Oral Presentation HUPO 2019 - 18th Human Proteome Organization World Congress

Proteomic profiling of plasma lipoprotein particles as a tool to identify novel subspecies. (#68)

Sonja Hartwig 1 2 , Joerg Kotzka 1 2 , Stefan Lehr 1 2
  1. German Diabetes Center (DDZ), German Diabetes Center, Duesseldorf, NRW, Germany
  2. German Center for Diabetes Research (DZD), Duesseldorf, NRW, Germany


Plasma lipoprotein particles are complex microemulsions of proteins and lipids, which can be divided into seven classes based on size, lipid composition, and apolipoproteins (chylomicrons, chylomicron remnants, VLDL, IDL, LDL, HDL and Lp (a)). Recent studies reveal that the protein composition of each lipoprotein particle class is highly diverse and modulations of these may have significant impact on the development of metabolic diseases. The aim of this study is to comprehensively characterize the protein signatures of the known lipoprotein particles derived from human plasma samples to identify novel subspecies.


Human plasma samples (0.5ml) were separated by size exclusion FPLC on a Superpose 6 column. To assign the fractions to dedicated lipoprotein classes apolipoprotein marker proteins were monitored by western blot analyses and matched to cholesterol and triglyceride content. In order to avoid contamination from co-eluting plasma proteins lipoprotein particles fractions were cleaned up with Calcium Silicate Hydrate (CSH). Subsequently, isolated lipoprotein particles were measured by high resolution mass spectrometry (Orbitrap Lumos).


Continuous FPLC-separation revealed 20 fractions containing lipoprotein particles. By means of the marker protein and cholesterol/triglyceride distribution, fractions were assigned to distinct lipoprotein particles, i.e. VLDL, IDL, LDL and HDL. Proteomic profiling by mass spectrometry assigned more than 400 different proteins to the lipoprotein fractions.


Our proteomic profiling approach provide a basis to identify novel compositions of known lipoprotein particles which might represent as predictive markers for multifactorial metabolic diseases including type 2 diabetes.