Essay 12.2 Metabolic Profiling of Plant Cells

Essay 12.2 Metabolic Profiling of Plant Cells

Sonia Osorio, Alisdair R. Fernie, Max-Planck-Institut für Molekulare Pflanzenphysiologie, Golm, Germany

(December, 2012)


Metabolomics was defined by Fiehn as “a comprehensive analysis in which all the metabolites of an organism are identified and quantified” (Fiehn 2002). Metabolomics has emerged as a valuable technology for the comprehensive profiling and comparison of metabolites in biological systems, including both primary and secondary metabolism. Given that plants are especially rich in chemically diverse metabolites and that these are usually present in a wide range of concentrations, plant metabolomics is highly challenging. In this essay we will introduce the methods currently used to analyze metabolites in plants and to interpret the large data sets obtained. We will then describe the general applicability of these methods in phenotyping genetically and environmentally diverse plant systems. We will additionally concentrate on the growing role of metabolite profiling in plant systems biology. This essay will begin with a historical evaluation of holistic approaches to biochemistry. Thereafter, the major focus will be on the integration of broad-range metabolite profiling into genomics approaches, and we will briefly touch on the use of metabolic profiling in the identification of gene function and in genomics-assisted breeding. Finally, we will describe future prospects for this rapidly expanding branch of plant biochemistry.


Given the chemical diversity and dynamic range of the metabolites present in the plant kingdom, no single analytical method is currently capable of extracting and detecting all metabolites. However, iterative improvement of the coverage and accuracy of applied methods is ongoing.

Traditional analysis deals with a limited number of compounds that are expected to be of particular importance in a given situation. For example, in the case of carbohydrate metabolism, analyses usually focus on starch and sugars, and investigations of glycolysis and respiration tend to look at intermediates of these individual pathways. While these approaches have yielded important insights into metabolic regulation, they have been limited to the pathway under study. These studies are also labor intensive because typical enzyme-based metabolite analyses provide information on only a single compound per assay. An alternative method, chromatographic separation coupled to nonspecific detection, can only be applied to relatively simple mixtures, which often require clean-up steps.

Over the past decade, several methods suitable for large-scale analysis and comparison of metabolites in plant extracts have been established. Chief among these have been nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). MS-based platforms are most widely used in plant metabolomics, where a separation technique, such as gas chromatography (GC), capillary electrophoresis (CE) or liquid chromatography (LC), is coupled to various kinds of MS. Methods developed in our institute, based on the coupling of gas chromatography to mass spectrometry (GC-MS), enable the identification and robust quantification of a few hundred primary metabolites within a single extract, for example 150 compounds identified in potato (Solanum tuberosum) tuber (Roessner et al. 2000) and 326 compounds identified in Arabidopsis thaliana leaf (Fiehn et al. 2000). This method has recently been applied to other plant species, such as tomato, strawberry, peach, pepper, and maize (Schauer et al. 2006, Fait et al. 2008, Lombardo et al. 2011, Osorio et al. 2011b, Osorio et al. 2012, Witt et al. 2012). The technical reliability of the method was tested by a wide range of control studies that determined both the variability of the procedure and the recovery of 25 defined metabolites through the process. These tests revealed that the biological variability among wild-type potato tuber samples was generally an order of magnitude higher than that associated with the analytical process (Roessner et al. 2000). GC-MS has a relatively broad coverage of non-volatile compound classes, mainly those involved in primary metabolism, including organic and amino acids, sugars, sugar alcohols, and phosphorylated intermediates within the polar phase (Roessner et al. 2000, Schauer et al. 2006), as well as lipophilic compounds, such as fatty acids, fatty alcohols, and sterols, and aliphatic compounds within the apolar phases (Fiehn et al. 2000). The preferred method for analyzing semi-polar metabolites is LC-MS. Compounds detected by LC-MS include the large and often economically important group of plant secondary metabolites, such as phenylpropanoids, flavonoids, and alkaloids (Tohge et al. 2005, Moco et al. 2007, Fait et al. 2008, Iijima et al. 2008, Mintz-Oron et al. 2008, Tohge et al. 2011). Depending on the type of column used, various primary metabolites, including several polar organic acids and amino acids, can be reliably analyzed by LC-MS.

