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Mass Spectrometry-Based Profiling of Metabolites in Human Biofluids

  • Tanushree Chakraborty
  • Soumen Kanti MannaEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1928)

Abstract

Cancer poses a daunting challenge to researchers and clinicians alike. Early diagnosis, accurate prognosis, and prediction of therapeutic response remain elusive in most types of cancer. In addition, lacunae in our understanding of cancer biology continue to hinder advancement of therapeutic strategies. Metabolic reprogramming has been identified as integral to pathogenesis and progression of the disease. Consequently, analysis of biofluid metabolome has emerged as a promising approach to further our understanding of disease biology as well as to identify cancer biomarkers. However, unbiased identification of robust and meaningful differences in metabolic signatures remains a non-trivial task. This chapter describes a generalized strategy for global metabolic profiling of human biofluids using ultra-performance liquid chromatography (UPLC) and mass spectrometry, which together offer a sensitive, high-throughput, and versatile platform. A step-by-step protocol for performing untargeted metabolic profiling of urine and serum (or plasma), using hydrophilic interaction liquid chromatography (HILIC) or reverse-phase (RP) chromatography coupled with electrospray ionization mass spectrometry (ESI-MS) to multivariate data analysis and identification of metabolites of interest has been detailed.

Key words

Urine Serum Plasma Metabolomics Untargeted profiling HILIC RP UPLC-ESI-MS 

Notes

Acknowledgments

Authors would like to sincerely acknowledge the contribution of Mr. Kristopher W. Krausz in developing methods for UPLC-ESIMS analysis and Dr. Frank J. Gonzalez (Laboratory of Metabolism, Center for Cancer Research, National Cancer Institute, Bethesda, USA) for his encouragement and support. This work was supported by Saha Institute of Nuclear Physics, Kolkata, India.

