Nutritional lipidomics: molecular metabolism, analytics, and diagnostics

Abstract

The field of lipidomics is providing nutritional science a more comprehensive view of lipid intermediates. Lipidomics research takes advantage of the increase in accuracy and sensitivity of mass detection of MS with new bioinformatics toolsets to characterize the structures and abundances of complex lipids. Yet, translating lipidomics to practice via nutritional interventions is still in its infancy. No single instrumentation platform is able to solve the varying analytical challenges of the different molecular lipid species. Biochemical pathways of lipid metabolism remain incomplete and the tools to map lipid compositional data to pathways are still being assembled. Biology itself is dauntingly complex and simply separating biological structures remains a key challenge to lipidomics. Nonetheless, the strategy of combining tandem analytical methods to perform the sensitive, high-throughput, quantitative, and comprehensive analysis of lipid metabolites of very large numbers of molecules is poised to drive the field forward rapidly. Among the next steps for nutrition to understand the changes in structures, compositions, and function of lipid biomolecules in response to diet is to describe their distribution within discrete functional compartments lipoproteins. Additionally, lipidomics must tackle the task of assigning the functions of lipids as signaling molecules, nutrient sensors, and intermediates of metabolic pathways.

Keywords: Bile acids; Lipids; Mass spectrometry; Nutrition; Oxylipins.

Figure 1

Targeted lipidomics using ultrahigh-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) and multivariate analysis. Selection of the target fatty acid and oxylipin metabolites for (A) LC separation with the appropriate mobile and stationary phase. Tandem MS and multiple reaction monitoring (MRM) provide molecular ions and daughter ion fragments (B) as well as specific and selective transitions (C) essential to identify and quantify the compounds, including those with co-eluting peaks. Metabolite levels are used in (D) data processing with principal component analysis and other multivariate methods (plot from Zivkovic et al., 2012) [99].

Figure 2

Lipid homeostasis regulation by nuclear receptors. (A) Fatty acid and triglyceride homeostasis. (B) Cholesterol and bile acid homeostasis. The arrows represent the lipid biosynthesis, metabolism, or transport pathways are positive regulated by the indicated nuclear receptors. The “T” lines represent inhibition. Pregnane X receptor (PXR), farnesoid x receptor (FXR), liver X receptor (LXR), and peroxisome proliferator-activated receptors (PPAR).

Figure 2

Lipid homeostasis regulation by nuclear receptors. (A) Fatty acid and triglyceride homeostasis. (B) Cholesterol and bile acid homeostasis. The arrows represent the lipid biosynthesis, metabolism, or transport pathways are positive regulated by the indicated nuclear receptors. The “T” lines represent inhibition. Pregnane X receptor (PXR), farnesoid x receptor (FXR), liver X receptor (LXR), and peroxisome proliferator-activated receptors (PPAR).

Figure 3

Reprinted from Metabolomics, Volume 8, Issue 6, 2012, 1102–1113, Angela M. Zivkovic, Jun Yang, Katrin Georgi, Christine Hegedus, Malin L. Nording, Aifric O’Sullivan, J. Bruce German, Ronald J. Hogg, Robert H. Weiss, Curt Bay, Bruce D. Hammock, Figure 1, with kind permission from Springer Science and Business Media. Fatty acid precursors and their oxylipin products.The fatty acids linoleic acid (LA; 18:2n6), α-linolenic acid (ALA, 18:3n3), arachidonic acid (ARA, 20:4n6), dihommo-γ-linolenic acid (DGLA; 20:3n6), eicosatrienoic acid (ETA; 20:3n9), eicosapentaenoic acid (EPA; 20:5n3), and docosahexaenoic acid (DHA; 22:6n3) are precursors to a number of oxylipin products produced via the cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P 450 (CYP) enzymes. The oxylipin products of the COX pathway include prostaglandins (PGE1, PGD1, PGH2, PGF2α. PGE2, PGB2, PGD2, PGJ2, 15-deoxy-PGJ2, PGI2, 6-keto-PGF1α, PGE3, PGH3, and resolvin E1) and thromboxanes (TXA2, TXB2). The oxylipin products of the LOX pathway include hydroperoxyeicosatetraenoic acids (HpETEs) and dihydroxyeicosatetraenoic acid (DiHETE), (further converted to hydroxyeicosatetraenoic acids (HETEs)), hydroxyoctadecadienoic acids (HOTrEs), hydroxyeicosaptenaenoic acids (HEPEs), hydroxydocosahexaenoic acid (17-HDoHE), and leukotrienes (LTA4, LTB4, 20-OH-LTB4, 20-COOH-LTB4, 6-trans-LTB4, LTC4, LTD4, LTE4, LTB3, LTB5) as well as the hydroxyoctadienoic acids (HODEs), and trihydroxyoctamonoenoic acids (TriHOMEs). The products of the CYP hydroxy (OH) pathway include 20-HETE, and the products of the CYP epoxy pathway include the epoxyeicosatrienoic acids (EETs), epoxyoctadecadienoic acids (EpODEs), epoxyoctamonoenoic acids (EpOMEs), epoxyeicosatetreaenoic acids (EpETEs), and epoxydocosapentaenoic acids (EpDPEs), as well as the downstream soluble epoxide hydrolase (sEH) metabolites dihydroxyoctamonoenoic acids (DiHOMEs), dihydroxyeicosatrienoic acids (DiHETrEs), dihydroxyoctadecadienoic acids (DiHODEs), dihydroxyeicosatetraenoic acids (DiHETEs), and dihydroxydocosapentaenoic acids (DiHDPEs). Each fatty acid precursor and its oxylipin products are colored the same: LA, orange; DGLA, yellow; ETA, dark blue; ALA, purple; EPA, green; DHA, red; and ARA, light blue.

Figure 4

Principal component analysis (PCA) is an unsupervised multivariate projection method for pattern recognition in multidimensional data tables. To illustrate the concept of PCA, the variables (x₁, x₂, x₃) describing the objects (black squares and gray circles) are reduced from a three-dimensional space to a two-dimensional plane by modeling the principal components (PC) in the direction of the largest variability. The resulting score plot (to the right) facilitates the interpretation of clusters and outliers among the objects. This concept might be extended to reducing unlimited number of variables to typically one to three principal components in order to summarize the major portion of the variability.