An alternative platform for broad-scope metabolic profiling is NMR, which relies on the detection of paramagnetic nuclei of atoms following application of a constant magnetic field. These methods are well-developed and well-validated and the computer software associated with NMR instruments is advanced (Kim et al. 2011). NMR has both advantages and disadvantages. The latter rests with its relatively low sensitivity, while the former in its ability, to rapidly distinguish the complex metabolome in plant and crop systems. However, as an unbiased approach, used along with data mining and subsequent statistical analysis, it has more than proved its worth as a metabolomic screening tool in crops. Furthermore, the application of 2D (Ludwig and Viant 2010, Palama et al. 2010), 3D (Chikayama et al. 2008), and higher dimensional approaches (Eisenreich and Bacher 2007) offers the ability to resolve structural and temporal changes in the metabolome.

In conclusion, MS-based systems linked to GC and LC have generally become the approach of choice in plant and crop metabolomics.

Application of Metabolic Profiling Methods to the Characterization of Diverse Biological Systems

Fleshy fruit offer a further level of chemical complexity for the metabolomics researcher because the metabolites that determine fruit quality—including nutrients, putative bioactive compounds, and toxins—and thereby influence marketing and consumption, are very diverse. The metabolic diversity is matched by a broad dynamic range. For example, total anthocyanin content ranges from 0 mg/g in some fruit to 2–10 mg/g in blackcurrant, raspberry, blueberry, and less-studied fruit such as choke berry and elderberry (Clifford 2000). Likewise, metabolites responsible for organolepsis (taste; sugars and organic acids) and nutrition (vitamins C and A), and compounds with putative bioactivity (flavonoids and ellagitannins) display similar levels of variation. Despite the importance of metabolites in determining fruit quality, only a few true metabolic studies (i.e., untargeted studies of metabolite changes by GC-MS, LC-MS or NMR) have been done on fruit, and these have been limited to comparatively few species, such as apple (Rudell et al. 2008, Rudell et al. 2009), melon (Biais et al. 2009), raspberry (Stewart et al. 2007, McDougall et al. 2008), strawberry (Fait et al. 2008, Osorio et al. 2011b, Zhang et al. 2011), peach (Lombardo et al. 2011), and grape (Grimplet et al. 2009, Ali et al. 2010). Interestingly, metabolomics has been brought into the processed food arena with a combined GC-MS and GC-static headspace solid-phase micro-extraction (GC-HS-SPME) metabolomic study of durum wheat varieties (Beleggia et al. 2009). This study established correlations between the matrix of compounds in raw wheat and the volatile components in cooked wheat, and highlighted the influence of the wheat variety on end-product (pasta) flavor.

Also, metabolomics has flourished when applied in tandem with genetic modification (GM) technologies, largely to verify the transformations and to assess the potential for unintended effects. Roessner et al. (2001) used GC-MS to study potato tubers with altered sucrose catabolism. In this instance, four  different potato genotypes were profiled: the cultivar Desiree (WT plants) and Desiree plants overexpressing a yeast invertase (which catalyzes the hydrolysis of sucrose to glucose and fructose)  in the cytosol (INV plants), a yeast invertase in combination with a bacterial glucokinase (which specifically catalyzes the phosphorylation of glucose) in the cytosol (GK3 plants), or a bacterial sucrose phosphorylase (which catalyzes the phosphorylytic cleavage of sucrose to glucose-1-phosphate and fructose) in the cytosol (SP plants). Metabolic profiling studies of these lines confirmed that all lines with enhanced sucrose mobilization showed a massive increase in metabolites of glycolytic and respiratory metabolism. While wild-type (WT) samples constituted a single cluster, which was distinct from SP, INV, and GK3 samples (Figure 1), the transgenic lines were quite distinct from one another. These results support the earlier conclusion that caution is required when interpreting biological events using a limited number of parameters. In this study, only apoINV plants did not show a clear separation from the wild type (Figure 1; Roessner et al. 2001).

Figure 1  Phenotyping of modified plant systems by metabolic profiling. The metabolic complement of these systems is analyzed by principal component analysis and is represented two-dimensionally based on the maximum differences between the metabolite contents of the various genetically modified potato tubers. Therefore, samples displaying similar metabolite complements are very close graphically, whereas those that are very distinct are very distant. (Reproduced from Roessner et al. 2001, with kind permission of the publisher. © American Society of Plant Biologists.)

