Reproducible Determination of High-Density Lipoprotein Proteotypes

Relationships between HDL subpopulation proteome and HDL function in overweight/obese people with and without coronary heart disease

BACKGROUND AND AIMS

The structure-function relationships of high-density lipoprotein (HDL) subpopulations are not well understood. Our aim was to examine the interrelationships between HDL particle proteome and HDL functionality in subjects with and without coronary heart disease (CHD).

METHODS

We isolated 5 different HDL subpopulations based on charge, size, and apolipoprotein A1 (APOA1) content from the plasma of 33 overweight/obese CHD patients and 33 age-and body mass index (BMI)-matched CHD-free subjects. We measured the relative molar concentration of HDL-associated proteins by liquid chromatography tandem mass spectrometry (LC-MS/MS) and assessed particle functionality.

RESULTS

We quantified 110 proteins associated with the 5 APOA1-containing HDL subpopulations. The relative molar concentration of these proteins spanned five orders of magnitude. Only 10 proteins were present in >1% while 73 were present in <0.1% concentration. Only 6 of the 10 most abundant proteins were apolipoproteins. Interestingly, the largest (α-1) and the smallest (preβ-1) HDL particles contained the most diverse proteomes. The protein composition of each HDL subpopulation was altered in CHD cases as compared to controls with the most prominent differences in preβ-1 and α-1 particles. APOA2 concentration was positively correlated with preβ-1 particle functionality (ABCA1-CEC/mg APOA1 in preβ-1) (R2 = 0.42, p = 0.005), while APOE concentration was inversely correlated with large-HDL particle functionality (SRBI-CEC/mg APOA1 in α-1+α-2) (R2 = 0.18, p = 0.01).

CONCLUSIONS

The protein composition of the different HDL subpopulations was altered differentially in CHD patients. The functionality of the small and large HDL particles correlated with the protein content of APOA2 and APOE, respectively. Our data indicate that distinct particle subspecies and specific particle associated proteins provide new information about the role of HDL in CHD.

Associations of an HDL apolipoproteomic index with cardiometabolic risk factors before and after exercise training in the HERITAGE Family Study

BACKGROUND AND AIMS

Previous studies have derived and validated an HDL apolipoproteomic score (pCAD) that predicts coronary artery disease (CAD) risk. However, the associations between pCAD and markers of cardiometabolic health in healthy adults are not known, nor are the effects of regular exercise on pCAD.

METHODS

A total of 641 physically inactive adults free of cardiovascular disease from the HERITAGE Family Study completed 20 weeks of exercise training. The pCAD index (range 0-100) was calculated using measurements of apolipoproteins A-I, C-I, C-II, C-III, and C-IV from ApoA-I-tagged serum (higher index = higher CAD risk). The associations between pCAD index and cardiometabolic traits at baseline and their training responses were assessed with Spearman correlation and general linear models. A Bonferroni correction of p < 8.9 × 10-04 was used to determine statistical significance.

RESULTS

The mean ± SD baseline pCAD index was 29 ± 32, with 106 (16.5 %) participants classified as high CAD risk. At baseline, pCAD index was positively associated with blood pressure, systemic inflammation, and body composition. HDL size, VO2max, and HDL-C were negatively associated with pCAD index at baseline. Of those classified as high CAD risk at baseline, 52 (49 %) were reclassified as normal risk after training. Following training, pCAD index changes were inversely correlated (p < 1.4 × 10-04) with changes in HDL-C, HDL size, and LDL size.

CONCLUSIONS

A higher pCAD index was associated with a worse cardiometabolic profile at baseline but improved with regular exercise. The results from this study highlight the potential role of HDL apolipoproteins as therapeutic targets for lifestyle interventions, particularly in high-risk individuals.

Simultaneous targeted and discovery-driven clinical proteotyping using hybrid-PRM/DIA

BACKGROUND

Clinical samples are irreplaceable, and their transformation into searchable and reusable digital biobanks is critical for conducting statistically empowered retrospective and integrative research studies. Currently, mainly data-independent acquisition strategies are employed to digitize clinical sample cohorts comprehensively. However, the sensitivity of DIA is limited, which is why selected marker candidates are often additionally measured targeted by parallel reaction monitoring.

METHODS

Here, we applied the recently co-developed hybrid-PRM/DIA technology as a new intelligent data acquisition strategy that allows for the comprehensive digitization of rare clinical samples at the proteotype level. Hybrid-PRM/DIA enables enhanced measurement sensitivity for a specific set of analytes of current clinical interest by the intelligent triggering of multiplexed parallel reaction monitoring (MSxPRM) in combination with the discovery-driven digitization of the clinical biospecimen using DIA. Heavy-labeled reference peptides were utilized as triggers for MSxPRM and monitoring of endogenous peptides.

