Abstract
The basis of this review is to evaluate the field of metabolomics. The strategies used in this field will be explored to understand the process of biomarker discovery, especially those with clinical value, giving rise to personalized medicine. Metabolomics is the process of profiling metabolites in a biological system, due to this it has significant potential in decoding the ultimate product of the genomic processes. It is becoming increasingly clear that the field has possible limitations that is resolved by a potential metabolomic assay that has the ability to directly target a selection of metabolites combating the issue of variability amongst samples and allow for reproducible data. Recently, an increased effort has been made to formulate a universally accepted approach. Enabling the field to progress into to a more clinical-based setting. Diseases in a biological system tend to have a “signature” of sorts: a fluctuating metabolite profile is a representation of cellular activity. Monitoring such fluctuations allows for health care that accounts for external factors such as (i) lifestyle, (ii) environmental factors and (iii) genetic information in advance of treatment.
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References
Kaddurah-Daouk R, Weinshilboum R, Pharmacometabolomics Research (2015, July) Metabolomic signatures for drug response phenotypes: pharmacometabolomics enables precision medicine. Clin Pharmacol Ther 98(1):71–75. https://doi.org/10.1002/cpt.134
Johnson CH, Ivanisevic J, Siuzdak G (2016, July) Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 17(7):451–459. https://doi.org/10.1038/nrm.2016.25
Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI (2007, October 18) The human microbiome project. Nature 449(7164):804–810. https://doi.org/10.1038/nature06244
Riekeberg E, Powers R (2017) New frontiers in metabolomics: from measurement to insight. F1000Res 6:1148. https://doi.org/10.12688/f1000research.11495.1
ter Kuilea BH, Westerho HV (2001) Transcriptome meets metabolome hierarchical and metabolic regulation of the glycolytic pathway. FEBS Lett 500:169–171
Guo L et al (2015, September 1) Plasma metabolomic profiles enhance precision medicine for volunteers of normal health. Proc Natl Acad Sci U S A 112(35):E4901–E4910. https://doi.org/10.1073/pnas.1508425112
Duarte IF, Diaz SO, Gil AM (2014, May) NMR metabolomics of human blood and urine in disease research. J Pharm Biomed Anal 93:17–26. https://doi.org/10.1016/j.jpba.2013.09.025
Wang L, Liu X, Yang Q (2018) Application of metabolomics in cancer research: as a powerful tool to screen biomarker for diagnosis, monitoring and prognosis of cancer. Biom J 1(9). https://doi.org/10.21767/2472-1646.100050
Marshall DD, Powers R (2017, May) Beyond the paradigm: combining mass spectrometry and nuclear magnetic resonance for metabolomics. Prog Nucl Magn Reson Spectrosc 100:1–16. https://doi.org/10.1016/j.pnmrs.2017.01.001
Wiese S, Reidegeld KA, Meyer HE, Warscheid B (2007, February) Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteome research. Proteomics 7(3):340–350. https://doi.org/10.1002/pmic.200600422
Pan Z, Raftery D (2007, January) Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics. Anal Bioanal Chem 387(2):525–527. https://doi.org/10.1007/s00216-006-0687-8
Aslam B, Basit M, Nisar MA, Khurshid M, Rasool MH (2017, February) Proteomics: technologies and their applications. J Chromatogr Sci 55(2):182–196. https://doi.org/10.1093/chromsci/bmw167
Wolfender JL, Marti G, Thomas A, Bertrand S (2015, February 20) Current approaches and challenges for the metabolite profiling of complex natural extracts. J Chromatogr A 1382:136–164. https://doi.org/10.1016/j.chroma.2014.10.091
Tessitore A et al (2013) Serum biomarkers identification by mass spectrometry in high-mortality tumors. Int J Proteomics 2013:125858. https://doi.org/10.1155/2013/125858
Dettmer K (2007) Mass spectrometry-based metabolomics. Mass spectrometry review. NIH Public Access 26(1):51–78
de Antignac JP, Wasch K, De Monteau F, Brabander H, Andre F, Le Bizec B (2005) The ion suppression phenomenon in liquid chromatography–mass spectrometry and its consequences in the field of residue analysis. Anal Chim Acta 529(1–2):129–136. https://doi.org/10.1016/j.aca.2004.08.055
