HDL is a complex molecular particle mediating reverse cholesterol transport in the human body. High plasma levels of HDL cholesterol are clinically associated with a lower risk of coronary heart disease (CHD) and diabetes mellitus type 2 (T2DM). However, HDL could not yet be successfully exploited for the prevention or treatment of disease. This is mainly because the structure-function relationship of this complex particle is still unresolved. A prerequisite for establishing such a structure-function relationship would be the detailed molecular knowledge about the components of the HDL particle in health and disease.
Here, we set out to characterize the protein and lipid composition of HDL particles from normal individuals and patients with CHD and/or T2DM. To link particle composition with phenotypic functionality, we implemented a set of eleven cellular in vitro assays as functional readouts for vasoprotective and anti-diabetic effects mediated by HDL. To quantitatively analyze the HDL particle proteotype of a larger patient cohort we made use of data-independent acquisition mass spectrometry (DIA/SWATH-MS). We first established a DIA/SWATH library from pooled HDL samples, which resulted in a DIA spectral library representing 356 protein groups. We used the HDL library in order to digitize a clinical HDL sample cohort consisting of 166 patients including healthy controls, resulting in the quantitation of 182 protein groups across the cohort. Simultaneously, the phenotypic impact of these HDL particles was tested in our cellular model systems as measures for clinical/biological functionality. Bioinformatics analysis using elastic net regularization revealed novel structure-function determinants which were tested in vitro using reconstituted minimal HDL particles of defined protein/lipid composition. Here, GPLD1 was found to be one of the major determinants of HDL-mediated anti-apoptotic function.
Together, we established an integrative approach and bioinformatical framework supporting the discovery of novel HDL-determinants of clinical relevance.