Methods applications and concepts of metabolite profiling Primary metabolism

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Dirk Steinhauser and Joachim Kopka

Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 PotsdamGolm, Germany

Abstract

In the 1990s the concept of a comprehensive analysis of the metabolic complement in biological systems, termed metabolomics or alternately metabonomics, was established as the last of four cornerstones for phenotypic studies in the post-genomic era. With genomic, transcriptomic, and proteomic technologies in place and metabolomic phenotyping under rapid development all necessary tools appear to be available today for a fully functional assessment of biological phenomena at all major system levels of life. This chapter attempts to describe and discuss crucial steps of establishing and maintaining a gas chromatography/electron impact ionization/ mass spectrometry (GC-EI-MS)-based metabolite profiling platform. GC-EI-MS can be perceived as the first and exemplary profiling technology aimed at simultaneous and non-biased analysis of primary metabolites from biological samples. The potential and constraints of this profiling technology are among the best understood. Most problems are solved as well as pitfalls identified. Thus GC-EI-MS serves as an ideal example for students and scientists who i ntend to enter the field of metabolomics. This chapter will be biased towards GC-EI-MS analyses but aims at discussing general topics, such as experimental design, metabolite identification, quantification and data mining.

Introduction

In the 1990s the concept of a comprehensive analysis of the metabolic complement in biological systems, termed metabolomics [1, 2] or alternately metabonomics [3, 4], was established as the last of four corner stones for phenotypic studies in the post-genomic era (e.g., [5-8]). With genomic, transcriptomic, and proteomic technologies in place and metabolomic phenotyping under rapid development all necessary tools appear to be available today for a functional assessment of biological phenomena at all major system levels of life. However, all '-omics' technologies are at different stages of comprehensiveness, sample throughput and accuracy of constituent identification and quantification. While the set of genes in an organism can be exactly defined and described, knowledge of the full inventory of metabolites and a truly comprehensive metabolome analysis remains a vision for the future. The

Figure 1. Principal component analysis covering 38.5% and 21.9% of total variance in a dataset of leaf metabolite profiles from Arabidopsis thaliana ecotype Columbia. Plants were environmentally challenged by highlight (L, diamonds; long-term adaptation to 560 and 850 ^E/m2 compared to a control at 120-150 ^.E/m), by high temperature (H, squares; up to 4 h at 40C compared to a control at 20C) and by low temperature (C, circles; up to 96 h at 4C compared to a control at 20C) [17]. Different formatting highlights environmental challenge (A) and time course compared to the control group (B). Note: (1) Highlight and high temperature response exhibits an expected partial overlap (arrows). (2) Cold de-acclimatized plants (CD, triangles; 24 h reversion to 20C after 96 h at 4C) show the existence of metabolic memory after reversion to optimum temperature conditions.

Principal Component 1 Principal Component 1

Figure 1. Principal component analysis covering 38.5% and 21.9% of total variance in a dataset of leaf metabolite profiles from Arabidopsis thaliana ecotype Columbia. Plants were environmentally challenged by highlight (L, diamonds; long-term adaptation to 560 and 850 ^E/m2 compared to a control at 120-150 ^.E/m), by high temperature (H, squares; up to 4 h at 40C compared to a control at 20C) and by low temperature (C, circles; up to 96 h at 4C compared to a control at 20C) [17]. Different formatting highlights environmental challenge (A) and time course compared to the control group (B). Note: (1) Highlight and high temperature response exhibits an expected partial overlap (arrows). (2) Cold de-acclimatized plants (CD, triangles; 24 h reversion to 20C after 96 h at 4C) show the existence of metabolic memory after reversion to optimum temperature conditions.

highly diverse chemical properties of metabolites which range from gasses, such as O2 and CO2, to macromolecules such as starch and complex lipids, is the crucial limiting factor. This high diversity impedes comprehensive metabolomics with single analytical technologies. Thus the current developments in metabolomic technologies focus on establishment and optimization of minimally overlapping, broad-spectrum metabolite profiling methods which have been pioneered decades earlier (e.g., [9-11]).

This chapter attempts to describe and discuss crucial steps of establishing and maintaining a gas chromatography/electron impact ionization/mass spectrometry (GC-EI-MS)-based metabolite profiling platform. GC-EI-MS can be perceived as the first and exemplary profiling technology aimed at simultaneous and non-biased analysis of primary metabolites from biological samples [12, 13]. The potential and constraints of this profiling technology are among the best understood. Most problems are solved as well as pitfalls identified. Thus GC-EI-MS serves as an ideal example for students and scientists who intend to enter the field of metabolomics. This chapter will be biased towards GC-EI-MS analyses but aims at discussing general topics, such as experimental design, metabolite identification, quantification and data mining. For a more detailed review of metabolic inactivation, metabolome sampling, metabolite extraction, chemical derivatization, gas chromatographic separation, mass spectral ionization and detection the reader is referred to previous reviews [14-16].

