Metabolomics in Food and Nutrition

Bart C Weimer, Carolyn Slupsky

• Publisher: Woodhead Publishing
• Year: 2013
• ISBN:
  9780857098818
• Topic: Technology & Engineering
• Subtopic: Food Science
• Index: Technology & Engineering

Metabolomics enables valuable information about the biochemical composition of foods to be rapidly obtained. Since the biochemical profile of food largely determines key food properties such as flavour and shelf life, the information gained using metabolomics-based methods will enable greater control of food quality and also help to determine the relationship between diet and health. Metabolomics in food and nutrition provides an overview of their current and potential use in the food industry.Part one reviews equipment, methods and data interpretation in metabolomics including the use of nuclear magnetic resonance (NMR), statistical methods in metabolomics, and metabolic reconstruction databases and their application to metabolomics research. Part two explores applications of metabolomics in humans, plants and food. Chapters discuss metabolomics in nutrition, human samples for health assessments, and current methods for the analysis of human milk oligosaccharides (HMOs) and their novel applications. Further chapters highlight metabolomic analysis of plants and crops, metabolomics for the safety assessment of genetically modified (GM) crops, and applications of metabolomics in food science including food composition and quality, sensory and nutritional attributes.With its distinguished editors and team of expert contributors, Metabolomics in food and nutrition is a technical resource for industrial researchers in the food and nutrition sectors interested in the potential of metabolomics methods and academics and postgraduate students working in the area.

• Provides an overview of the current and potential future use of metabolomics in the food industry
• Chapters focus on key applications and review the analytical methods used and the bioinformatics techniques involved in processing the results
• Discusses metabolomics in nutrition, human samples for health assessments, and current methods for the analysis of human milk oligosaccharides (HMOs) and their novel applications

Part I

Equipment, methods and data interpretation in metabolomics

1

Equipment and metabolite identification (ID) strategies for mass-based metabolomic analysis

C.J. Wachsmuth, P.J. Oefner and K. Dettmer, University of Regensburg, Germany

Abstract:

Mass spectrometry (MS) in combination with chromatographic separation techniques such as liquid chromatography (LC) and gas chromatography (GC) is widely used in metabolomics. Shortcomings include the lack of comprehensive mass spectral libraries for LC-MS, the frequent need for derivatization in GC-MS, and the lack of metabolite mass spectral libraries for derivatives. The lack of comprehensive mass spectral libraries can be overcome using soft ionization techniques. In combination with high-resolution MS, measurement of accurate mass of the quasi-molecular ion allows elemental formulas to be searched against metabolite databases. This chapter describes the basics of LC, GC and MS and discusses limitations in their application to metabolomics, with an emphasis on the identification of unknown analytes.

Key words

metabolite identification

high-resolution mass spectrometry

gas chromatography–mass spectrometry (GC-MS)

liquid chromatography-mass spectrometry (LC-MS)

metabolic fingerprinting

metabolic profiling

metabolomics

database search

1.1 Introduction

Metabolomics (Dettmer and Hammock, 2004) aims at providing an in-depth view of chemical changes in cells, tissues, organs or organisms evoked by cellular processes in response to genetic and environmental causes. It is an integral part of systems biology and provides a direct link between an external stimulus and the phenotype or physiology of a biological system (Gygi et al., 1999, Sumner et al., 2003). As such, it contributes, for example, to our understanding of the role of nutrition in maintaining good health and in contributing to or causing disease (German et al., 2002).

Metabolome analysis comprises both the qualitative and the quantitative assessment of low-molecular mass compounds (< 1000 Da), which show tremendous diversity in their chemical and physical properties. Moreover, metabolite concentrations range over up to ten orders of magnitude. There are different strategies in metabolomics (Fiehn, 2002, Dettmer et al., 2007): metabolic profiling is a hypothesis-driven rather than hypothesis-generating approach, which aims at the quantitative analysis of sets of metabolites representing either a particular biochemical pathway or a specific class of compounds. It imposes particularly high requirements on the robustness and accuracy of an analytical method. A related approach is target analysis, which is directed at the measurement of only a few selected analytes, such as biomarkers. Metabolic fingerprinting, on the other hand, pursues the recognition of changes in metabolite patterns (so-called ‘fingerprints’) in response to disease, environmental or genetic alterations. Its ultimate goal is the identification of discriminating metabolites. Metabolic fingerprinting puts high requirements on precision of measurement and data alignment, as well as classification and multivariate analysis of large and high-dimensional data sets.

