Foodomics
Foodomics is considered a subdiscipline of the four major types of omcs: genomics, proteomics, transcriptomics, and metabolomics. Genomics and proteomics arose in the 1990s from the introduction of high-throughput technologies for rapid DNA sequencing and comprehensive MS-based protein identification (, , ). This resulted in the development of microarrays that allowed the rapid and comprehensive characterization of gene expression, leading to the emergence of transcriptomics in the early 2000s (). These disciplines quickly precipitated the development of metabolomics. Metabolomics is the application of high-throughput analytical chemistry technologies (LC-MS, NMR, GC-MS) directed at characterizing the metabolome (i.e. the small molecules associated with metabolism) (, ). Though not as rapid or as high throughput as its omics cousins, metabolomics allows researchers to measure hundreds or even thousands of metabolites at a time, as opposed to only one or a few compounds. This new-found capacity to measure so many chemicals at once led to a number of metabolomic projects, aimed at identifying the metabolomes of microbes (), plants () and humans (, , ). These types of projects typically employ LC-MS, GC-MS, NMR or a combination of all 3 techniques to identify and/or quantify as many metabolites as possible in cells, tissues and biofluids of the organisms of interest. These comprehensive metabolomic studies were also complemented by a number of much more specific metabolomic studies aimed at characterizing the metabolic responses of humans to the intake of various foods or food constituents such as soy (), citrus fruits (), nuts (), meats () and tea ().
Metabolite levels are of particular biological importance because, like the canary in a coal mine, they are usually the first to respond to both internal physiological and external environmental changes in state. This makes them useful for various tasks, such as monitering and studying the body's immediate response to stimuli, developing biomarkers for early disease detection, and optimizing agricultural production and food safety.
- Galas DJ, McCormack SJ. An historical perspective on genomic technologies. Curr Issues Mol Biol 2003;5:123-7.
- Pettersson E, Lundeberg J, Ahmadian A. Generations of sequencing technologies. Genomics 2009;93:105-11.
- Patterson SD, Aebersold RH. Proteomics: the first decade and beyond. Nat Genet 2003;33:311-23.
- Devaux F, Marc P, Jacq C. Transcriptomes, transcription activators and microarrays. FEBS Lett 2001;498:140-4.
- Thomas GH. Metabolomics breaks the silence. Trends Microbiol 2001;9:158-.
- Nicholson JK, Lindon JC, Holmes E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999;29:1181-9.
- van der Werf MJ, Overkamp KM, Muilwijk B, Coulier L, Hankemeier T. Microbial metabolomics: toward a platform with full metabolome coverage. Anal Biochem 2007;370:17-25.
- Bais P, Moon SM, He K, Leitao R, Dreher K, Walk T, Sucaet Y, Barkan L, Wohlgemuth G, Roth MR, et al. PlantMetabolomics.org: A Web Portal for Plant Metabolomics Experiments. Plant Physiol 2010;152:1807-16.
- Wishart DS. Human Metabolome Database: completing the 'human parts list'. Pharmacogenomics 2007;8:683-6.
- Takeda I, Stretch C, Barnaby P, Bhatnager K, Rankin K, Fu H, Weljie A, Jha N, Slupsky C. Understanding the human salivary metabolome. NMR Biomed 2009;22:577-84.
- Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, Sinelnikov I, Krishnamurthy R, Eisner R, Gautam B, et al. The Human Serum Metabolome. PLoS ONE 2011;6:e16957.
- Solanky KS, Bailey NJ, Beckwith-Hall BM, Bingham S, Davis A, Holmes E, Nicholson JK, Cassidy A. Biofluid 1H NMR-based metabonomic techniques in nutrition research - metabolic effects of dietary isoflavones in humans. J Nutr Biochem 2005;16:236-44.
- Lloyd AJ, Beckmann M, Fave G, Mathers JC, Draper J. Proline betaine and its biotransformation products in fasting urine samples are potential biomarkers of habitual citrus fruit consumption. Br J Nutr 2011;106:812-24.
- Tulipani S, Llorach R, Jáuregui O, López-Uriarte P, Garcia-Aloy M, Bullo M, Salas-Salvadó J, Andrés-Lacueva C. Metabolomics Unveils Urinary Changes in Subjects with Metabolic Syndrome following 12-Week Nut Consumption. J Proteome Res 2011;10:5047-58.
- Cross AJ, Major JM, Sinha R. Urinary Biomarkers of Meat Consumption. Cancer Epidemiology Biomarkers & Prevention 2011;20:1107-11.
- Van Dorsten FA, Daykin CA, Mulder TPJ, Van Duynhoven JPM. Metabonomics approach to determine metabolic differences between green tea and black tea consumption. J Agric Food Chem 2006;54:6929-38.