As detailed bio-analytic aspects are best exemplified with a relevant experiment in mind, most discussions will refer to one data set, which describes the metabolic phenotype of environmentally challenged and genetically modified Arabidopsis thaliana plants as summarized by a principal components analysis (Fig. 1). This experiment charts metabolic changes of a model plant in response to common environmental stresses such as variable light and temperature [17].

Experimental design

Pairwise comparison, dose dependency or time-course

Alongside the immediate and full metabolic inactivation at and following time of sampling [14, 15], the crucial issue in a metabolite profiling study is experimental design. It is evident that the result and quality of a profiling experiment depends on a design which is optimally fitted to the question that is about to be addressed. If a genetically modified organism (GMO) or an environmental challenge is first analyzed for metabolic equivalence, metabolite profiling studies can be successfully used to screen for relevant metabolic changes (e.g., [18, 19]). This task is purely descriptive and can be solved by pairwise or multiple comparison. In a comparative experiment only one factor, such as the genotype or one environmental parameter, is changed and all other influences are, ideally, kept constant. Typically each of the compared conditions is replicated within one experiment and in independent consecutive experimental repeats. The aim of repetition is to distinguish true differences from unavoidable experimental errors and basic biological variability (see control samples of Fig 1B; also note that the cold stress experiment was performed in two independent experiments which cannot be distinguished by PCA analysis). By application of statistical significance tests any detected change within the metabolic phenotype can be unequivocally linked to the experimental manipulation, such as mutant versus ecotype [12], temperature stress [17], transgene expression or chemical treatment with glucose (e.g., [13, 20]). Functional genomics studies employ multiple comparative analyses for the classification of genes with yet unknown or hypothetical function by similarity of the metabolic phenotypes [8]. However, these comparisons typically result in multiple detected statistically significant changes. Among these the primary mechanistic effect of modified genes or environmental impact can not unambiguously be distinguished from secondary pleiotropic metabolic adaptations to the usually constitutive genetic modification. In other words the permanent presence or absence of transgene expression throughout the life cycle of a GMO may result in unexpected long-term adaptations of primary metabolism, which up to today were overlooked by biased and targeted metabolic analysis.

One strategy to dissect primary metabolic effects from secondary adaptations is the use of dose dependency. In environmental challenges different light intensities, temperatures or concentrations of nutrients and chemicals can be applied. In GMO studies stably modified lines with a range of low, medium to high transgene expression can be selected. Chemically controlled or otherwise inducible promoters can be employed for the same purpose. The use of these promoters may yield different metabolic responses compared to constitutive promoters and generate novel insights into metabolic regulation (e.g., [21]). In all cases sensitive metabolic effects which respond to small doses can be distinguished from effects of high doses that are more prone to cause pleiotropic effects. Moreover, the dose quantity can be linked to a quantitative metabolic effect for example by application of correlation analysis. It can be argued that those metabolic effects which show a strict dose dependency have a strong mechanistic link. Caution needs to be applied in thoroughly controlling dose dependency experiments. For example the effect of a chemical inductor needs to be distinguished from the effect of transgene expression. Also environmental changes may not be independent, for example increased light intensity and heat have similar metabolic effects as is demonstrated by a partial overlap of the heat response and the highlight metabolite phenotypes of Arabidopsis thaliana rosette leaves (Fig. 1A).

The best but also most demanding strategy to dissect possible mechanisms of metabolic changes is a time-course design (Fig. 1B). It can be argued that early changes are linked to sensing and represent a direct response mechanism, whereas secondary adaptations will be observed in a long-term transition from the initial to a final metabolic state to, for example, a cold-adapted metabolism (Fig. 1A). Time-course investigations do not only allow comparison of initial and stably adapted metabolic states but also unravel the sequence of metabolic events and transient, i.e., reversible changes, which would otherwise be overlooked, such as early maltose and maltotriose accumulation in Arabidopsis thaliana cold adaptation (Fig. 2). The example of cold adaptation in plants also unveils that the history of a biological system may determine the metabolic phenotype. Cold de-acclimatized plants, even after 24 h reversion to optimum temperature, still exhibit a metabolic memory (Fig. 1A). In conclusion, good experimental practice for optimum reproduction of biological experiments not only controls the conditions at the time of sampling but also the history of the biological objects.

Fingerprinting, profiling or exact quantification

The experimental design of GC-EI-MS analyses has a strong impact on the accuracy of metabolome studies. Three major approaches were described and have been extensively discussed, i.e., fingerprinting, metabolite profiling and exact quantification [6-8, 22]. In general, the complexity of information and number of theoretically covered metabolites decreases when moving from fingerprinting to exact quantification [8]. Typically a concomitant increase in experimental complexity is observed, with higher time demand, and requirements for quantitative standardization or compound identification.