RESULTS

We first evaluated hybrid-PRM/DIA in a clinical context on a pool of 185 selected proteotypic peptides for tumor-associated antigens derived from 64 annotated human protein groups. We demonstrated improved reproducibility and sensitivity for the detection of endogenous peptides, even at lower concentrations near the detection limit. Up to 179 MSxPRM scans were shown not to affect the overall DIA performance. Next, we applied hybrid-PRM/DIA for the integrated digitization of biobanked melanoma samples using a set of 30 AQUA peptides against 28 biomarker candidates with relevance in molecular tumor board evaluations of melanoma patients. Within the DIA-detected approximately 6500 protein groups, the selected marker candidates such as UFO, CDK4, NF1, and PMEL could be monitored consistently and quantitatively using MSxPRM scans, providing additional confidence for supporting future clinical decision-making.

CONCLUSIONS

Combining PRM and DIA measurements provides a new strategy for the sensitive and reproducible detection of protein markers from patients currently being discussed in molecular tumor boards in combination with the opportunity to discover new biomarker candidates.

Mapping the dynamic high-density lipoprotein synapse

BACKGROUND AND AIMS

Heterogeneous high-density lipoprotein (HDL) particles, which can contain hundreds of proteins, affect human health and disease through dynamic molecular interactions with cell surface proteins. How HDL mediates its long-range signaling functions and interactions with various cell types is largely unknown. Due to the complexity of HDL, we hypothesize that multiple receptors engage with HDL particles resulting in condition-dependent receptor-HDL interaction clusters at the cell surface.

METHODS

Here we used the mass spectrometry-based and light-controlled proximity labeling strategy LUX-MS in a discovery-driven manner to decode HDL-receptor interactions.

RESULTS

Surfaceome nanoscale organization analysis of hepatocytes and endothelial cells using LUX-MS revealed that the previously known HDL-binding protein scavenger receptor B1 (SCRB1) is embedded in a cell surface protein community, which we term HDL synapse. Modulating the endothelial HDL synapse, composed of 60 proteins, by silencing individual members, showed that the HDL synapse can be assembled in the absence of SCRB1 and that the members are interlinked. The aminopeptidase N (AMPN) (also known as CD13) was identified as an HDL synapse member that directly influences HDL uptake into the primary human aortic endothelial cells (HAECs).

CONCLUSIONS

Our data indicate that preformed cell surface residing protein complexes modulate HDL function and suggest new theragnostic opportunities.

Differences in HDL-related mortality risk between individuals with and without hypertension: a prospective cohort study in UK Biobank

BACKGROUND AND AIMS

Very high levels of high-density lipoprotein cholesterol (HDL-C) have been paradoxically linked to increased mortality risk. The present study aimed to examine associations of HDL-C and varied sizes of the HDL particle (HDL-P) with mortality risk stratified by hypertension.

METHODS AND RESULTS

This prospective cohort study included 429 792 participants (244 866 with hypertension and 184 926 without hypertension) from the UK Biobank. During a median follow-up of 12.7 years, 23 993 (9.8%) and 8142 (4.4%) deaths occurred among individuals with and without hypertension, respectively. A U-shaped association of HDL-C with all-cause mortality was observed in individuals with hypertension after multivariable adjustment, whereas an L-shape was observed in individuals without hypertension. Compared with individuals with normal HDL-C of 50-70 mg/dL, those with very high HDL-C levels (>90 mg/dL) had a significantly higher risk of all-cause mortality among individuals with hypertension (hazard ratio, 1.47; 95% confidence interval, 1.35-1.61), but not among those without hypertension (1.05, 0.91-1.22). As for HDL-P, among individuals with hypertension, a larger size of HDL-P was positively whereas smaller HDL-P was negatively associated with all-cause mortality. After additional adjustment for larger HDL-P in the model, the U-shaped association between HDL-C and mortality risk was altered to an L-shape among individuals with hypertension.

CONCLUSIONS

The increased risk of mortality associated with very high HDL-C existed only in individuals with hypertension, but not in those without hypertension. Moreover, the increased risk at high HDL-C levels in hypertension was likely driven by larger HDL-P.

Proteomic and functional analysis of HDL subclasses in humans and rats: a proof-of-concept study

BACKGROUND

The previous study investigated whether the functions of small, medium, and large high density lipoprotein (S/M/L-HDL) are correlated with protein changes in mice. Herein, the proteomic and functional analyses of high density lipoprotein (HDL) subclasses were performed in humans and rats.

