Perspective pubs.acs.org/JAFC
Targeted Metabolomics: a New Section in the Journal of Agricultural and Food Chemistry James N. Seiber,† Russell J. Molyneux,§ and Peter Schieberle*,# †
4143 Meyer Hall, Department of Environmental Toxicology, University of California, Davis, California 95616, United States Daniel K. Inouye College of Pharmacy of Hawaii at Hilo, 34 Rainbow Drive, Hilo, Hawaii 96720, United States # Deutsche Forschungsanstalt für Lebensmittelchemie, Lise-Meitner-Straße 34, 85354 Freising, Germany §
tarting in 2013, JAFC initiated a new section “Targeted Metabolomics Applied to Agriculture and Food” for manuscripts describing applications of metabolomics related to research topics in agriculture, food, and nutrition, in particular, metabolite-targeted analysis and progress in the development of analytical platforms for metabolomics methodologies. This description was expanded somewhat in the Journal’s Scope, Policy, and Instructions for authors (revised April 2013) with the additional statement: “A metabolome is the complete quantitative set of small molecules (metabolites) in a biological system, i.e., a food, which varies continuously with time. Also metabonomics studies, focused on changes in a given metabolome, e.g., induced by environmental conditions or diseases, fall into this category. Metabolic profiling or metabolomics fingerprinting correlated with multivariate or data-mining methods are acceptable, if presented in a targeted way”. This Perspective is meant to further explain the types of studies/topics that might fall within the scope of the new targeted metabolomics section of the Journal of Agricultural and Food Chemistry and to encourage authors to submit manuscripts to this section of JAFC. Due to the rapid development in the sensitivity and/or selectivity of analytical equipment, such as NMR, GC×GC-MS, or LC-MS (i.e., UHPLC-Q-TOF) as well as the option to handle large data sets by new software, metabolomics has developed and expanded largely in the past 15 years. A symposium, “Applications of Metabolomics in Agriculture”, was held at the American Chemical Society Spring meeting in 2005 covering information to that date, and a Perspective was subsequently published summarizing the content of the symposium, which included papers on understanding metabolic pathways, food and environmental safety, and nutrition, diet, and health.1 The Perspective particularly pointed out the challenges and opportunities which lay ahead for this exciting field. Metabolomics is a very promising approach of the postgenomics era aimed at facilitating our understanding of the dynamics of biochemical composition within living systems. It is fundamental to understanding systems biology in its efforts to integrate DNA, RNA, protein, and metabolite analyses in phenotypic, morphological, clinical, food, and other biological contexts. Metabolomics can also be quite useful as a guide to optimizing trait development in agricultural products2 or in improving food-processing conditions by comparing changes in the food’s metabolome.3 The analytical tools of most use in metabolomics (NMR spectroscopy, mass spectrometry, and others) can yield massive data sets when following changes in
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the metabolome of often hundreds of metabolites simultaneously over time, for example, in relation to nutritional status, environmental changes, etc. Thus, it provides a more complete picture of composition than generally seen with traditional plant biochemistry and natural product analyses. The literature differentiates between two concepts of metabolomics: untargeted and targeted methodologies. 4 Untargeted methods are intended to analyze as many compounds as possible of the entire set of chemical compounds in a given sample, that is, a food at different stages of fermentation/processing or a body fluid after ingestion of a certain diet, including unknowns. Usually the raw data on the analyzed set of molecules occurring in a minimum of two samples are compared and, after data alignment, compounds occurring in one of the samples are visualized by application of bioinformatics approaches, that is, dedicated software. The untargeted approach is, thus, unbiased and tries to differentiate/compare samples by statistical means, that is, chemometrics, to find discriminant metabolites whether the structure was known or is unknown. It must, however, be kept in mind that the set of compounds analyzed depends on several parameters, in particular, on the conditions of compound isolation