Applying lipidomics strategies to study lipid metabolic diseases
Zhang, Wenxuan
DOI:
10.33612/diss.169407826
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Publication date: 2021
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Zhang, W. (2021). Applying lipidomics strategies to study lipid metabolic diseases. University of Groningen. https://doi.org/10.33612/diss.169407826
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Chapter 1
General introduction and outline
of the thesis
Lipids and lipid functions
The term ‘lipids’ refers to distinct groups of hydrophobic or amphipathic molecules that show a remarkably large structural and functional diversity. According to the LIPID MAPS comprehensive classification system, lipids are classified into 8 categories based on their chemical features1,2 (Figure 1). The
various lipid molecules play pivotal biological roles in energy metabolism, membrane composition and cellular signaling (Figure 2).
Figure 1. Eight defined lipid categories according to the LIPID MAPS classification system.
The structures of representative molecules are presented for each category. Adopted with permission from Fahy, Eoin, et al. “Lipid classification, structures and tools.” Biochimica et
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Triglycerides (TGs), that belong to the category glycerolipids and are generally referred to as “fat”, are key molecules for energy storage in the body. TGs can be stored as lipid droplets within cells such as adipocytes and can be efficiently disassembled to generate mono- and diacylglycerides, glycerol and free fatty acids and reconstructed in demand to the energy state of the body. Excessive accumulation of TGs in adipose tissues and in organs is associated with multiple obesity-related diseases such type 2 diabetes, non-alcoholic fatty liver disease and cardiovascular diseases3,4. Other lipids, like phospholipids, sphingolipids
and sterols, are major components of cell membranes and the membranous systems that separate different cellular compartments5. Their arrangement
determines membrane fluidity, surface charge and other physical properties that are closely related to the functions of organelles and cells 6. Some lipids,
such as polyunsaturated fatty acids and their derivatives, although in very low abundances, act as bioactive lipid mediators and participate in signal transduction pathways, e.g. during inflammation7,8.
Figure 2. General cellular functions of lipid categories. Figure is adopted with permission from
Wenk “Lipidomics: new tools and applications.” Cell 143.6 (2010): 888-895.
Lipid metabolism
In the small intestine, dietary TGs are digested into fatty acids and monoacylglycerides (MGs) by pancreatic lipases with the aid of bile acids and subsequently taken up by enterocytes9. Dietary phospholipids (PLs) are either
fatty acids and then taken up by enterocytes. The absorbed fatty acids, MGs and lysoPLs are reconstructed as TGs and PLs, respectively within the enterocyte. TGs and PLs10, together with other lipids and apolipoprotein B-48, assemble
to form nascent chylomicrons that are excreted into lymph. Once nascent chylomicrons reach the blood stream, they mature by accepting apolipoprotein C-II and apolipoprotein E11. TGs in chylomicrons are hydrolyzed into fatty acids
that can be utilized by peripheral tissues, mostly by adipose tissue, muscle, and heart12. Remnants of chylomicrons enriched with cholesteryl esters are
endocytosed by hepatocytes and stored within hepatocytes as TGs. The liver is capable of packing these lipids again into very low density lipoproteins (VLDL). These lipoproteins are the main form of lipid transportation in the blood stream to provide fatty acids and glycerol to other tissues during fasting, i.e., when no dietary lipids are available. VLDL particles, after removal of their TGs, are converted into low density lipoproteins (LDL). LDL is enriched with cholesterol and cholesterol esters, which are readily oxidized and then contribute to the development of atherosclerosis12,13.
Immediately after intake of fatty foods and their absorption from the intestine, fatty acids are released from lipoproteins by lipoprotein lipases. During the fasting state, fatty acids are released from VLDL particles by a similar process as well as from adipose tissues by the consecutive actions of adipose triglyceride lipase, hormone sensitive lipase and MG lipase14. When free fatty acids enter
the different cell types, they can either be stored again as TGs or decomposed by fatty acid oxidation processes to provide energy. Fatty acid oxidation occurs in mitochondria and peroxisomes. In mitochondria, the process consists of a cycle with 4 consecutive reactions catalyzed by a number of enzymes, i.e., flavin adenine dinucleotide (FAD)-linked dehydrogenases, hydratases, Nicotinamide adenine dinucleotide (NAD)-linked dehydrogenases and finally thiolases15. In
peroxisomes, fatty acid oxidation differs from mitochondrial β-oxidation by the fact that the first reaction uses a FAD-linked oxidase instead of a FAD-linked dehydrogenase16. Mitochondrial fatty acid oxidation takes long chain fatty acids
and breaks these gradually down into acetyl-CoA, while peroxisomal fatty acid oxidation starts with very long chain fatty acids and breaks these fatty acids only partly down to medium chain fatty acids and acetyl-CoA. Further oxidation of these medium-chain fatty acids occurs in mitochondria. The produced acetyl-CoA units, NADH and FADH2, are then used in the Tricarboxylic acid (TCA) -cycle and oxidative phosphorylation, respectively, to capture the chemical energy in the form of adenosine triphosphate (ATP). Cells not only take up fatty acids, they also synthesize them de novo starting from acetyl-CoA, catalyzed by a series of enzymes starting with acetyl-CoA carboxylase (ACC) and fatty acid
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synthase (FAS). Next, cells elongate and desaturate long chain fatty acids in processes that are catalyzed by the various elongation of very long chain fatty acids proteins (ELOVLs) and fatty acid desaturases (FADSs), respectively, to produce new fatty acids with different chain lengths and different degrees of saturation for further processing17,18.