Mass spectrometry (MS) has become a prominent tool in metabolomics. It allows not only the sensitive quantitative determination of metabolites over a few orders of magnitude of dynamic range, but also their identification based on accurate mass measurements, isotopic distributions, and characteristic fragmentation patterns. Originally used in combination with gas chromatography to detect metabolites in urine and tissue extracts (Dalgliesh et al., 1966), the introduction of electrospray ionization (Fenn et al., 1989) popularized direct infusion mass spectrometry (Goodacre et al., 2003, Zahn et al., 2001). However, in the case of complex biological specimens, one has to cope with so-called matrix effects, in which the matrix co-extracted with the analytes can alter the signal response, resulting in poor analytical accuracy, linearity, and reproducibility. Furthermore, direct infusion MS cannot distinguish between isobaric and isomeric species, whose masses are identical, and which often yield similar fragmentation profiles. While still popular in the targeted analysis of metabolites in combination with extraction techniques for enrichment of analytes and the use of appropriate stable-isotope labeled analogues (Gieger et al., 2008), direct infusion MS has been increasingly replaced by the on-line coupling of high-performance separation techniques such as liquid chromatography (LC) and gas chromatography (GC) to MS using a variety of interfaces for the ionization of eluting analytes.

Mass spectrometry instrumentation has seen significant improvement over the years, including the development of robust front-end ionization technologies for a wide range of compound classes, increased sensitivity and resolution, ease of use, and hybrid instruments such as the linear ion trap–Fourier transform ion cyclotron resonance mass spectrometer for fast multistage tandem MS experiments as well as ultra-high resolution and accurate mass determinations. Nevertheless, metabolite identification remains a major challenge. At present, identification can be achieved by means of mass spectral libraries in the case of commonly used electron ionization by GC-MS, but mass spectral library searches typically identify only a minority of signals (Almstetter et al., 2011, Almstetter et al., 2009). Additional strategies must be implemented, such as the generation of quasi-molecular ions by soft ionization methods (chemical ionization (CI), atmospheric pressure chemical ionization (APCI)) followed by calculating sum formulas and additionally considering the isotopic pattern (Kind and Fiehn, 2006) and chemical and heuristic rules (Kind and Fiehn, 2007). Accurate mass measurements for identification are commonly performed using LC-MS, and databases are searched afterwards. The lack of reference compounds, however, impedes unambiguous assignment of unknowns. MSⁿ acquisition and interpretation of the fragmentation pattern might be of help, as it provides additional structural information. Recent years have seen a flurry of novel computational approaches for identifying metabolites, but systematic evaluations of their performance are still lacking, making a definitive judgment on their routine usefulness difficult.

Cross-platform approaches are utilized to cope with analyte diversity. Moreover, multiple metabolite databases can be searched (e.g. HMDB (Wishart et al., 2009), LIPID MAPS (Sud et al., 2007), and METLIN (Smith et al., 2005)). Once identified, metabolites may be visualized within their metabolic pathways (e.g. provided by KEGG database) to facilitate biological interpretation. Finally, metabolomics data can be incorporated with results obtained by the other -omics methods to obtain a global picture of the organism under study.

This chapter portrays front-end separation (LC, GC) and MS technologies for both targeted and untargeted metabolomics, with a special focus on compound ID approaches.

1.2 Liquid chromatography

Coupling of MS with a separation technique (GC, LC) reduces the complexity of the mass spectra of biological specimens due to metabolite separation in a time dimension, provides isomer and isobar separation, and delivers information on the physicochemical properties of metabolites. LC is preferred for semi- or nonvolatile analytes provided that they can be dissolved in a suitable solvent such as water, water/methanol or water/acetonitrile in the case of reversed phase chromatography. Ideally, the sample should be dissolved in the mobile phase of the starting gradient.

An LC assembly consists of a pumping system for one to four solvents (often joined by an on-line degasser) that constitute the mobile phase required for carrying the dissolved analyte mixture through the LC column. The analyte mixture is introduced in the majority of applications by an autosampler that uses a six-port valve to maintain an uninterrupted flow through the column. Finally, following separation according to interaction with the stationary phase, analytes are detected and a chromatogram is recorded. Commonly employed detection methods include UV absorbance at selected wavelengths or full UV/Vis spectra data acquisition by means of a diode-array detector, fluorescence and electrochemical detection, the latter being the most sensitive and selective mode of LC detection for the measurement of trace amounts of oxidizable or reducible compounds, as well as mass spectrometry. The latter is the most universal detector. Depending on the type of mass analyzer, it can be applied to both screening and selective detection provided that the analytes can be ionized. Selective detection as pr...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributor contact details
• Woodhead Publishing Series in Food Science, Technology and Nutrition
• Introduction
• Part I: Equipment, methods and data interpretation in metabolomics
• Part II: Applications of metabolomics in humans, plants and food
• Index