METHODS

After purifying S/M/L-HDL subclasses from healthy humans (n = 6) and rats (n = 3) using fast protein liquid chromatography (FPLC) with calcium silica hydrate (CSH) resin, the proteomic analysis by mass spectrometry was conducted, as well as the capacities of cholesterol efflux and antioxidation was measured.

RESULTS

Of the 120 and 106 HDL proteins identified, 85 and 68 proteins were significantly changed in concentration among the S/M/L-HDL subclasses in humans and rats, respectively. Interestingly, it was found that the relatively abundant proteins in the small HDL (S-HDL) and large HDL (L-HDL) subclasses did not overlap, both in humans and in rats. Next, by searching for the biological functions of the relatively abundant proteins in the HDL subclasses via Gene Ontology, it was displayed that the relatively abundant proteins involved in lipid metabolism and antioxidation were enriched more in the medium HDL (M-HDL) subclass than in the S/L-HDL subclasses in humans, whereas in rats, the relatively abundant proteins associated with lipid metabolism and anti-oxidation were enriched in M/L-HDL and S/M-HDL, respectively. Finally, it was confirmed that M-HDL and L-HDL had the highest cholesterol efflux capacity among the three HDL subclasses in humans and rats, respectively; moreover, M-HDL exhibited higher antioxidative capacity than S-HDL in both humans and rats.

CONCLUSIONS

The S-HDL and L-HDL subclasses are likely to have different proteomic components during HDL maturation, and results from the proteomics-based comparison of the HDL subclasses may explain the associated differences in function.

Strategies for consistent and automated quantification of HDL proteome using data-independent acquisition (DIA)

The introduction of mass spectrometry-based proteomics has revolutionized HDL field, with the description, characterization and implication of HDL-associated proteins in an array of pathologies. However, acquiring robust, reproducible data is still a challenge in the quantitative assessment of HDL proteome. Data-independent acquisition (DIA) is a mass spectrometry methodology that allows the acquisition of reproducible data, but data analysis remains a challenge in the field. Up to date, there is no consensus in how to process DIA-derived data for HDL proteomics. Here, we developed a pipeline aiming to standardize HDL proteome quantification. We optimized instrument parameters, and compared the performance of four freely available, user-friendly software tools (DIA-NN, EncyclopeDIA, MaxDIA and Skyline) in processing DIA data. Importantly, pooled samples were used as quality controls throughout our experimental setup. A carefully evaluation of precision, linearity, and detection limits, first using E. coli background for HDL proteomics, and second using HDL proteome and synthetic peptides, was undertaken. Finally, as a proof of concept, we employed our optimized and automated pipeline to quantify the proteome of HDL and apolipoprotein B (APOB)-containing lipoproteins. Our results show that determination of precision is key to confidently and consistently quantify HDL proteins. Taking this precaution, any of the available software tested here would be appropriate for quantification of HDL proteome, although their performance varied considerably.

High-density lipoprotein revisited: biological functions and clinical relevance

Previous interest in high-density lipoproteins (HDLs) focused on their possible protective role in atherosclerotic cardiovascular disease (ASCVD). Evidence from genetic studies and randomized trials, however, questioned that the inverse association of HDL-cholesterol (HDL-C) is causal. This review aims to provide an update on the role of HDL in health and disease, also beyond ASCVD. Through evolution from invertebrates, HDLs are the principal lipoproteins, while apolipoprotein B-containing lipoproteins first developed in vertebrates. HDLs transport cholesterol and other lipids between different cells like a reusable ferry, but serve many other functions including communication with cells and the inactivation of biohazards like bacterial lipopolysaccharides. These functions are exerted by entire HDL particles or distinct proteins or lipids carried by HDL rather than by its cholesterol cargo measured as HDL-C. Neither does HDL-C measurement reflect the efficiency of reverse cholesterol transport. Recent studies indicate that functional measures of HDL, notably cholesterol efflux capacity, numbers of HDL particles, or distinct HDL proteins are better predictors of ASCVD events than HDL-C. Low HDL-C levels are related observationally, but also genetically, to increased risks of infectious diseases, death during sepsis, diabetes mellitus, and chronic kidney disease. Additional, but only observational, data indicate associations of low HDL-C with various autoimmune diseases, and cancers, as well as all-cause mortality. Conversely, extremely high HDL-C levels are associated with an increased risk of age-related macular degeneration (also genetically), infectious disease, and all-cause mortality. HDL encompasses dynamic multimolecular and multifunctional lipoproteins that likely emerged during evolution to serve several physiological roles and prevent or heal pathologies beyond ASCVD. For any clinical exploitation of HDL, the indirect marker HDL-C must be replaced by direct biomarkers reflecting the causal role of HDL in the respective disease.

