From models to crop species: caveats and solutions for translational metabolomics

Takayuki Tohge

doi:10.3389/fpls.2011.00061

Abstract

Although plant metabolomics is largely carried out on Arabidopsis it is essentially genome-independent, and thus potentially applicable to a wide range of species. However, transfer between species, or even between different tissues of the same species, is not facile. This is because the reliability of protocols for harvesting, handling and analysis depends on the biological features and chemical composition of the plant tissue. In parallel with the diversification of model species it is important to establish good handling and analytic practice, in order to augment computational comparisons between tissues and species. Liquid chromatography–mass spectrometry (LC–MS)-based metabolomics is one of the powerful approaches for metabolite profiling. By using a combination of different extraction methods, separation columns, and ion detection, a very wide range of metabolites can be analyzed. However, its application requires careful attention to exclude potential pitfalls, including artifactual changes in metabolite levels during sample preparation under variations of light or temperature and analytic errors due to ion suppression. Here we provide case studies with two different LC–MS-based metabolomics platforms and four species (Arabidopsis thaliana, Chlamydomonas reinhardtii, Solanum lycopersicum, and Oryza sativa) that illustrate how such dangers can be detected and circumvented.

Highlights

Plant metabolomics is a relatively new analytic strategy which provides complementary information to transcriptomic and proteomic studies as well as important information in its own right concerning the regulation of metabolic networks (Hall et al, 2002; Bino et al, 2004)
There are several examples of Arabidopsis genes that have been identified with the help of metabolomic approaches including MYB transcription factors (Hirai et al, 2007; Stracke et al, 2007), O-methyltransferase (Tohge et al, 2007), glycosyltransferases (Tohge et al, 2005; Yonekura-Sakakibara et al, 2007, 2008), acyltransferases (Luo et al, 2007), UDP-rhamnose synthase (Yonekura-Sakakibara et al, 2008), and pyrophosphorylase (Okazaki et al, 2009) with the approach being effective in other species (Aharoni et al, 2000; Goossens et al, 2003; Achnine et al, 2005; Yamazaki et al, 2008)
HARVESTING – OBTAINING REPRESENTATIVE MATERIAL AND AVOIDING HANDLING-INDUCED CHANGES Expression of genes and activity of enzymes associated with photosynthesis, respiration, and energy metabolism are rapidly affected by changes in environmental conditions

Summary

Introduction

Plant metabolomics is a relatively new analytic strategy which provides complementary information to transcriptomic and proteomic studies as well as important information in its own right concerning the regulation of metabolic networks (Hall et al, 2002; Bino et al, 2004). The use of metabolic profiling in plants, as in all species, was restricted to diagnostic approaches in which the obtained profiles were used as markers for a range of biological conditions (Sauter et al, 1988; Meyer et al, 2007; Semel et al, 2007; Carmo-Silva et al, 2009; Scherling et al, 2009; Widodo Patterson et al, 2009) Such studies remain highly important, in medical research (Nicholson and Wilson, 2003; Griffin and Nicholls, 2006), more sophisticated uses of metabolic profiling have recently been developed, including identifying regulated enzymes and exploring the regulatory structure of pathways (Tiessen et al, 2002; Arrivault et al, 2009), searching for unexpected effects of genetic manipulation (Catchpole et al, 2005), screening wild species for beneficial chemical composition (Zhu and Wang, 2000; El-Lithy et al, 2005), gaining a more comprehensive view of metabolic regulation and as part of integrative analyses for the systemic response of environmental genetic perturbations (Hirai et al, 2004, 2005; Fukushima et al, 2009; Sulpice et al, 2009; Trenkamp et al, 2009). There are several examples of Arabidopsis genes that have been identified with the help of metabolomic approaches including MYB transcription factors (Hirai et al, 2007; Stracke et al, 2007), O-methyltransferase (Tohge et al, 2007), glycosyltransferases (Tohge et al, 2005; Yonekura-Sakakibara et al, 2007, 2008), acyltransferases (Luo et al, 2007), UDP-rhamnose synthase (Yonekura-Sakakibara et al, 2008), and pyrophosphorylase (Okazaki et al, 2009) with the approach being effective in other species (Aharoni et al, 2000; Goossens et al, 2003; Achnine et al, 2005; Yamazaki et al, 2008)

Methods

Results

Conclusion