Lipid handling and apolipoprotein dynamics are complicated pathways to follow in vivo. Singh et al. (2016) describe a mass spectrometriy-based high-resolution accurate-mass (HRAM) workflow that uses stable isotope labeling (SIL) to enable tracer studies in human subjects following a high-fat diet.1 Not only do the researchers show protein kinetics in circulation, but they also present quantitative data from multiple apolipoproteins involved in high-density lipoprotein (HDL) production.
In a clinical setting, HDL measurement can predict cardiovascular disease risk factors. However, measuring the five different sizes of HDLs over time is difficult because they comprise different apolipoproteins, each showing multiple genetic polymorphisms. The standard approach is to infer concentration by measuring cholesterol content in conjunction with ApoA-1, the most common constituent of HDL.
In the workflow proposed by Singh et al., trideuterated leucine (D3-Leu) administered in vivo is assayed by gas chromatography-mass spectrometry (GC-MS) to generate tracer dilution curves that run in conjunction with HRAM proteomics analysis. For the HRAM MS-proteomics workflow, the team used SIL FlexQuant-generated internal standards for the various apolipoproteins to create a parallel reaction monitoring (PRM) assay. By combining the GC-MS tracer studies data and the proteomic identification by HRAM-PRM, the researchers were able to construct compartmental modeling for the different HDL components.
The team recruited three obese individuals (two female, one male; body mass indices of 26, 31 and 31) who presented with low HDL cholesterol concentrations. None of the individuals was taking lipid-altering medications at the time of the study. The participants received a test diet high in unsaturated fat, which they followed for 32 days. On day 28 of the diet, the test subjects received an intravenous infusion of D3-Leu followed by blood collection at 0, 0.5, 1, 1.5, 2, 3 and 4 hours and then every two hours thereafter until 18 hours post infusion. The team collected additional samples at 22, 46, 70 and 94 hours.
Using cation exchange resin, the scientists isolated the D3-Leu tracer from the plasma samples, then measured dilution kinetics by GC-MS. They also purified HDL from plasma samples with immunoenrichment to collect bound and free ApoA-1. Following native gel-polyacrylamide gel electrophoresis to separate fractions, the team performed in-gel trypsin digestion before mass spectrometric evaluation on a Q Exactive mass spectrometer coupled with an Easy-nLC 1000 liquid chromatograph (both Thermo Scientific). Singh et al. analyzed the fractions in direct data acquisition mode, quantitating peptide fragments through PRM with FLEXQuant-constructed SIL apolipoproteins as internal standards.
The researchers identified 58 proteins in common from the three test subjects, with ApoA-1 appearing in all sizes of HDL. The HDL proteomes segregated into five major subproteomes according to apolipoprotein content and size. Apolipoprotein enrichment curves varied across all time points as measured by GC-MS, with ApoA-1 and ApoA-11 peaking at 8–12 hours post infusion, and ApoE peaking at 2–4 hours. The researchers were able to follow individual apolipoprotein kinetics to follow removal or shift into other HDLs over time.
In conclusion, Singh et al. consider that the HRAM-PRM workflow described is sufficient to allow simultaneous monitoring for many different apolipoproteins and development of compartmental modeling that shows movement across all HDL sizes. They are confident that HRAM-PRM will enable studies on the metabolism of individual HDL apolipoproteins with specific reference to research into disease and disease risk.
Reference
1. Singh, S.A., et al. (2016) “Multiple apolipoprotein kinetics measured in human HDL by high resolution/accurate mass parallel reaction monitoring,” Journal of Lipid Research, 57 (pp.714–728). doi: 10.1194/jlr.D061432.
Post Author: Amanda Maxwell. Mixed media artist; blogger and social media communicator; clinical scientist and writer. A digital space explorer, engaging readers by translating complex theories and subjects creatively into everyday language.
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