Fatty acyl residues are the common structural apolar parts that are shared by complex lipids such as glycerolipids, PLs and sphingolipids. Simplified graphic lipid synthesis pathways are shown in figure 3. De novo synthesis of these complex lipids starts with the acylation of glycerol-3-phosphate (Gly3P) with consecutive acylation to form phosphatidic acid (PA). Depending on the type of dephosphorylation of PA, DG or cytidine diphosphate diacylglycerol (CDP-DG) are produced. DG can be acylated to TG or it can be partially hydrolyzed to MG. Reversely, TG can be hydrolyzed to DG and subsequent to MG. Moreover, MG can be acylated back to DG. This allows for a constant adaptation of the fatty acyl composition of TGs, DGs and MGs to the particular composition of the cellular fatty acyl-CoA pool19. DG and CDP-DG also participate in the Kennedy pathway
of de novo synthesis of PLs20. In this pathway DG is condensed with the ‘activated
headgroup’ CDP-Choline or CDP-Ethanolamine to form glycerophosphocholine (PC) or glycerophosphoethanolamine (PE), respectively. With the ‘activated tailgroup’, DG starts a second pathway of de novo synthesis of PLs. CDP-DG condenses with an extra Gly3P and a second CDP-CDP-DG to form cardiolipin (CL). CDP-DG condenses with inositol to form glycerophosphoinositol (PI) or with serine to form glycerophosphoserine (PS). De novo synthesis of ether lipids starts with the acylation of dihydroxyacetone phosphate (DHAP) and de novo synthesis of sphingolipids starts with acylation and decarboxylation of serine21,22. Besides de novo syntheses of PLs, remodeling of PLs is of major
importance. With regard to the remodeling of head groups, 3 reactions are involved, i.e., decarboxylation of PS to produce PE20, base exchange of PC and
PE with serine giving rise to PS and tri-methylation of PE to form PC. Remodeling of the fatty acyl-residues is another important feature of PL metabolism. Two processes are of importance, first, the so-called Lands cycle comprising of phospholipase A2 and lysoPL acyltransferase and second, fatty acyl exchange between PLs catalyzed by transacylases23.
Figure 3. Simplified complex lipid synthesis and remodeling. This figure is remodeled and
extended with permission from Shindou, Hideo, and Takao Shimizu. “Acyl-CoA: lysophospholipid acyltransferases.” Journal of Biological Chemistry 284.1 (2009): 1-5.
Lipid metabolism needs to be highly dynamic, because the lipid composition of subcellular membranes is organelle-specific and the distribution of lipids over membranes of these organelles is highly asymmetric24. This specificity
in lipid composition of organellar membranes is essential for their proper functioning25,26. Membrane trafficking by small intracellular vesicles during
exocytosis and endocytosis as well as the transfer or exchange of individual lipids at organelle contact sites or by proteins, however, tend to equilibrate the lipid composition of membranes of organelles and the plasma membrane27,28.
Asymmetry in lipid composition of membranes in cells has to be maintained actively in the face of these equilibrating mechanisms. Metabolic trapping is one of the strategies used by cells. This is accomplished by localization of specific enzymes involved in lipid metabolism to specific organelles such that a high metabolic rate at that particular site maintains the asymmetry. Finally, energy consuming ATP-dependent flippases and translocases drive the asymmetry in lipid composition between both leaflets of a membrane6. As a consequence, high
rates of synthesis, remodeling, exchange and degradation of lipids combined with highly dynamic membrane trafficking maintain homeostasis of the lipid pool in different membranes inside cells but also in different biological systems. The regulation of cellular lipid metabolism is clearly highly complex and not yet fully understood.