Significant effort has gone into exploiting metabolomics in tomato, which is a model system for fruit ripening and the associated biological processes, by functional genomics analysis and metabolic engineering (Fernie and Schauer 2009, Osorio et al. 2009). For example, researchers used 1H NMR to follow changes in organoleptic and nutritional quality of greenhouse-grown tomato fruit and found that nutrient solution recycling had very little effect on fruit composition (Deborde et al. 2009).

From Diagnostics to Systems Biology

Several recent studies have illustrated the utility of combining data from metabolomics with that from other genomics platforms to provide new insights into both gene annotation (Goossens et al. 2003, Achnine et al. 2005, Fridman et al. 2005, Hagel et al. 2008) and regulation in complex biological systems (Urbanczyk-Wochniak et al. 2003, Hirai et al. 2004, Alba et al. 2005, Osorio et al. 2011a, Osorio et al. 2012). These approaches have resulted in the identification of numerous candidate genes, including several whose expression correlated strongly with the levels of metabolites with important nutritional or organoleptic properties. In this vein, a study of metabolite–transcript correlation in the tomato high pigment (hp1) mutant during ripening made it possible to identify transcription factors that might be important for fruit ripening. In addition, coexpression network analysis suggested some key transcription factor genes that are involved in photosynthesis and fruit development (Rohrmann et al. 2012).

Similarly, a combination of metabolomic, proteomic, and expression profiling of several tomato ripening mutants (Osorio et al. 2011a) during fruit development and ripening uncovered new aspects of metabolic regulation during tomato ripening. First, there was frequently only a weak correlation between the expression levels of a transcript and the abundance of its corresponding protein during early ripening, suggesting that posttranscriptional regulatory mechanisms play an important role in these stages; however, this correlation was much greater at later stages. Second, there were very strong correlations between ripening-associated transcripts and specific metabolite groups, such as organic acids, sugars, and cell wall–related metabolites. These results further revealed multiple ethylene-associated events during tomato ripening, providing new insights into the molecular biology of ethylene-mediated ripening regulatory networks (Osorio et al. 2011a).

In plants, several genes have been annotated on the basis of correlations between transcript and metabolite levels or simply of altered metabolite profiles. So far, these approaches have proved more informative in the study of secondary metabolism: for example, clear annotations were possible for genes associated with the isoflavonoid and triterpenoid pathways of Medicago truncatula (Achnine et al. 2005, Suzuki et al. 2005), the methylketone pathway of tomato (Fridman et al. 2005), pyridine alkaloid biosynthesis in tobacco (Goossens et al. 2003), and glucosinolate, flavonoid, and sterol biosynthesis in Arabidopsis (Hirai et al. 2004, Tohge et al. 2005, Morikawa et al. 2006). In these examples, altered metabolite profiles could be correlated to specific genes, and heterologous overexpression could then be used to prove gene function. Although the above examples are based mainly on loss-of-function analysis, metabolite profiling has also been used in conjuction with gain-of-function analysis. For example, when a gene of known function, a member of the threonine aldolase family, was introduced into Arabidopsis thaliana and characterized by metabolite profiling, the results confirmed the expected function, but also showed novel regulatory effects (Fernie et al. 2004).