References

  1. 1.
    Warburg O (1956) On the origin of cancer cells. Science 123(3191):309–314CrossRefGoogle Scholar
  2. 2.
    Vander Heiden MG, Cantley LC, Thompson CB (2009) Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324(5930):1029–1033.  https://doi.org/10.1126/science.1160809CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Pavlova NN, Thompson CB The emerging hallmarks of cancer metabolism. Cell Metab 23(1):27–47.  https://doi.org/10.1016/j.cmet.2015.12.006CrossRefPubMedCentralGoogle Scholar
  4. 4.
    Hsu PP, Sabatini DM (2008) Cancer cell metabolism: Warburg and beyond. Cell 134(5):703–707.  https://doi.org/10.1016/j.cell.2008.08.021CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    DeBerardinis RJ, Mancuso A, Daikhin E, Nissim I, Yudkoff M, Wehrli S, Thompson CB (2007) Beyond aerobic glycolysis: transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. Proc Natl Acad Sci U S A 104(49):19345–19350.  https://doi.org/10.1073/pnas.0709747104CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Johnson C, Warmoes MO, Shen X, Locasale JW Epigenetics and cancer metabolism. Cancer Lett 356(2 Pt A):309–314.  https://doi.org/10.1016/j.canlet.2013.09.043CrossRefGoogle Scholar
  7. 7.
    Kinnaird A, Zhao S, Wellen KE, Michelakis ED Metabolic control of epigenetics in cancer. Nat Rev Cancer 16(11):694–707.  https://doi.org/10.1038/nrc.2016.82CrossRefPubMedCentralGoogle Scholar
  8. 8.
    Ma S, Jiang B, Deng W, Gu ZK, Wu FZ, Li T, Xia Y, Yang H, Ye D, Xiong Y, Guan KL D-2-hydroxyglutarate is essential for maintaining oncogenic property of mutant IDH-containing cancer cells but dispensable for cell growth. Oncotarget 6(11):8606–8620.  https://doi.org/10.18632/oncotarget.3330
  9. 9.
    Dang L, White DW, Gross S, Bennett BD, Bittinger MA, Driggers EM, Fantin VR, Jang HG, Jin S, Keenan MC, Marks KM, Prins RM, Ward PS, Yen KE, Liau LM, Rabinowitz JD, Cantley LC, Thompson CB, Vander Heiden MG, Su SM (2009) Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462(7274):739–744.  https://doi.org/10.1038/nature08617CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Kerins MJ, Vashisht AA, Liang BX, Duckworth SJ, Praslicka BJ, Wohlschlegel JA, Ooi A Fumarate mediates a chronic proliferative signal in fumarate hydratase-inactivated cancer cells by increasing transcription and translation of ferritin genes. Mol Cell Biol 37(11). pii: e00079-17).  https://doi.org/10.1128/MCB.00079-17
  11. 11.
    Sciacovelli M, Goncalves E, Johnson TI, Zecchini VR, da Costa AS, Gaude E, Drubbel AV, Theobald SJ, Abbo SR, Tran MG, Rajeeve V, Cardaci S, Foster S, Yun H, Cutillas P, Warren A, Gnanapragasam V, Gottlieb E, Franze K, Huntly B, Maher ER, Maxwell PH, Saez-Rodriguez J, Frezza C Fumarate is an epigenetic modifier that elicits epithelial-to-mesenchymal transition. Nature 537(7621):544–547.  https://doi.org/10.1038/nature19353CrossRefPubMedCentralGoogle Scholar
  12. 12.
    Selak MA, Armour SM, MacKenzie ED, Boulahbel H, Watson DG, Mansfield KD, Pan Y, Simon MC, Thompson CB, Gottlieb E (2005) Succinate links TCA cycle dysfunction to oncogenesis by inhibiting HIF-alpha prolyl hydroxylase. Cancer Cell 7(1):77–85.  https://doi.org/10.1016/j.ccr.2004.11.022CrossRefPubMedGoogle Scholar
  13. 13.
    Yang M, Pollard PJ Succinate: a new epigenetic hacker. Cancer Cell 23(6):709–711.  https://doi.org/10.1016/j.ccr.2013.05.015CrossRefPubMedCentralGoogle Scholar
  14. 14.
    Chan EC, Koh PK, Mal M, Cheah PY, Eu KW, Backshall A, Cavill R, Nicholson JK, Keun HC (2009) Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). J Proteome Res 8(1):352–361.  https://doi.org/10.1021/pr8006232CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Denkert C, Budczies J, Kind T, Weichert W, Tablack P, Sehouli J, Niesporek S, Konsgen D, Dietel M, Fiehn O (2006) Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors. Cancer Res 66(22):10795–10804.  https://doi.org/10.1158/0008-5472.CAN-06-0755CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Hirayama A, Kami K, Sugimoto M, Sugawara M, Toki N, Onozuka H, Kinoshita T, Saito N, Ochiai A, Tomita M, Esumi H, Soga T (2009) Quantitative metabolome profiling of colon and stomach cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry. Cancer Res 69(11):4918–4925.  https://doi.org/10.1158/0008-5472.CAN-08-4806CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Rocha CM, Barros AS, Gil AM, Goodfellow BJ, Humpfer E, Spraul M, Carreira IM, Melo JB, Bernardo J, Gomes A, Sousa V, Carvalho L, Duarte IF Metabolic profiling of human lung cancer tissue by 1H high resolution magic angle spinning (HRMAS) NMR spectroscopy. J Proteome Res 9(1):319–332.  https://doi.org/10.1021/pr9006574CrossRefPubMedCentralGoogle Scholar