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Lipidome and lipidomics
All lipids present in a given biological system are known as the lipidome. Lipidome compositions in different cell types, tissues and living organisms can be very diverse29,30. A change in lipidome composition over time usually
indicates changes in dietary lipid intake or altered lipid metabolism. Therefore, detailed monitoring of changes in the lipidome itself can provide insights into genetically- or metabolically-determined alterations in lipid metabolism or link lifestyle, e.g., diet composition or exercise, to lipid phenotypes. To gain a systematic understanding of the lipidome, comprehensive lipidomic analytical methods such as high-throughput techniques to detect lipid molecules from different lipid categories were developed in recent years 31–33. Most lipidomic
methods are based on High Resolution Mass Spectrometry (HRMS), usually coupled with High Performance Liquid Chromatography (HPLC), to reach maximum capacities for lipid detection, identification and quantification34. In
single untargeted lipidomics analyses, hundreds to thousands of lipids can be separated and analyzed, covering all major lipid categories35,36. Additionally,
targeted mass spectrometry-based methods were also developed for deep profiling of selected lipid classes/species for studies on specific lipid pathways or lipids with special functions37–40.
Lipidomics workflows
Similarly to other “omics” approaches, lipidomics workflows can be divided into three major sections, pre-analytics, analytics and post-analytics (Fig.4). To obtain reproducible and valid results from lipidomics analyses, special considerations should be taken for all aspects of the workflow.
Pre-analytics
Lipidomics is often applied to address biological questions related to lipid metabolism41. There are two types of studies that are relevant for this thesis.
One is hypothesis-driven, where clear experimental groups are defined as part of a study design that is based on a hypothesis. Other factors need to be strictly controlled to focus on differences between the experimental groups related to the working hypothesis. This type of studies requires a relatively smaller sample size due to the careful control of other factors. Prior knowledge of the background of the study can guide the analytical choice and statistical analysis. Another type of study referred to as a population study, to unveil the lipid diversities within a defined population. In this case, the aims are to explore lipid metabolism42, to set reference values for a population in order to assign lipids as
biomarker for diagnostic purposes43 or to find correlations between lipids and
sample size and standardized sample handling procedures for all samples in order to acquire reliable data acquisition and data analysis result. Lipidomics population studies are often combined with other omics analyses to build a comprehensive understanding of the population, which requires more careful planning for different layers of the omics analyses.
Analytics
Sample preparation is of crucial importance for meaningful lipidomics analysis. Different bio-materials have distinct lipidome compositions. Tissues such as liver and adipose tissue contain large proportions of glycerolipids and cholesterol while cultured cells such as fibroblast and HeLa cells are mainly composed of PLs and lysoPLs45–47. Lipid profiles of bio-fluids can vary significantly depending
on the prevailing physiological state. Major considerations when selecting lipid extraction methods are sample type and the lipids of interest. Most lipid extraction methods are based on liquid-liquid extraction (two-phase extraction methods) or on protein precipitation (one phase extraction methods)48–53. For
two phase extraction methods, lipids are enriched in the organic phase, while other components remain in the aqueous phase or as insoluble debris that is removed by centrifugation. The choice of organic solvents and their proportions determine the extraction efficiency for different lipids. Samples containing a high percentage of neutral lipids (e.g., TGs and cholesterol esters) require solvents which are highly apolar for efficient extraction, while samples with mostly PLs and lysoPLs call for a mildly polar solvent system to extract lipids with an appreciable portion remaining in the aqueous phase. One phase extraction methods usually use water-soluble organic solvents to precipitate proteins while retaining lipids in the organic phase54,55. These methods are preferred
when the sample mainly contains lipids with an appreciable partitioning into water. Those considerations must be taken into account before deciding on the most appropriate lipid extraction method.
The choice of the mass-spectrometry-based analytical method determines the depth and the breadth of lipid profiling. Although plenty of LC-MS-based methods and direct infusion-based methods exist, there is still no single method that covers the entire extracted lipidome due to its diverse chemical characteristics and the wide concentration range of lipids within categories and between categories. We can still choose between detecting the major common lipid species presented in samples (untargeted lipidomics) or detecting specific low-abundance lipid classes or single lipid species such as sphingomyelins, ceramides, fatty acids (semi-targeted and targeted lipidomics). It is also possible to setup methods to detect lipids which share common chemical
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characteristics (e.g., PLs with a choline head group56) based on the featured
MS/MS fragments during analysis.