The approach of focusing on individual genes can easily be extended to explore the phenotypic importance of genome regions. Recently, GC-MS was used to profile leaves and mature fruit of breeding populations of wild tomato species (Schauer et al. 2005). Changes in metabolite content were identified in these species that are potentially important with respect to both stress responses and nutritional benefits. Subsequently, metabolite profiling was used to identify pericarp metabolite quantitative trait loci (QTL) (Schauer et al. 2006) in a set of well-characterized tomato introgression lines of the wild species S. pennellii (ILs) (Eshed and Zamir 1995). This study resulted in the identification of 889 QTL that were stable over two independent harvests and governed the accumulation of 74 metabolites, including important primary metabolites, such as sugars, organic acids, essential amino acids, intermediate metabolites, and vitamins. Furthermore, many of the metabolites for which QTL were detected are important nutritionally, agronomically or organoleptically; it has therefore been a goal of modern plant breeding to screen wild genetic resources that could be introduced into modern varieties to improve specific traits. To follow up this work, heritability of these traits was investigated by analyzing an additional year´s harvest and evaluating the metabolite profiles of lines heterozygous for the introgression (ILHs) (Schauer et al. 2008). These studies revealed that most of the metabolic QTL (174 of 332) were dominantly inherited, with relatively high proportions of additively (61 of 332) or recessively (80 of 332) inherited QTL and a negligible number of QTL displaying overdominant inheritance. Interestingly, the mode of inheritance was quantitatively different between diverse classes of compounds (i.e., between sugars and acids), whereas several metabolite pairs belonging to the same pathway displayed a similar mode of inheritance at the same chromosomal loci. However, evaluation of the association between morphological and metabolic traits in the ILHs revealed that this correlation was far less prominent than in the ILs. Thus, the possibility of uncoupling enhanced metabolite content from any penalties with respect to plant performance and fecundity could prove an important advance in the use of genomics-driven breeding approaches.

To summarize, the integration of metabolite profiling with other genomics tools is starting to prove very effective in gene-functional annotation and identification of candidate genes for biotechnology and/or breeding strategies.

Conclusions and Perspectives

Current technologies employed for metabolic profiling clearly are showing remarkable value for comprehensive phenotyping strategies and for functional genomics. The recent paradigm shift in biology research towards systems biology (Appleby et al. 2009) has increased the use of “omic technologies,” with metabolomics playing a key part because it characterizes crop metabolic end points. Although current protocols do not cover the full metabolite complement of the plant cell, improvements in the coverage of metabolomics techniques have been made. An important component of metabolic regulation is compartmentation and specialization. However, organelles extracted from whole tissue homogenates generally originate from a range of cell types. Experimental evidence is now becoming available to suggest that there are distinct differences between organelles located in different tissues and cells. The availability of techniques to examine these differences routinely means that it should now be possible to study the function and interactions of individual organelles in the metabolic context of a particular cell environment. With the combination of next-generation technologies (Varshney et al. 2009) and isotope labeling applied to metabolite profiling, it would appear likely that an abundance of important information concerning the regulation of primary metabolism will become accessible (Tohge and Fernie 2012). Furthermore, the development and combination of many analytical techniques will allow a full description of the metabolome status of a plant. When this is achieved, global analyses of RNA, protein, and metabolites will allow us to obtain a full picture of the complexity of the system under study.


Achnine, L., Huhman, D. V., Farag, M. A., Sumner, L. W., Blount, J. W., and Dixon, R. A. (2005) Genomics-based selection and functional characterization of triterpene glycosyltransferases from the model legume Medicago truncatula. Plant J. 41(6): 875–887.

Alba, R., Payton, P., Fei, Z., McQuinn, R., Debbie, P., Martin, G. B., Tanksley, S. D., and Giovannoni, J. J. (2005) Transcriptome and selected metabolite analyses reveal multiple points of ethylene control during tomato fruit development. Plant Cell 17(11): 2954–2965.

Ali, K., Maltese, F., Choi, Y. H., and Verpoorte, R. (2010) Metabolic constituents of grapevine and grape-derived products. Phytochem. Rev. 9(3): 357–378.

Appleby, N., Edwards, D., and Batley, J. (2009) New technologies for ultra-high throughput genotyping in plants. Methods Mol. Biol. 513: 19–39.

Beleggia, R., Platani, C., Spano, G., Monteleone, M., and Cattivilli, L. (2009) Metabolic profiling and analysis of volatile composition of durum wheat semolina and pasta. J. Cereal Sci. 49: 301–309.

Biais, B., Allwood, J. W., Deborde, C., Xu, Y., Maucourt, M., Beauvoit, B., Dunn, W. B., Jacob, D., Goodacre, R., Rolin, D., and Moing, A. (2009) 1H NMR, GC-EI-TOFMS, and data set correlation for fruit metabolomics: application to spatial metabolite analysis in melon. Anal. Chem. 81(8): 2884–2894.

