The metabolome is the final succeeding product of the genome (Dunn et al. 2005, Lattimer and Haub 2010) and represents the total amount of small molecules – the metabolites - in a living cell (Nicholson et al. 1999). The resulting scientific discipline called “Metabolomics” and the related “Metabonomics”
term were defined by Fiehn and Nicholson (Nicholson et al. 1999, Fiehn 2002, Nicholson and Wilson 2003)
Alongside the other “omics” approaches, like genomics, transcriptomics and proteomics, metabolomics is one of the “omics”-disciplines in systems biology applied not only to investigate the metabolome.
Metabolomics enables to complete the information received from the genome and proteome on a functional level, to characterize the phenotype and to study the complex function of the metabolites in many different regulatory processes inside or outside the cell (Villas-Boas et al. 2005). Further, metabolites are intermediates of biochemical processes and thus play a very important role in connecting different pathways in organisms (Villas-Boas et al. 2005).
The metabolome varies in response to different influences (e.g. nutrition, medication and physiology), individual influences in health and disease and the involvement of the gut microbiota in the biological processes and thus reveals the complexity of the metabolome. Metabolomics plays a role in several research areas such as medical and clinical research (e.g. cancer, nutrition, obesity and diabetes), fundamental research and environmental interests. Therefore, metabolomics is applied to many sample origins, comprising different body fluids (plasma, urine), microbiome (gut microbiota, feces), cells, tissues or aquatic samples. To give a small abstract of the complexity of the metabolome, it may consist of hydrophilic compounds, carbohydrates, alcohols, ketones, amino acids, fatty acids, lipids and many others, but in many cases the identity of several metabolites remains unknown. This complexity makes it nearly impossible to study the whole metabolome simultaneously (Villas-Boas et al. 2005). In metabolome analyses, mostly metabolites with a molecular mass < 1000 Dalton (Da) are analyzed. In this context, in metabolomics another distinction is made between non-targeted and targeted analysis.
1.1.1 Non-targeted vs. targeted metabolome analysis – dealing with the unknown
The aim of non-targeted or also called untargeted analyses is to get a global overview of the variety of metabolites and metabolite classes present in a biological system. Hereby, identification and/or quantification of the metabolites is not needed (Fiehn 2002). The dominating focus here is the characterization of the biological samples and the identification of the overall metabolite profiles in the given objective. Here, it is possible to combine various analytical techniques to analyze the metabolome (Villas-Boas et al. 2005).
The non-targeted approach is faced with many unknown metabolites, whose identification is time-consuming and costly (Bowen and Northen 2010). Furthermore, identification of metabolites is difficult and demanding, which poses many challenges of experimental and analytical nature (Peironcely et al.
2013). Even if the modern analytical techniques allow detecting hundreds or thousands of features within one analysis, it’s nearly impossible to identify each detected feature. Currently available databases are not comprehensive and cover only a proportion of metabolites, which can be assigned to potential metabolites. Many metabolites in a complex matrix remain unknown, which correspond to either adducts, fragments, dimers or trimers or possibly new metabolites (Witting et al. 2015). Therefore, usually the most discriminant features obtained by statistical analyses will be selected for identification (Bowen and Northen 2010). In practice, not always well-known metabolites are responsible for class discrimination, but also the unknown ones. Therefore, different approaches for metabolite identification can be performed, whereas tandem mass spectrometry (Chapter 1.2.2.4) is widely used.
Conversely, in the targeted analyses a pre-defined set of metabolites is analyzed and quantified. These pre-defined metabolites usually belong to one class of metabolites (Fiehn 2002) as carbohydrates, fatty acids or lipids and usually comprise further sample preparation. Nowadays, for this characterization of metabolites further termini for the individual metabolite class analysis, including lipidomics for lipids (the lipidome) or glycomics for glycans (the glycome) were defined (Griffiths and Wang 2009). In general, there is no universal metabolomics approach for both types of analyses.
Additionally, many other periphrases of targeted and non-targeted analysis already exist, such as metabolite profiling and metabolic fingerprinting. Therefore, in 2007 Goodacre summarized the most common used analytical techniques for differently applied metabolome analysis (Goodacre 2007).
Hereafter, an abstract of the table is summarized. For targeted metabolite analysis, high-performance
liquid chromatography (HPLC), gas chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS) are widely used, whereas for the non-targeted approaches, techniques with high, even ultra-high resolution and high performance, such as quadrupole time-of-flight mass spectrometry (Q-ToF-MS) or Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) are used. In summary, the requirements to investigate the metabolome comprises the sample collection and preparation, the adequate analytical approach with appropriate sensitivity and selectivity (Fiehn 2002), which will further be described in detail in chapter 1.2.
1.1.2 Lipidomics as part of systems biology
Lipidomics became a biologically and analytically attractive technique to analyze the lipid content in a biological system and is able to complete the metabolome analyses. Lipids play important roles in many biological processes such as in energy storage, membrane lipids or as signal molecules (Witting et al.
2014) and even play a central role in gut physiology (Gregory et al. 2013). Thus, various classes of lipids are present in biological samples and already were classified by the Lipid Maps consortium (www.lipidmaps.org) into the following main classes with several subclasses: fatty acyls (FA), glycerolipids (GL), glycerophospholipids (GP), sphingolipids (SP), sterol lipids (ST), prenol lipids (PR), saccharolipids (SL) and polyketides (PK) (Fahy et al. 2005, Fahy et al. 2009). Another category of GP are lysolipids, emerging through the loss of one or both acyl groups (Gregory et al. 2013).
Figure 1.1-1: Structural overview of the various lipid classes.
A: saturated fatty acids differentiating in chain length in SCFA (C1:0 – C5:0), MCFA (C6:0 – C12:0) and LCFA (C13:0 – C22:0). B: unsaturated fatty acids (different chain length, mono-unsaturated and poly-unsaturated fatty acids possible). C: Sterol lipids and steroid conjugates with glycine or sulfate conjugation. D: Various classes of Glycerophospholipids with changing head groups, R1 and R2 can be fatty acids (with various chain length, double bonds and branches) bound by an ether or ester bond.
The variety and complexity of the lipids are impressive, as the combinations in lipids with different chain lengths, branches, side chains, double bonds, head groups, functional groups and other modifications are almost never-ending. To illustrate the complexity, Figure 1.1-1 shows some structural information of the various lipid classes (e.g. saturated/unsaturated fatty acids, STs and GPs). However, this complexity coincidentally poses difficulties in the analysis and differentiation of lipids with respect to isomeric and isobaric lipid species. The analytical technique is not only required to separate isomers, but also to sensitively detect and identify lipids from different classes. Therefore, a chromatographic separation with high performance coupled to a mass spectrometer with high resolution is one of the analytical approaches for lipidomics or lipid profiling applied, wherefore reversed phase (RP) columns using an acetonitrile (ACN) and isopropyl alcohol (IPA) gradient are commonly used (Bird et al. 2011,
Witting et al. 2014). In addition to lipid profiling, another method, called “shotgun lipidomics” is applied.
Here, the crude lipid extract is analyzed by direct infusion into the MS (DI-MS) (Han and Gross 2005), without prior chromatographic or other separation methods. The major disadvantage of this method is the impossibility to separate closely related isomers (Witting et al. 2014), due to their similar physicochemical properties. Even if shotgun lipidomics is still at its early stage, it provides a convincing basis to investigate the lipidome in biological samples and shows its increasing potential to analyze thousands of lipid species (Han and Gross 2005).