Network-Based Analysis on Orthogonal Separation of Human Plasma Uncovers Distinct High Density Lipoprotein Complexes

Abstract

High density lipoprotein (HDL) particles are blood-borne complexes whose plasma levels have been associated with protection from cardiovascular disease (CVD). Recent studies have demonstrated the existence of distinct HDL subspecies; however, these have been difficult to isolate and characterize biochemically. Here, we present the first report that employs a network-based approach to systematically infer HDL subspecies. Healthy human plasma was separated into 58 fractions using our previously published three orthogonal chromatography techniques. Similar local migration patterns among HDL proteins were captured with a novel similarity score, and individual comigration networks were constructed for each fraction. By employing a graph mining algorithm, we identified 183 overlapped cliques, among which 38 were further selected as candidate HDL subparticles. Each of these 38 subparticles had at least two literature supports. In addition, GO function enrichment analysis showed that they were enriched with fundamental biological and CVD protective functions. Furthermore, gene knockout experiments in mouse model supported the validity of these subparticles related to three apolipoproteins. Finally, analysis of an apoA-I deficient human patient's plasma provided additional support for apoA-I related complexes. Further biochemical characterization of these putative subspecies may facilitate the mechanistic research of CVD and guide targeted therapeutics aimed at its mitigation.

Keywords: apolipoprotein; comigration pattern; high-density lipoprotein; human plasma; maximal clique; particle fractionation; protein network; proteomics; subspecies.

Figure 1.

Schematic diagram of the systematic approach to identify HDL complexes based on three orthogonal chromatographic separation techniques. Healthy human plasmas were separated by three separation methods into an array of fractions. For each fraction, MS analysis was performed to discover the identities and spectral counts of proteins. Based on abundance profiles, individual co-migration networks were constructed with our Local S-score. A graph mining algorithm was applied to discover protein complexes. After filtering with molecular weight threshold, multiple validation approaches were utilized to test the putative HDL subspecies.

Figure 2.

Abundance profile examples of multiple HDL-associated proteins and number of proteins detected by MS within each fraction from the separation method (a-b) GF, (b-d) AE, and (e-f) IEF.

Figure 3.

The rationale of Local S-score for capturing the local similarity of proteins’ migration patterns. (a) The mechanism of our separation methods is utilizing different characteristics of each subspecies to create their specific distributions, i.e., migration patterns. For example, in the GF approach, larger size subparticles are likely to migrate fast and fall into early fractions, while smaller size ones would be detected in the following fractions. (b) Successive neighboring fractions may contain the same type of particles. The same subparticles may distribute among multiple neighboring fractions in a specific pattern, e.g., one subparticle containing proteins A, B, and C may distribute as shown in the figure. (c) Those proteins within the same subspecies are likely to have highly similar migration patterns locally. This local similarity may be only reflected within a certain range of fractions, i.e., sliding window.

Figure 4.

A local co-migration network constructed for 19^th fraction of GF method. Size of the vertex reflects network degree of this vertex. Circled subnet is a maximal clique within this network, corresponding to the well-known TLF particle.

Figure 5.

ROC curves for the traditional score and our Local S-score with various sliding windows w.

Figure 6.

Histogram of the most significantly enriched functions of 38 identified HDL subspecies. Distribution of the functions covers both fundamental biological functions and CVD-protective functions.