Kell DB (2004, June) Metabolomics and systems biology: making sense of the soup. Curr Opin Microbiol 7(3):296–307. https://doi.org/10.1016/j.mib.2004.04.012
Bingol K, Bruschweiler R (2015, September) Two elephants in the room: new hybrid nuclear magnetic resonance and mass spectrometry approaches for metabolomics. Curr Opin Clin Nutr Metab Care 18(5): 471–477. [Online]. Available https://www.ncbi.nlm.nih.gov/pubmed/26154280
Dumez JN et al (2015, September 7) Hyperpolarized NMR of plant and cancer cell extracts at natural abundance. Analyst 140(17):5860–5863. https://doi.org/10.1039/c5an01203a
Ardenkjær-Larsen JH (2003) Increase in signal-to-noise ratio of +10,000 times in liquid state NMR. PNAS 100(18):10158–10163
Mark CB, Does D, Allen P, Snyder R (1998) Multi-component T1 relaxation and magnetisation transfer in peripheral nerve. Magn Reson Imaging 16(9):1033–1041
Dutta P, Martinez GV, Gillies RJ (2013, September 1) A new horizon of DNP technology: application to in-vivo (13)C magnetic resonance spectroscopy and imaging. Biophys Rev 5(3):271–281. https://doi.org/10.1007/s12551-012-0099-2
Schroeder MA (2008) In vivo assessment of pyruvate dehydrogenase flux in the heart using hyperpolarized carbon-13 magnetic resonance. PNAS 105(33):12051–12056
Trivedi DK, Hollywood KA, Goodacre R (2017, March) Metabolomics for the masses: the future of metabolomics in a personalized world. New Horiz Transl Med 3(6):294–305. https://doi.org/10.1016/j.nhtm.2017.06.001
Misra BB (2018, April) New tools and resources in metabolomics: 2016–2017. Electrophoresis 39(7):909–923. https://doi.org/10.1002/elps.201700441
Karaman İ, Nørskov NP, Yde CC, Hedemann MS, Bach Knudsen KE, Kohler A (2014) Sparse multi-block PLSR for biomarker discovery when integrating data from LC–MS and NMR metabolomics. Metabolomics 11(2):367–379. https://doi.org/10.1007/s11306-014-0698-
Boccard J, Rutledge DN (2013, March 26) A consensus orthogonal partial least squares discriminant analysis (OPLS-DA) strategy for multiblock Omics data fusion. Anal Chim Acta 769:30–39. https://doi.org/10.1016/j.aca.2013.01.022
Broadhurst DI, Kell DB (2006) Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2(4):171–196. https://doi.org/10.1007/s11306-006-0037-z
Poste G (2011) Bring on the biomarkers. Nature, Comment 469: 156–157, online 12 Jan 2011. https://doi.org/10.1038/469156a
Bonham VL, Callier SL, Royal CD (2016) Will precision medicine move us beyond race? N Engl J Med 374(21):2003–2005. https://doi.org/10.1056/NEJMp1511294
Dunn WB et al (2015) Molecular phenotyping of a UK population: defining the human serum metabolome. Metabolomics 11:9–26. https://doi.org/10.1007/s11306-014-0707-1
O’Neill J (2016) Tackling drug-resistant infections globally. https://amr-review.org/sites/default/files/160518_Final%20paper_with%20cover.pdf
Trupp M et al (2012) Metabolomics reveals amino acids contribute to variation in response to simvastatin treatment. PLoS One 7(7):e38386. https://doi.org/10.1371/journal.pone.0038386
Armstrong AW et al (2014) Metabolomics in psoriatic disease: pilot study reveals metabolite differences in psoriasis and psoriatic arthritis. F1000Res 3(248):1–15. https://doi.org/10.12688/f1000research.4709.1
Sitter B (2013) Metabolic changes in psoriatic skin under topical corticosteroid treatment. BMC Dermatol 13(8):471–5945
Barabasi AL, Gulbahce N, Loscalzo J (2011, January) Network medicine: a network-based approach to human disease. Nat Rev Genet 12(1):56–68. https://doi.org/10.1038/nrg2918
Moeschler JB (2013) Chapter 37 – Neurodevelopmental disabilities: global developmental delay, intellectual disability, and autism. In: Rimoin D, Pyeritz R, Korf B (eds) Emery and Rimoin’s principles and practice of medical genetics. Academic, Oxford, pp 1–15
Costa e Silva JA (2013, January) Personalized medicine in psychiatry: new technologies and approaches. Metabolism 62(Suppl 1):S40–S44. https://doi.org/10.1016/j.metabol.2012.08.017