Chikayama, E., Suto, M., Nishihara, T., Shinozaki, K., and Kikuchi, J. (2008) Systematic NMR analysis of stable isotope labeled metabolite mixtures in plant and animal systems: coarse grained views of metabolic pathways. PLoS One 3(11): e3805.

Clifford, M. N. (2000) Anthocyanins—nature, occurrence and dietary burden. J. Sci. Food Agric. 80: 1063–1072.

Deborde, C., Maucourt, M., Baldet, P., Bernillon, S., Biais, B., Talon, G., Ferrand, C., Jacob, D., Ferry-Dumazet, H., de Daruvar, A., and Moing, A. (2009) Proton NMR quantitative profiling for 81 quality assessment of greenhouse-grown tomato fruit. Metabolomics 5: 183–198.

Eisenreich, W., and Bacher, A. (2007) Advances of high-resolution NMR techniques in the structural and metabolic analysis of plant biochemistry. Phytochemistry 68(22–24): 2799–2815.

Eshed, Y., and Zamir, D. (1995) An introgression line population of Lycopersicon pennellii in the cultivated tomato enables the identification and fine mapping of yield-associated QTL. Genetics 141(3): 1147–1162.

Fait, A., Hanhineva, K., Beleggia, R., Dai, N., Rogachev, I., Nikiforova, V. J., Fernie, A. R., and Aharoni, A. (2008) Reconfiguration of the achene and receptacle metabolic networks during strawberry fruit development. Plant Physiol. 148(2): 730–750.

Fernie, A. R., and Schauer, N. (2009) Metabolomics-assisted breeding: a viable option for crop improvement? Trends Genet. 25(1): 39–48.

Fernie, A. R., Trethewey, R. N., Krotzky, A. J., and Willmitzer, L. (2004) Metabolite profiling: from diagnostics to systems biology. Nat. Rev. Mol. Cell Biol. 5(9): 763–769.

Fiehn, O. (2002) Metabolomics—the link between genotypes and phenotypes. Plant Mol. Biol. 48(1-2): 155–171.

Fiehn, O., Kopka, J., Dormann, P., Altmann, T., Trethewey, R. N., and Willmitzer, L. (2000) Metabolite profiling for plant functional genomics. Nat. Biotechnol. 18(11): 1157–1161.

Fridman, E., Wang, J., Iijima, Y., Froehlich, J. E., Gang, D. R., Ohlrogge, J., and Pichersky, E. (2005) Metabolic, genomic, and biochemical analyses of glandular trichomes from the wild tomato species Lycopersicon hirsutum identify a key enzyme in the biosynthesis of methylketones. Plant Cell 17(4): 1252–1267.

Goossens, A., Hakkinen, S. T., Laakso, I., Seppanen-Laakso, T., Biondi, S., De Sutter, V., Lammertyn, F., Nuutila, A. M., Soderlund, H., Zabeau, M., Inze, D., and Oksman-Caldentey, K. M. (2003) A functional genomics approach toward the understanding of secondary metabolism in plant cells. Proc. Natl. Acad. Sci. USA 100(14): 8595–8600.

Grimplet, J., Cramer, G. R., Dickerson, J. A., Mathiason, K., Van Hemert, J., and Fennell, A. Y. (2009) VitisNet: "Omics" integration through grapevine molecular networks. PloS One 4(12): e8365.

Hagel, J. M., Weljie, A. M., Vogel, H. J., and Facchini, P. J. (2008) Quantitative 1H nuclear magnetic resonance metabolite profiling as a functional genomics platform to investigate alkaloid biosynthesis in opium poppy. Plant Physiol. 147(4): 1805–1821.

Hirai, M. Y., Yano, M., Goodenowe, D. B., Kanaya, S., Kimura, T., Awazuhara, M., Arita, M., Fujiwara, T., and Saito, K. (2004) Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proc. Natl. Acad. Sci. U S A 101(27): 10205–10210.

Iijima, Y., Nakamura, Y., Ogata, Y., Tanaka, K., Sakurai, N., Suda, K., Suzuki, T., Suzuki, H., Okazaki, K., Kitayama, M., Kanaya, S., Aoki, K., and Shibata, D. (2008) Metabolite annotations based on the integration of mass spectral information. Plant J. 54(5): 949–962.