from the insoluble matrix. Furthermore, a sufficient number of samples must have been analyzed to allow statistical calculations.5 In contrast, targeted metabolomic approaches aim at measuring defined groups of previously identified metabolites that are biochemically relevant. The targeted approach is designed to first confirm the structures of specific compounds by using reference compounds or reliable library data, and the “targeted” marker metabolites are then commonly compared on the basis of quantitative results, including concentration changes over time or condition. The number of analytes considered is usually high in both approaches, although target approaches must be limited to compounds representing, for example, food quality or that are markers of a biological phenomenon. A recent paper explains well the term metabolomics and the differences between a targeted and an untargeted approach.4 Nevertheless, there is a close relationship between the two approaches, because an untargeted approach should be done first to show differences in the set of analytes in the samples under investigation, that is, to help annotate those compounds Received: Revised: Accepted: Published: 22
October 15, 2013 December 5, 2013 December 9, 2013 December 20, 2013 dx.doi.org/10.1021/jf4046254 | J. Agric. Food Chem. 2014, 62, 22−23
Journal of Agricultural and Food Chemistry
Perspective
conventional potato crops. Proc. Natl. Acad. Sci. U.S.A. 2005, 102, 14458−14462. (3) Beleggia, R.; Platani, C.; Papa, R.; Di Chio, A.; Barros, E.; Mashaba, C.; Wirth, J.; Fammartino, A.; Sautter, C.; Conner, S.; Rauscher, J.; Stewart, D.; Cativelli, L. Metabolomics and food processing: from semolina to pasta. J. Agric. Food Chem. 2011, 59, 9366−9377. (4) Patti, G. J.; Yanes, O.; Siuzdak, G. Metabolomics: the apogee of the omics trilogy. Nat. Rev., Mol. Cell Biol. 2012, 13, 263−269. (5) Goodacre, R.; Broadhurst, D.; Smilde, A. K.; Kristal, B. S.; Baker, J. D.; Beger, R.; Bessant, C.; Connor, S.; Capuani, G.; Craig, A.; Ebbels, T.; Kell, D. B.; Manetti, C.; Newton, J.; Paternostro, G.; Somorjai, R.; Sjöström, M.; Trygg, J.; Wulfert, F. Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics 2007, 3, 231−241. (6) Cordell, G. A.; Shin, Y. G. Finding the needle in the haystack. The dereplication of natural products extracts. Pure Appl. Chem. 1999, 71, 1089−1094. (7) Bradshaw, J.; Butina, D.; Dunn, A. J.; Green, R. H.; Hajek, M.; Jones, M. M.; Lindon, J. C.; Sidebottom, P. J. A. Rapid and facile method for the dereplication of purified natural extracts. J. Nat Prod. 2001, 64, 1541−1544. (8) Molyneux, R. J.; Schieberle, P. Compound identification: a Journal of Agricultural and Food Chemistry perspective. J. Agric. Food Chem. 2007, 55, 4625−4629. (9) Schieberle, P.; Molyneux, R. J. Quantitation of sensory-active and bioactive constituents in food: a Journal of Agricultural and Food Chemistry perspective. J. Agric. Food Chem 2012, 60, 2404−2408. (10) Intelman, D.; Haseleu, G.; Dunkel, A.; Lagemann, A.; Stephan, A.; Hofmann, T. Comprehensive sensomics analysis of hop-derived bitter compounds during storage of beer. J. Agric. Food .Chem. 2011, 59, 1939−1953. (11) Vrhovsek, U.; Masuero, D.; Gasperotti, M.; Franceschi, P.; Caputi, L.; Viola, R.; Mattivi, F. A versatile targeted metabolomics method for the rapid quantification of multiple classes of phenolics in fruits and beverages. J. Agric. Food Chem. 2012, 60, 8831−8840. (12) Kim, H.-J.; Park, K.-J.; Lim, J.-H. Metabolomic analysis of phenolic compounds in buckwheat (Fagopyrum esculentum M.) sprouts treated with methyl jasmonate. J. Agric. Food Chem. 2011, 59, 5707− 5713. (13) Hanhinva, K.; Rogachev, I.; Aura, A.-M.; Aharoni, A.; Poutanen, K.; Mykkänen, H. Qualitative characterization of benzoxazinoid derivatives in whole grain rye and wheat by LC-MS metabolite profiling. J. Agric. Food Chem. 2011, 59, 921−927. (14) Kiefl, J.; Pollner, G.; Schieberle, P. Sensomics analysis of key hazelnut odorants (Corylus avellana L. ‘Tonda Gentile’) using comprehensive two-dimensional gas chromatography in combination with time-of-flight mass spectrometry (GC×GC-TOF-MS). J. Agric. Food Chem. 2013, 61, 5226−5235. (15) Fiehn, O.; Wohlgemuth, G.; Scholz, M.; Kind, T.; Lee, D. Y.; Lu, Y.; Moon, S.; Nikolau, B. Quality control for plant metabolomics: reporting MSI-Compliant studies. Plant J. 2008, 53, 691−704. (16) Salek, R. M.; Steinbeck, C.; Viant, M. R.; Goodacre, R.; Dunn, W. B. The role of reporting standards for metabolite annotation and identification in metabolomics studies. Gigascience 2013, 2, 13−14.