After data acquisition, data preprocessing procedures are performed to identify lipid species and quantify their concentrations or relative intensities based on the acquired raw data. The major steps include peak picking, retention time alignment, deconvolution, lipid database matching, lipid annotation, normalization and ultimately (relative) quantification. Different algorithms and software packages have been developed and are updated rapidly during recent years, from a few limited options to personalized strategies, which are suitable for different types of lipidomics workflows 57–61.
Figure 4. General lipidomics workflow. Major steps and considerations are listed for each
section of the workflow. The figure has been modified with permission from Burla, Bo, et al. “MS-based lipidomics of human blood plasma: a community-initiated position paper to develop accepted guidelines.” Journal of Lipid Research 59.10 (2018): 2001-201762.
Post-Analytics
Like for other “omics” approaches, extracting useful biological information from hundreds to thousands of lipid signals is still a big challenge. Lipidomics data analysis is often performed with a combination of unsupervised and supervised multivariate statistical analysis, clustering-based analysis and hypothesis-testing analysis63. Principal component analysis (PCA) is the most
widely used explorative and unbiased multivariate method, which provides an overview of the major variations between the measured samples. It reduces the number of dimensions, originally defined by the number of measured lipids, by projecting samples onto new dimensions with the coordinates named principal components64. In an unsupervised manner, this approach visualizes
principle components. Clustering-based strategies, such as hierarchical clustering, enable sample ordering and lipid ordering based on the patterns of the measured lipid profiles. The changes in lipids and samples are often visualized in a heatmap. For hypothesis driven studies, discriminative analyses such as partial least squares discriminant analysis (PLS-DA) and random forest, are used to search for lipids that have discriminating power for the separation between the experimental groups. Hypothesis testing analyses such as Student’s t-test and ANOVA are used in combination with multivariate tools for capturing significantly changed lipids between groups. Besides, emerging bioinformatics tools for lipidomic enrichment analysis have the potential to identify common structural and functional patterns of lipids selected from discriminating analyses65,66. Collectively, lipids may be organized and visualized
in different ways to help in deriving biological information from the analyzed data. Besides lipidomics data, information collected at different molecular layers (e.g. genes, mRNAs, proteins) may be integrated to better explain and explore the changes in lipid metabolism mechanistically.
Lipidomics and multi-omics
Better characterization of lipid metabolism can possibly be achieved by the incorporation of multiple molecular layers of information. The development of techniques for this purpose now offers the opportunity to quantify different molecular profiles from genes to proteins to metabolites (Figure 5). Lipidomics is currently considered as a new branch of trans-omics integrative networks. For instance, by performing genome-wide association analysis of lipid species from plasma samples, new associations were identified between lipid species-associated loci with obesity, thrombophlebitis and gallbladder disease in a population study 67. A combined lipidomics - genomics approach has the potential
to be used for predicting new genetic risk factors and thus to benefit molecular clinical diagnostics in the future. Another exemplary study made use of genes, proteins and lipids measured in 107 different mouse strains and identified novel mechanisms of lipid metabolism regulation 68. Among hypothesis-driven
studies, one of the earliest studies combined lipidomics data with published genomics and proteomics data to study host lipid metabolic adaptations during influenza virus replication. This led to the defininition of ether-linked PCs as a unique, pathogenicity-dependent signature for influenza69. More studies
have integrated lipidomics with other omics layers to better interpret altered lipid metabolism under disease state conditions70–72. With the development
of new bioinformatics solutions for multi-omics data integration, more of the underlying, biologically-interesting messages will be extracted from the vast amount of omics data 73,74.
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Figure 5. Trans-omics landscape. Development of techniques that allow detection of different
levels of molecular profiles. The molecular profile of each omics layer reflects the regulation from other layers. Thus multi-omics data integration furthers our understanding from a single layer to a system-wide overview of metabolic pictures. Adopted from Yugi, Katsuyuki, et al. “Trans-omics: how to reconstruct biochemical networks across multiple ‘omic’ layers.” Trends
in Biotechnology 34.4 (2016): 276-290.