Kim, H. K., Choi, Y. H., and Verpoorte, R. (2011) NMR-based plant metabolomics: where do we stand, where do we go? Trends Biotechnol. 29(6): 267–275.

Lombardo, V. A., Osorio, S., Borsani, J., Lauxmann, M. A., Bustamante, C. A., Budde, C. O., Andreo, C. S., Lara, M. V., Fernie, A. R., and Drincovich, M. F. (2011) Metabolic profiling during peach fruit development and ripening reveals the metabolic networks that underpin each developmental stage. Plant Physiol. 157(4): 1696–1710.

Ludwig, C., and Viant, M. R. (2010) Two-dimensional J-resolved NMR spectroscopy: review of a key methodology in the metabolomics toolbox. Phytochem. Anal. 21(1): 22–32.

McDougall, G., Martinussen, I., and Stewart, D. (2008) Towards fruitful metabolomics: high throughput analyses of polyphenol composition in berries using direct infusion mass spectrometry. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 871(2): 362–369.

Mintz-Oron, S., Mandel, T., Rogachev, I., Feldberg, L., Lotan, O., Yativ, M., Wang, Z., Jetter, R., Venger, I., Adato, A., and Asaph, A. (2008) Gene expression and metabolism in tomato fruit surface tissues. Plant Physiol. 147(2): 823–851.

Moco, S., Capanoglu, E., Tikunov, Y., Bino, R. J., Boyacioglu, D., Hall, R. D., Vervoort, J., and De Vos, R. C. (2007) Tissue specialization at the metabolite level is perceived during the development of tomato fruit. J. Exp. Bot. 58(15–16): 4131–4146.

Morikawa, T., Mizutani, M., Aoki, N., Watanabe, B., Saga, H., Saito, S., Oikawa, A., Suzuki, H., Sakurai, N., Shibata, D., Wadano, A., Sakata, K., and Ohta, D. (2006) Cytochrome P450 CYP710A encodes the sterol C-22 desaturase in Arabidopsis and tomato. Plant Cell 18: 1008–1022.

Osorio, S., Tohge, T., and Fernie, A. (2009) Application of metabolomic profiling for identifying valuable traits in tomato. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 4: 024.

Osorio, S., Alba, R., Damasceno, C. M., Lopez-Casado, G., Lohse, M., Zanor, M. I., Tohge, T., Usadel, B., Rose, J. K., Fei, Z., Giovannoni, J. J., and Fernie, A. R. (2011a) Systems biology of tomato fruit development: combined transcript, protein, and metabolite analysis of tomato transcription factor (nor, rin) and ethylene receptor (Nr) mutants reveals novel regulatory interactions. Plant Physiol. 157(1): 405–425.

Osorio, S., Bombarely, A., Giavalisco, P., Usadel, B., Stephens, C., Araguez, I., Medina-Escobar, N., Botella, M. A., Fernie, A. R., and Valpuesta, V. (2011b) Demethylation of oligogalacturonides by FaPE1 in the fruits of the wild strawberry Fragaria vesca triggers metabolic and transcriptional changes associated with defence and development of the fruit. J. Exp. Bot. 62(8): 2855–2873.

Osorio, S., Alba, R., Nikoloski, Z., Kochevenko, A., Fernie, A. R., and Giovannoni, J. J. (2012) Integrative comparative analyses of transcript and metabolite profiles from pepper and tomato ripening and development stages uncovers species-specific patterns of network regulatory behavior. Plant Physiol. 159(4): 1713–1729.

Palama, T. L., Menard, P., Fock, I., Choi, Y. H., Bourdon, E., Govinden-Soulange, J., Bahut, M., Payet, B., Verpoorte, R., and Kodja, H. (2010) Shoot differentiation from protocorm callus cultures of Vanilla planifolia (Orchidaceae): proteomic and metabolic responses at early stage. BMC Plant Biol. 10: 82.

Roessner, U., Luedemann, A., Brust, D., Fiehn, O., Linke, T., Willmitzer, L., and Fernie, A. R. (2001) Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell 13(1): 11–29.

Roessner, U., Wagner, C., Kopka, J., Trethewey, R. N., and Willmitzer, L. (2000) Technical advance: simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. Plant J. 23(1): 131–142.