that are likely to be most relevant to addressing the research plan or question. If marker compounds are pinpointed from such “untargeted” comparisons, a library search must follow to establish whether a compound is already known. This process is commonly known as dereplication.6,7 If the structure is not yet described, the study must then be focused on the identification and quantitation of the marker compounds according to standards set forth in previous JAFC perspectives.8,9 However, whereas in conventional studies these standards require complete identification of compounds under investigation, it is obvious that in a targeted metabolomics study, dealing with tens or hundreds of compounds, it is almost impossible to conform to such requirements. Nevertheless, when claims are made for specific compounds as metabolic markers, such compounds should be fully characterized. In an ideal situation this will require that both NMR and MS techniques be concurrently applied (together with others that might be definitive), because the former tends to lack sensitivity and the latter, selectivity. Researchers who follow an untargeted approach are strongly encouraged to undertake further characterization of those compounds showing differences in given samples. JAFC will limit the section on targeted metabolomics to those manuscripts that at least attempt to characterize the differing compounds to draw substantive conclusions, for example, on pathways or mechanisms of formation or modification. An untargeted approach alone is primarily descriptive and may not be driven by a scientific concept and experimental design and, therefore, generally will not fall within JAFC’s scope. Because JAFC is primarily a chemistry journal, it is important for authors to include in their manuscripts examples of their spectroscopic/ spectrometric data sets, so that readers can evaluate the ability of the techniques to discriminate between the numerous compounds that may be present. JAFC has already published a number of good examples translating the metabolomics approach into food chemistry, which illustrate the breadth and depth of this field.1,3,10−14 Also, guidelines for minimum standards in conducting metabolomics studies published by, for example, the Metabolomics Standard Initiative (MSI) or the COSMOS (Coordination of Standards in Metabolomics), should be consulted.15,16 With the boundaries defined in this Perspective, we leave it to authors, reviewers, and readers of the Journal to populate and comment on the development of targeted metabolomics applicable to agricultural and food chemistry.
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AUTHOR INFORMATION
Corresponding Author
*(P.S.) Phone: +49 8161 712932. Fax: +49 8161 71 2970. Email:
[email protected]. Notes
The authors declare no competing financial interest.
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REFERENCES
(1) Dixon, R. A.; Gang, D. R.; Charlton, A. J.; Fiehn, O.; Kuiper, H. A.; Reynolds, T. L.; Tjeerdema, R. S.; Jeffery, E. H.; German, J. B.; Ridley, W. P.; Seiber, J. N. Applications of metabolomics in agriculture. J. Agric. Food Chem. 2006, 54, 8984−8994. (2) Catchpole, G. S.; Beckmann, M.; Enot, D. P.; Mondhe, M.; Zywicki, B.; Taylor, J.; Hardy, N.; Smith, A.; King, R. D.; Kell, D. B.; Fiehn, O.; Draper, J. Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and 23
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