Lemay V, Hamet P, Hizel C, Lemarié É, Tremblay Y (2017) Chapter 16 – Personalized medicine: interdisciplinary perspective, world tidal wave, and potential growth for the emerging countries. In: Verma M, Barh D (eds) Progress and challenges in precision medicine. Academic, Amsterdam, pp 301–314
Worldometers. https://www.worldometers.info/world-population/. Accessed Aug 2019
Sarkar BK (2017) Big data for secure healthcare system: a conceptual design. Complex Intell Syst 3(2):133–151. https://doi.org/10.1007/s40747-017-0040-1
Park S, Chung K, Jayaraman S (2014) Chapter 1.1 – Wearables: fundamentals, advancements, and a roadmap for the future. In: Sazonov E, Neuman Eds MR (eds) Wearable sensors. Academic, Oxford, pp 1–23
Muoio D. Google and Alphabet’s 20 most ambitious moonshot projects. https://www.businessinsider.com/20-moonshot-projects-by-google-turned-alphabet-2016-2?r=US&IR=T
Long E et al (2017) An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat Biomed Eng, Article, 1: 0024, online 30 Jan 2017. https://doi.org/10.1038/s41551-016-0024., https://www.nature.com/articles/s41551-016-0024#supplementary-information
Crockett D (2016) Going beyond genomics. https://www.healthcatalyst.com/going-beyond-genomics-in-precision-medicine
Hollywood K, Brison DR, Goodacre R (2006, September) Metabolomics: current technologies and future trends. Proteomics 6(17):4716–4723. https://doi.org/10.1002/pmic.200600106
Auffray C, Chen Z, Hood L (2009, January 20) Systems medicine: the future of medical genomics and healthcare. Genome Med 1(2):1–11. https://doi.org/10.1186/gm2
Javitt G (2010) Which way for genetic-test regulation. Nature 466:817–818
Monteiro MS (2013) Metabolomics analysis for biomarker discovery. Curr Med Chem 20:257–271
Kaufman DJ, Baker R, Milner LC, Devaney S, Hudson KL (2016) A survey of U.S. adults’ opinions about conduct of a nationwide precision medicine initiative(R) cohort study of genes and environment. PLoS One 11(8):e0160461. https://doi.org/10.1371/journal.pone.0160461
NIH (2016) NIH funds biobank to support Precision Medicine Initiative Cohort Program. https://www.nih.gov/news-events/news-releases/nih-funds-biobank-support-precision-medicine-initiative-cohort-program#:~:text=As%20part%20of%20President%20Obama's,which%20aims%20to%20enroll%201
Beger RD et al (2016) Metabolomics enables precision medicine: “a white paper, community perspective”. Metabolomics 12(10):149–164. [Online]. Available: https://www.ncbi.nlm.nih.gov/pubmed/27642271
Armitage E, Barbas G (2014, January) Metabolomics in cancer biomarker discovery: current trends and future perspectives. J Pharm Biomed Anal 87:1–11. https://doi.org/10.1016/j.jpba.2013.08.041
Dhanasekaran SM (2001) Delineation of prognostic biomarkers in prostate cancer. Nature 412(6849):822–826
Pashayan N, Pharoah P (2012, June) Population-based screening in the era of genomics. Per Med 9(4):451–455. https://doi.org/10.2217/pme.12.40
Mathelin C, Cromer A, Wendling C, Tomasetto C, Rio MC (2006, March) Serum biomarkers for detection of breast cancers: a prospective study. Breast Cancer Res Treat 96(1):83–90. https://doi.org/10.1007/s10549-005-9046-2
Kim K, Taylor SL, Ganti S, Guo L, Osier MV, Weiss RH (2011, May) Urine metabolomic analysis identifies potential biomarkers and pathogenic pathways in kidney cancer. OMICS 15(5):293–303. https://doi.org/10.1089/omi.2010.0094
Bodhani A (2015) The connected body. Eng Technol 10(4):44–47
Moore HM, Compton CC, Lim MD, Vaught J, Christiansen KN, Alper J (2009, September 1) Biospecimen research network symposium: advancing cancer research through biospecimen science. Cancer Res 69(17):6770-2. https://doi.org/10.1158/0008-5472.CAN-09-1795
Lee SMC (2019) Metabolomic and genomic markers of atherosclerosis as related to oxidative stress, inflammation, and vascular function in twin astronauts. https://www.nasa.gov/twins-study/research
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Sherlock, L., Mok, K.H. (2021). Metabolomics and Its Applications to Personalized Medicine. In: Park, J.M., Whang, D.R. (eds) EKC 2019 Conference Proceedings. EKC 2019. Springer, Singapore. https://doi.org/10.1007/978-981-15-8350-6_3
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