Decoding Functional High-Density Lipoprotein Particle Surfaceome Interactions

High-density lipoprotein (HDL) is a mixture of complex particles mediating reverse cholesterol transport (RCT) and several cytoprotective activities. Despite its relevance for human health, many aspects of HDL-mediated lipid trafficking and cellular signaling remain elusive at the molecular level. During HDL’s journey throughout the body, its functions are mediated through interactions with cell surface receptors on different cell types. To characterize and better understand the functional interplay between HDL particles and tissue, we analyzed the surfaceome-residing receptor neighborhoods with which HDL potentially interacts. We applied a combination of chemoproteomic technologies including automated cell surface capturing (auto-CSC) and HATRIC-based ligand–receptor capturing (HATRIC-LRC) on four different cellular model systems mimicking tissues relevant for RCT. The surfaceome analysis of EA.hy926, HEPG2, foam cells, and human aortic endothelial cells (HAECs) revealed the main currently known HDL receptor scavenger receptor B1 (SCRB1), as well as 155 shared cell surface receptors representing potential HDL interaction candidates. Since vascular endothelial growth factor A (VEGF-A) was recently found as a regulatory factor of transendothelial transport of HDL, we next analyzed the VEGF-modulated surfaceome of HAEC using the auto-CSC technology. VEGF-A treatment led to the remodeling of the surfaceome of HAEC cells, including the previously reported higher surfaceome abundance of SCRB1. In total, 165 additional receptors were found on HAEC upon VEGF-A treatment representing SCRB1 co-regulated receptors potentially involved in HDL function. Using the HATRIC-LRC technology on human endothelial cells, we specifically aimed for the identification of other bona fide (co-)receptors of HDL beyond SCRB1. HATRIC-LRC enabled, next to SCRB1, the identification of the receptor tyrosine-protein kinase Mer (MERTK). Through RNA interference, we revealed its contribution to endothelial HDL binding and uptake. Furthermore, subsequent proximity ligation assays (PLAs) demonstrated the spatial vicinity of MERTK and SCRB1 on the endothelial cell surface. The data shown provide direct evidence for a complex and dynamic HDL receptome and that receptor nanoscale organization may influence binding and uptake of HDL.

The Role of High-Density Lipoprotein Cholesterol in 2022

PURPOSE OF THE REVIEW

High-density lipoproteins (HDL) are responsible for the transport in plasma of a large fraction of circulating lipids, in part from tissue mobilization. The evaluation of HDL-associated cholesterol (HDL-C) has provided a standard method for assessing cardiovascular (CV) risk, as supported by many contributions on the mechanism of this arterial benefit. The present review article will attempt to investigate novel findings on the role and mechanism of HDL in CV risk determination.

RECENT FINDINGS

The most recent research has been aimed to the understanding of how a raised functional capacity of HDL, rather than elevated levels per se, may be responsible for the postulated CV protection. Markedly elevated HDL-C levels appear instead to be associated to a raised coronary risk, indicative of a U-shaped relationship. While HDL-C reduction is definitely related to a raised CV risk, HDL-C elevations may be linked to non-vascular diseases, such as age-related macular disease. The description of anti-inflammatory, anti-oxidative and anti-infectious properties has indicated potential newer areas for diagnostic and therapeutic approaches. In the last two decades inconclusive data have arisen from clinical trials attempting to increase HDL-C pharmacologically or by way of recombinant protein infusions (most frequently with the mutant A-I Milano); prevention of stent occlusion or heart failure treatment have shown instead significant promise. Targeted clinical studies are still ongoing.

A peptidoform based proteomic strategy for studying functions of post-translational modifications

Protein post-translational modifications (PTMs) generate an enormous, but as yet undetermined, expansion of the produced proteoforms. In this Viewpoint, we firstly reviewed the concepts of proteoform and peptidoform. We show that many of the current PTM biological investigation and annotation studies largely follow a PTM site-specific rather than proteoform-specific approach. We further illustrate a potentially useful matching strategy in which a particular "modified peptidoform" is matched to the corresponding "unmodified peptidoform" as a reference for the quantitative analysis between samples and conditions. We suggest this strategy has the potential to provide more directly relevant information to learn the PTM site-specific biological functions. Accordingly, we advocate for the wider use of the nomenclature "peptidoform" in future bottom-up proteomic studies.