The study of metabolic diseases with lipidomics
One of the hot areas of lipidomics applications are inborn and acquired metabolic diseases, in which altered lipid profiles are often observed with progression of the disease75. Lipid fingerprinting is applied to evaluate changes in lipid profiles
and to discover lipid signatures that discriminate between the healthy situation and the various disease states. Enzymatic defects of lipid metabolism are commonly caused by dysfunction of specific enzymes involved in fatty acid beta-oxidation, lipid biosynthesis and lipid remodeling76,77. Patients show symptoms
as a consequence of abnormal levels or of deficiencies of certain lipids or lipid metabolites in cells, organs and biofluids. Capturing the imbalanced state of a lipidome may offer a better understanding of the pathogenesis of the disease as well as distinguishing the potential subtypes of the disease. Lipidomics analysis applied to fibroblasts from patients with peroxisomal disorders shows an altered phospholipid composition78. The levels of certain lipid species were
changed in four different types of peroxisomal enzyme deficiencies79. In another
study of mitochondrial disorders caused by leucine rich pentatricopeptide repeat containing (LRPPRC), untargeted lipidomics revealed disturbed lipid homeostasis in patients and proposed plasma plasmalogen as a potential lipid biomarker 80. Broader lipidomics applications have been focused on the
links between lipids and complex metabolic syndromes in diseases such as dyslipidemia, obesity, insulin resistance, cancer and Alzheimer’s disease.
Several recent reviews summarized the role of lipid species in disease development or as potential biomarkers81,82.
Aims and Scope of this thesis
The overall aim of this thesis is to establish an untargeted lipidomics workflow for different biological applications and to apply this workflow, together with different statistical strategies, to elucidate the relationships between lipid profiles and metabolic disorders. In Part I of the thesis, I described the possible considerations of the pre-analytical stage of the lipidomics workflow. Then I compared and evaluated different lipid extraction methods using our untargeted lipidomics workflow. In Part II of the thesis, I applied lipidomics in three different disease-related topics, to prove the value of lipidomics as a useful tool for biological mechanistic studies and clinical studies. In Part III, I briefly summarized the results and discussed the future perspectives and challenges of lipidomics techniques in clinical and biological applications .
Part I: Evaluation of lipid extraction systems
Chapter 2 summarizes the possible challenges and pitfalls of the pre-analytical stage of an untargeted lipidomics workflow. I focused on lipid stability issues, sample handling and storage in different study designs and on the pros & cons of different lipid extraction methods. The aim is to provide the reader with background information to approach those challenges in a systematic manner. Chapter 3 describes the evaluation of four different, commonly used lipid extraction procedures by comparing a one-phase extraction method and three two-phase extraction methods on human plasma samples. Strengths and weaknesses of each extraction method with respect to extraction efficiency for different types of lipid molecules were elucidated. This work may serve as a guide for selecting a given lipid extraction method, which is one of the critical steps prior to LC-MS analysis.
Part II: Application of lipidomics to disease-related studies
In Chapter 4, I applied untargeted lipidomics on plasma samples from subjects
with extreme levels of LDL-cholesterol in combination with targeted genomics. We found compositional differences in the plasma lipidome between the subjects with extremely high LDL-c levels and subjects with extremely low LDL-c levels. We also showed that individuals with pathogenic Apolipoprotein B (APOB) mutations shared similar lipid profiles, which indicates an association between genetic mutations and lipid profiles. This work shows the potential to apply plasma lipidomics to study the molecular origins of dyslipidemia.
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In Chapter 5, I applied lipidomics to study an inborn error of metabolism, namely
the deficiency of medium-chain acyl-CoA dehydrogenase (MCAD), which is involved in fatty acid β-oxidation. To elucidate the role of hepatic MCAD in the response to energetic stress, we measured the different hepatic metabolites and the lipidomes of liver, brown adipose tissue, white adipose tissue, blood and muscle from fasted MCAD knockout mice exposed to cold. In contrast to the wild type mice, MCAD knockout mice showed lower blood glucose levels, lower hepatic amino acid levels, higher liver weights, as well as higer liver TG levels and blood medium-chain acyl-carnitine levels. Lipidomics analysis revealed an accumulation of TG species containing medium-chain fatty-acyl branches in the liver and brown adipose tissue. Collectively, MCAD-KO mice exhibit a compensatory response in which amino acids partly take over the role of fatty acids in the generation of ATP, glucose, and ketone bodies, while excess medium-chain fatty-acid intermediates are exported and rerouted into TGs. We also identified specific medium chain TG species in blood with extremely high levels in MCAD KO mice. These results need to be validated in human studies as potential lipid biomarkers for the disease.
In Chapter 6, we studied the effect of liver receptor homolog-1 (LRH-1)
whole-body knock-down on hepatic lipid homeostasis in mice under different dietary regimes. We acquired transcriptomics, proteomics and lipidomics data. We analyzed each layer of omics data and performed multi-omics integration of the different layers. The results show that altered lipid profiles in lrh-1 knock down mice are partly due to changes in expression of some key genes involved in lipid metabolism and also as a consequence of an intensive inflammatory process and the existence of liver fibrosis.
Part III
Chapter 7 summarizes the work in this thesis and provides some thoughts as to future perspectives for lipidomics analytical method development and clinical applications.
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