Fingerprinting studies appear to be the easiest approach to metabolome analysis. These studies utilize all detector readings for numerical analysis without the at-

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Figure 2. Transition of metabolic states exemplified by the time course of 4C cold adaptation of Arabidopsis thaliana plants, ecotype Columbia. Note that Maltose and Maltotriose exhibit early transitory accumulation followed by sustained increases in Glucose-6-phosphate, glucose, galactinol and ultimately raffinose, a metabolic product of galactinol in plants [17].

Figure 2. Transition of metabolic states exemplified by the time course of 4C cold adaptation of Arabidopsis thaliana plants, ecotype Columbia. Note that Maltose and Maltotriose exhibit early transitory accumulation followed by sustained increases in Glucose-6-phosphate, glucose, galactinol and ultimately raffinose, a metabolic product of galactinol in plants [17].

tempt, and in some cases even the potential, to unambiguously identify the specific metabolites represented in these experiments. Fingerprints are used for metabolic pattern comparison aimed at the discovery of experimental conditions which result in similar or identical metabolic responses, so-called metabolic phenocopies [20]. This approach is exploited in gene function analysis and has the potential to group genes with known function and orphan genes of unknown or hypothetical function into classes of similar or identical metabolic function [2, 5]. This type of metabolic pattern analysis appears to be especially promising when gene modifications result in 'silent' phenotypes. (For the definition of silent phenotype refer to [18].) This phenomenon is better defined as changes of the metabolic state in organisms, which do not show obvious visual or morphological traits.

Fingerprinting, however, has one fundamental requirement, which results from unavoidable technical drifts in the calibration of mass, retention time and ion current. These decalibration artifacts are inherent to all chromatographic and mass spectrometric analysis technologies. In GC-EI-MS analyses one of the technology breakthroughs was the employment of widely accepted reference substances for the automated mass calibration of the GC-MS systems, such as BFB (4-bromofluor-obenzene) and DFTPP (decafluorotriphenylphosphine). These substances are used in so-called tuning procedures which are inbuilt into the maintenance routines of the respective manufacturer. GC-MS tuning of the mass scale is usually performed prior to a series of analyses and allows accurate mass alignment. A rather low resolution of 1 atomic mass unit is sufficient for most of the small molecules which are routinely analyzed by GC-MS. More precise mass calibration can be obtained by reference compounds, which are continuously added to the GC effluent before mass analysis. This so-called 'lock-mass' technology is only useful for the high mass accuracy obtainable with sector field or specialized high-resolution time-of-flight GC-TOF-MS systems. While negligible for the low mass resolution typically achieved by quadrupole, iontrap or fast scanning time-of-flight GC-MS systems, the 'lock-mass' calibration has significantly improved routine LC-MS profiling experiments (e.g., [23]).

Likewise the retention time axis should be calibrated by use of retention time standard substances. One of the most widely accepted procedure utilizes mixtures of n-alkanes [24] and so-called retention time indices (RI) to correct for inevitable retention time shifts within and between series of consecutive chromatograms. The use of retention time indices has been introduced to GC-EI-MS metabolite profiling experiments [12, 13]. In these early studies n-acyl fatty acids were used, which were later substituted for n-alkanes [25] to allow for better comparability with the wealth of previous RI information, which - since 2005 - is commercially provided together with thousands of biologically relevant GC-MS mass spectra [26-28] by the NIST05 mass spectral library (National Institute of Standards and Technology, Gaithersburg, MD, USA; http://www.nist.gov/srd/mslist.htm).

One of the most critical causes for artifacts in fingerprinting studies, in many studies, is the non-calibrated ion current scale. The quantity of metabolic components from GC-MS runs is routinely measured by ion currents detected after chro-matography, ionization, and mass separation. The quantity of ions which reaches the final detector system is subject to multiple artifacts. One of the most important effects is exerted through the decrease of detector sensitivity over time. The detector sensitivity is partially corrected by the tuning procedure mentioned above. However, the best approach is the use of quantitative reference substances, so-called internal standards (IS), which are added to the biological sample at constant known quantities prior to metabolite extraction and are carried along throughout the complete analysis. The most versatile IS are stable isotope-labeled substances [12, 22, 29].

Today, software tools which use statistical algorithms for the alignment of mass and time dimensions promise good success by avoiding artifacts through false alignment (for example [30-32] or metAlign, http://www.metalign.nl [33, 34]). However, the limits of both mass and retention time drift successfully corrected by these software tools have still not been thoroughly tested. Therefore, chemical calibration of all three dimensions in hyphenated GC-EI-MS analysis represent the most secure approach towards valid fingerprinting (Fig. 3).

In contrast to fingerprinting, metabolite profiling studies attempt to identify all metabolites which are represented in the dataset. Non-identified components can be discarded or used for fingerprinting. In profiling experiments the analysis is restricted to the selected subset of those analytical detector readings which can be identified. The clear advantage of this approach is the possibility that the metabolic pattern of profiling experiments can be biochemically interpreted. Thus, besides pattern recognition and comparison, metabolite profiling has the potential to provide insight into the mechanism of gene function or the response triggered by envi

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