Metabolomics

Metabolomics is the scientific study of chemical processes involving metabolites, the small molecule substrates, intermediates, and products of cell metabolism. Specifically, metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind", the study of their small-molecule metabolite profiles.^[1] The metabolome represents the complete set of metabolites in a biological cell, tissue, organ, or organism, which are the end products of cellular processes.^[2] Messenger RNA (mRNA), gene expression data, and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell,^[3] and thus, metabolomics provides a direct "functional readout of the physiological state" of an organism.^[4] There are indeed quantifiable correlations between the metabolome and the other cellular ensembles (genome, transcriptome, proteome, and lipidome), which can be used to predict metabolite abundances in biological samples from, for example mRNA abundances.^[5] One of the ultimate challenges of systems biology is to integrate metabolomics with all other -omics information to provide a better understanding of cellular biology.

^ Daviss B (April 2005). "Growing pains for metabolomics". The Scientist. 19 (8): 25–28. Archived from the original on 13 October 2008.
^ Jordan KW, Nordenstam J, Lauwers GY, Rothenberger DA, Alavi K, Garwood M, et al. (March 2009). "Metabolomic characterization of human rectal adenocarcinoma with intact tissue magnetic resonance spectroscopy". Diseases of the Colon and Rectum. 52 (3): 520–525. doi:10.1007/DCR.0b013e31819c9a2c. PMC 2720561. PMID 19333056.
^ Villate A, San Nicolas M, Gallastegi M, Aulas PA, Olivares M, Usobiaga A, et al. (February 2021). "Review: Metabolomics as a prediction tool for plants performance under environmental stress". Plant Science. 303: 110789. doi:10.1016/j.plantsci.2020.110789. PMID 33487364. S2CID 230533604.
^ Hollywood K, Brison DR, Goodacre R (September 2006). "Metabolomics: current technologies and future trends". Proteomics. 6 (17): 4716–4723. doi:10.1002/pmic.200600106. PMID 16888765. S2CID 14631544.
^ Cavicchioli MV, Santorsola M, Balboni N, Mercatelli D, Giorgi FM (March 2022). "Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines". International Journal of Molecular Sciences. 23 (7): 3867. doi:10.3390/ijms23073867. PMC 8998886. PMID 35409231.

[1] Daviss B (April 2005). "Growing pains for metabolomics". The Scientist. 19 (8): 25–28. Archived from the original on 13 October 2008.

[2] Jordan KW, Nordenstam J, Lauwers GY, Rothenberger DA, Alavi K, Garwood M, et al. (March 2009). "Metabolomic characterization of human rectal adenocarcinoma with intact tissue magnetic resonance spectroscopy". Diseases of the Colon and Rectum. 52 (3): 520–525. doi:10.1007/DCR.0b013e31819c9a2c. PMC 2720561. PMID 19333056.

[Villate_2021-3] Villate A, San Nicolas M, Gallastegi M, Aulas PA, Olivares M, Usobiaga A, et al. (February 2021). "Review: Metabolomics as a prediction tool for plants performance under environmental stress". Plant Science. 303: 110789. doi:10.1016/j.plantsci.2020.110789. PMID 33487364. S2CID 230533604.

[Hollywood_2006-4] Hollywood K, Brison DR, Goodacre R (September 2006). "Metabolomics: current technologies and future trends". Proteomics. 6 (17): 4716–4723. doi:10.1002/pmic.200600106. PMID 16888765. S2CID 14631544.

[5] Cavicchioli MV, Santorsola M, Balboni N, Mercatelli D, Giorgi FM (March 2022). "Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines". International Journal of Molecular Sciences. 23 (7): 3867. doi:10.3390/ijms23073867. PMC 8998886. PMID 35409231.

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