies suggest that specific protein complements on the surface of the particles are responsible for the broad spectrum of HDL function. The goal of this study was to isolate and fully characterize specific subspecies of HDL based on their protein complement and particle size. We built upon our previous studies where we segregated lipoproteins using immunoaffinity chromatography using antibodies specific to the two major scaffold proteins of HDL; apolipoprotein (apo)A-I and apoA-II. Two populations were obtained directly from plasma that either had both apoA-I and apoA-II (LpA-I/A-II) or just apoA-I with no apoA-II (LpA-I). These subpopulations were further fractionated using size exclusion chromatography using four superdex columns in series. We observed at least three distinctly sized particles within the LpA-I/A-II subfraction and two distinctly sized particles in the LpA-I subfraction which all elute in the size range of HDL. We successfully isolated the “large” and “small” subpopulations observed in the LpA-I subfraction and compositional analysis revealed profound proteomic differences between the two populations. Particles in the larger fraction were enriched in more common apolipoprotiens important for lipid metabolic processes such as apoL-I, apoD, apoM and apoE. Conversely, the smaller LpA-I particles were enriched in proteins important for immune response and complement activation. Using multiple cross-linking reagents, we have mapped the protein-protein interactions on each of these subpopulations to begin generating detailed structural models of these particles in silico .