Metabolomics is the fourth ‘omics’ research field after genomics, transcriptomics and proteomics. The metabolome represents the entity of all ‘small’ molecules (< 1500 Dalton) and is most predictive of the phenotype of an organism.
Comparisons of the metabolome of well defined groups of individuals allow the identification of biomarkers, which are responsible for group differences. The purpose of this project is to analyse the metabolome in body fluids in smokers and non-smokers. Differences between these groups should lead to identification of biomarkers for smokers. This experimental setting could be applied to various groups and cases (e.g. healthy and diseased, etc.)
Figure: Strategy to identify novel biomarkers.
Methodically, the metabolome will be analyzed with gas chromatography coupled to time-of-flight mass spectrometry (GC-MS-TOF). Optionally, the metabolome could also be analyzed with LC-MS-Orbitrap. The obtained data will be processed with a mass spec deconvolution software (e.g. AMDIS) and further statistically analyzed. The signals responsible for the group differences will than be identified by making use of MS libraries. Candidate biomarkers will be validated with LC-MS/MS or GC-MS(/MS). This project is supported by Imperial Tobacco.
Related publications: For more information please click here
Daniel C. Müller, Christian Degen, Gerhard Scherer, Gerhard Jahreis, Reinhard Niessner, Max Scherer (2014) Metabolomics using GC-TOF-MS followed by subsequent GC-FID and HILIC-MS/MS revealed significantly altered fatty acid and phospholipid species profiles in plasma of smokers. J Chromatogr B Analyt Technol Biomed Life Sci. 2014; 966, 117-126
Daniel C. Müller, Markus Piller, Reinhard Niessner, Max Scherer, Gerhard Scherer. Untargeted Metabolomic Profiling in Saliva of Smokers and Nonsmokers by a Validated GC-TOF-MS Method. J Proteome Res. 2014 Mar 7;13(3):1602-13.
Daniel C. Müller, Markus Piller, Reinhard Niessner, Gerhard Scherer, Max Scherer (2014). Untargeted metabolic fingerprinting in urine and plasma of smokers and non-smokers. (in preparation)
Lipids and eicosanoids as biomarkers of effect
Cellular membranes are crucial to living cells. Despite their specific and different functions all membranes are commonly composed of lipids, which are arranged as continuous double layer. The most abundant membrane lipids of mammalian cells are the phospholipids phosphatidyl-choline (PC) and -ethanolamine (PE) (Ecker et al., PNAS, 2010; van Meer et al., Nat Rev Mol Cell Biol, 2008). Phospholipids are composed of a polar head group and two hydrophobic tails, which are fatty acids such as arachidonic acid (AA; C20:4). These are precursors of eicosanoids, which are signalling molecules with various physiological effects including inflammation.
Well known and investigated eicosanoids are Prostaglandins (PG), Thromboxanes (TX), Leukotrienes (LT), Epoxyeicosatrienoic (EET) and hydroxyleicosatetraenoic (HETE) derivatives. Eicosanoid generation is strongly increased under inflammatory conditions such as in diseases like the metabolic syndrome or when cells are under oxidative stress like smoking. Thus, these mediators might be used as biomarkers of effect.
Figure: Eicosanoid generation; PL: Phospholipids, PLA2: Phospholipase A2, AA: Arachidonic acid, COX: Cyclooxygenase, LOX: Lipoxygenase.
Eicosanoids are mostly extracted from urine and plasma by solid phase extraction (SPE) and analyzed with LC-MS/MS. However, extraction is very critical because these compounds are very unstable and their half life is very short. Separation by LC is also very tricky since a lot of these metabolites occur in isobaric forms.
Therefore we sucessfully developed methods to analyze selected phospholipid and eicosanoid species by LC-MS/MS and apply these methods in clinical studies (Sterz et al. JLR, 2012, Mueller et al. 2013, JCB in prep.). The eicosanoid pannel includes the following compounds:
tetranor PGE-M, tetranor PGD-M, 2,3-dinor-TXB2, 11-dehydro-TXB2, 12-HETE, LTE4, PGF2a, 8-iso-PGF2a, 2,3-dinor-8-iso-PGF2a (Sterz et al. JLR, 2012)
The phospholipid pannel comprises the two major membrane lipids phosphatidylcholine and phosphatidylethanolamine. Using HILIC-MS/MS we were able to quantify 39 individual PC species, and 40 PE species from plasma samples (Mueller et al. JCB, 2013).
In addition ABF has also methods for the quantification of various signalling lipids including sphingolipids, lysophospholipids and polyglycerophospholipids in its portfolio (Scherer M et al ClinChem 2009, JLR 2010, Anal Chem. 2010, BBA 2011).
These lipids comprise a highly diverse and complex class of molecules that serve not only as structural components of cell membranes but also as signalling molecules. Some metabolites including ceramide, sphingosine (SPH) and sphingosine-1-phosphate (S1P) have been shown to be involved in different cell functions such as proliferation, differentiation, growth arrest and apoptosis. Therefore, these lipids are associated to several diseases such as cancer, obesity and atherosclerosis. Strucutral diversity and inter-conversion of lipid metabolites represent technical challenges. Nevertheless, in order to understand the differential role of lipids in a regulatory network it is crucial to use specific and reliable methods, not only for the quantification of the major phospholipids such as PC and PE but also for the minor low abundant signalling lipids. Therefore, highly sensitive and robust methods based onn LC-MS/MS have been developed and can be applied to various biological matrices such as plasma, cells and different types of tissues.
Related publications: For more information please click here
Scherer M, Schmitz G, Liebisch G. High throughput analysis of sphingosine-1-
phosphate, sphinganine-1-phosphate and lysophosphatidic acid in plasma samples by
LC-MS/MS. Clin Chem. 2009 Jun; 55(6):1218-22.
Scherer M, Schmitz G, Liebisch G. Simultaneous quantification of cardiolipin,
bis(mono)acylglycerophosphate and their precursors by hydrophilic interaction LCMS/
MS including correction of isotopic overlap. Anal Chem. 2010 Nov 1;82(21):8794-9
Scherer M, Lethäuser K, Ecker J, Schmitz G, Liebisch G. A rapid and quantitative LCMS/
MS method to profile sphingolipids; J Lipid Res 2010, 51:2001-11.
Scherer M, Böttcher A, Schmitz G, Liebisch G. (2011) Sphingolipid profiling of human plasma and FPLC-separated lipoprotein fractions by hydrophilic interaction chromatography tandem mass spectrometry. Biochim Biophys Acta 2011 Feb;1811(2):68-75