Rohrmann, J., McQuinn, R., Giovannoni, J., Fernie, A. R., and Tohge, T. (2012) Tissue specificity and differential expression of transcription factors in tomato provide hints of unique regulatory networks during fruit ripening. Plant Signal. Behav. 7(12): 1639–1647.

Rudell, D. R., Mattheis, J. P., and Curry, E. A. (2008) Prestorage ultraviolet-white light irradiation alters apple peel metabolome. J. Agric. Food Chem. 56(3): 1138–1147.

Rudell, D. R., Mattheis, J. P., and Hertog, M. L. (2009) Metabolomic change precedes apple superficial scald symptoms. J. Agric. Food Chem. 57(18): 8459–8466.

Schauer, N., Zamir, D., and Fernie, A.R. (2005) Metabolic profiling of leaves and fruit of wild species tomato: a survey of the Solanum lycopersicum complex. J. Exp. Bot. 56(410): 297–307.

Schauer, N., Semel, Y., Roessner, U., Gur, A., Balbo, I., Carrari, F., Pleban, T., Perez-Melis, A., Bruedigam, C., Kopka, J., Willmitzer, L., Zamir, D., and Fernie, A.R. (2006) Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nature Biotech. 24(4): 447–454.

Schauer, N., Semel, Y., Balbo, I., Steinfath, M., Repsilber, D., Selbig, J., Pleban, T., Zamir, D., and Fernie, A. R. (2008) Mode of inheritance of primary metabolic traits in tomato. Plant Cell 20: 509–523.

Stewart, D., McDougall, G. J., Sungurtas, J., Verrall, S., Graham, J., and Martinussen, I. (2007) Metabolomic approach to identifying bioactive compounds in berries: advances toward fruit nutritional enhancement. Mol. Nutr. Food Res. 51(6): 645–651.

Suzuki, H., Reddy, M. S. S., Naoumkina, M., Aziz, N., May, G. D., Huhman, D. V., Summer, L. W., Blount, J. W., Mendes, P., and Dixon, R. A. (2005) Methyl jasmonate and yeast elicitor induce differential transcriptional and metabolic re-programming in cell suspension cultures of the model legume Medicago truncatula. Planta 220(5): 696–707.

Tohge, T., and Fernie, A. R. (2012) Annotation of plant gene function via combined genomics, metabolomics and informatics. J. Vis. Exp. (64): e3487.

Tohge, T., Nishiyama, Y., Hirai, M. Y., Yano, M., Nakajima, J., Awazuhara, M., Inoue, E., Takahashi, H., Goodenowe, D. B., Kitayama, M., Noji, M., Yamazaki, M., and Saito, K. (2005) Functional genomics by integrated analysis of metabolome and transcriptome of Arabidopsis plants over-expressing an MYB transcription factor. Plant J. 42(2): 218–235.

Tohge, T., Mettler, T., Arrivault, S., Carroll, A. J., Stitt, M., and Fernie, A. R. (2011) From models to crop species: caveats and solutions for translational metabolomics. Front. Plant Sci. 2: 61.

Urbanczyk-Wochniak, E., Luedemann, A., Kopka, J., Selbig, J., Roessner-Tunali, U., Willmitzer, L., and Fernie, A. R. (2003) Parallel analysis of transcript and metabolic profiles: a new approach in systems biology. EMBO Rep. 4(10): 989–993.

Varshney, R. K., Nayak, S. N., May, G. D., and Jackson, S. A. (2009) Next-generation sequencing technologies and their implications for crop genetics and breeding. Trends Biotech. 27(9): 522–530.

Witt, S., Galicia, L., Lisec, J., Cairns, J., Tiessen, A., Araus, J. L., Palacios-Rojas, N., and Fernie, A. R. (2012) Metabolic and phenotypic responses of greenhouse-grown maize hybrids to experimentally controlled drought stress. Mol. Plant 5(2): 401–417.

Zhang, J., Wang, X., Yu, O., Tang, J., Gu, X., Wan, X., and Fang, C. (2011) Metabolic profiling of strawberry (Fragaria x ananassa Duch.) during fruit development and maturation. J. Exp. Bot. 62(3): 1103–1118.