  18. 18.
    Roig B, Rodriguez-Balada M, Samino S, Lam EW, Guaita-Esteruelas S, Gomes AR, Correig X, Borras J, Yanes O, Guma J Metabolomics reveals novel blood plasma biomarkers associated to the BRCA1-mutated phenotype of human breast cancer. Sci Rep 7(1):17831.  https://doi.org/10.1038/s41598-017-17897-8
  19. 19.
    Jove M, Collado R, Quiles JL, Ramirez-Tortosa MC, Sol J, Ruiz-Sanjuan M, Fernandez M, de la Torre Cabrera C, Ramirez-Tortosa C, Granados-Principal S, Sanchez-Rovira P, Pamplona R A plasma metabolomic signature discloses human breast cancer. Oncotarget 8(12):19522–19533.  https://doi.org/10.18632/oncotarget.14521
  20. 20.
    Yonezawa K, Nishiumi S, Kitamoto-Matsuda J, Fujita T, Morimoto K, Yamashita D, Saito M, Otsuki N, Irino Y, Shinohara M, Yoshida M Nibu K Serum and tissue metabolomics of head and neck cancer. Cancer Genomics Proteomics 10(5):233–238Google Scholar
  21. 21.
    Nishiumi S, Kobayashi T, Ikeda A, Yoshie T, Kibi M, Izumi Y, Okuno T, Hayashi N, Kawano S, Takenawa T, Azuma T, Yoshida M A novel serum metabolomics-based diagnostic approach for colorectal cancer. PLoS One 7(7):e40459.  https://doi.org/10.1371/journal.pone.0040459CrossRefPubMedCentralGoogle Scholar
  22. 22.
    Hadi NI, Jamal Q, Iqbal A, Shaikh F, Somroo S, Musharraf SG Serum metabolomic profiles for breast cancer diagnosis, grading and staging by gas chromatography-mass spectrometry. Sci Rep 7(1):1715.  https://doi.org/10.1038/s41598-017-01924-9
  23. 23.
    Farshidfar F, Weljie AM, Kopciuk K, Buie WD, Maclean A, Dixon E, Sutherland FR, Molckovsky A, Vogel HJ, Bathe OF Serum metabolomic profile as a means to distinguish stage of colorectal cancer. Genome Med 4 (5):42. doi: https://doi.org/10.1186/gm341CrossRefPubMedCentralGoogle Scholar
  24. 24.
    Asiago VM, Alvarado LZ, Shanaiah N, Gowda GA, Owusu-Sarfo K, Ballas RA, Raftery D Early detection of recurrent breast cancer using metabolite profiling. Cancer Res 70(21):8309–8318.  https://doi.org/10.1158/0008-5472.CAN-10-1319CrossRefPubMedCentralGoogle Scholar
  25. 25.
    Ganti S, Weiss RH Urine metabolomics for kidney cancer detection and biomarker discovery. Urol Oncol 29(5):551–557.  https://doi.org/10.1016/j.urolonc.2011.05.013CrossRefPubMedCentralGoogle Scholar
  26. 26.
    Haznadar M, Cai Q, Krausz KW, Bowman ED, Margono E, Noro R, Thompson MD, Mathe EA, Munro HM, Steinwandel MD, Gonzalez FJ, Blot WJ, Harris CC Urinary metabolite risk biomarkers of lung cancer: a prospective cohort study. Cancer Epidemiol Biomarkers Prev 25(6):978–986.  https://doi.org/10.1158/1055-9965.EPI-15-1191CrossRefGoogle Scholar
  27. 27.
    Kind T, Tolstikov V, Fiehn O, Weiss RH (2007) A comprehensive urinary metabolomic approach for identifying kidney cancerr. Anal Biochem 363(2):185–195.  https://doi.org/10.1016/j.ab.2007.01.028CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Mathe EA, Patterson AD, Haznadar M, Manna SK, Krausz KW, Bowman ED, Shields PG, Idle JR, Smith PB, Anami K, Kazandjian DG, Hatzakis E, Gonzalez FJ, Harris CC Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer. Cancer Res 74(12):3259–3270.  https://doi.org/10.1158/0008-5472.CAN-14-0109CrossRefPubMedCentralGoogle Scholar
  29. 29.
    Patel D, Thompson MD, Manna SK, Krausz KW, Zhang L, Nilubol N, Gonzalez FJ, Kebebew E Unique and novel urinary metabolomic features in malignant versus benign adrenal neoplasms. Clin Cancer Res 23(17):5302–5310.  https://doi.org/10.1158/1078-0432.CCR-16-3156CrossRefPubMedCentralGoogle Scholar
  30. 30.
    Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J, Laxman B, Mehra R, Lonigro RJ, Li Y, Nyati MK, Ahsan A, Kalyana-Sundaram S, Han B, Cao X, Byun J, Omenn GS, Ghosh D, Pennathur S, Alexander DC, Berger A, Shuster JR, Wei JT, Varambally S, Beecher C, Chinnaiyan AM (2009) Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457(7231):910–914.  https://doi.org/10.1038/nature07762CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Ishikawa S, Sugimoto M, Kitabatake K, Sugano A, Nakamura M, Kaneko M, Ota S, Hiwatari K, Enomoto A, Soga T, Tomita M, Iino M Identification of salivary metabolomic biomarkers for oral cancer screening. Sci Rep 6:31520.  https://doi.org/10.1038/srep31520
  32. 32.
    Xu X, Cheng S, Ding C, Lv Z, Chen D, Wu J, Zheng S Identification of bile biomarkers of biliary tract cancer through a liquid chromatography/mass spectrometry-based metabolomic method. Mol Med Rep 11(3):2191–2198.  https://doi.org/10.3892/mmr.2014.2973CrossRefPubMedCentralGoogle Scholar
  33. 33.
    Locasale JW, Melman T, Song S, Yang X, Swanson KD, Cantley LC, Wong ET, Asara JM Metabolomics of human cerebrospinal fluid identifies signatures of malignant glioma. Mol Cell Proteomics 11(6):M111.014688.  https://doi.org/10.1074/mcp.M111.014688CrossRefGoogle Scholar
  34. 34.
    Armitage EG, Barbas C Metabolomics in cancer biomarker discovery: current trends and future perspectives. J Pharm Biomed Anal 87:1–11.  https://doi.org/10.1016/j.jpba.2013.08.041CrossRefPubMedCentralGoogle Scholar
  35. 35.
    Kim YS, Maruvada P, Milner JA (2008) Metabolomics in biomarker discovery: future uses for cancer prevention. Future Oncol 4(1):93–102.  https://doi.org/10.2217/14796694.4.1.93CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Ward PS, Patel J, Wise DR, Abdel-Wahab O, Bennett BD, Coller HA, Cross JR, Fantin VR, Hedvat CV, Perl AE, Rabinowitz JD, Carroll M, Su SM, Sharp KA, Levine RL, Thompson CB The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting alpha-ketoglutarate to 2-hydroxyglutarate. Cancer Cell 17(3):225–234.  https://doi.org/10.1016/j.ccr.2010.01.020CrossRefPubMedCentralGoogle Scholar
  37. 37.
    Tomlinson IP, Alam NA, Rowan AJ, Barclay E, Jaeger EE, Kelsell D, Leigh I, Gorman P, Lamlum H, Rahman S, Roylance RR, Olpin S, Bevan S, Barker K, Hearle N, Houlston RS, Kiuru M, Lehtonen R, Karhu A, Vilkki S, Laiho P, Eklund C, Vierimaa O, Aittomaki K, Hietala M, Sistonen P, Paetau A, Salovaara R, Herva R, Launonen V, Aaltonen LA (2002) Germline mutations in FH predispose to dominantly inherited uterine fibroids, skin leiomyomata and papillary renal cell cancer. Nat Genet 30(4):406–410.  https://doi.org/10.1038/ng849CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Manna SK, Tanaka N, Krausz KW, Haznadar M, Xue X, Matsubara T, Bowman ED, Fearon ER, Harris CC, Shah YM, Gonzalez FJ Biomarkers of coordinate metabolic reprogramming in colorectal tumors in mice and humans. Gastroenterology 146(5):1313–1324.  https://doi.org/10.1053/j.gastro.2014.01.017CrossRefPubMedCentralGoogle Scholar
  39. 39.
    Tautenhahn R, Cho K, Uritboonthai W, Zhu Z, Patti GJ, Siuzdak G An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol 30(9):826–828.  https://doi.org/10.1038/nbt.2348CrossRefPubMedCentralGoogle Scholar
  40. 40.
    Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vazquez-Fresno R, Sajed T, Johnson D, Li C, Karu N, Sayeeda Z, Lo E, Assempour N, Berjanskii M, Singhal S, Arndt D, Liang Y, Badran H, Grant J, Serra-Cayuela A, Liu Y, Mandal R, Neveu V, Pon A, Knox C, Wilson M, Manach C, Scalbert A HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res 46(D1):D608–D617.  https://doi.org/10.1093/nar/gkx1089CrossRefGoogle Scholar
  41. 41.
    Suhre K, Schmitt-Kopplin P (2008) MassTRIX: mass translator into pathways. Nucleic Acids Res 36(Web Server):W481–W484.  https://doi.org/10.1093/nar/gkn194CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Xia J, Wishart DS Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Curr Protoc Bioinformatics 55:14.10.11–14.10.91.  https://doi.org/10.1002/cpbi.11CrossRefPubMedCentralGoogle Scholar
  43. 43.
    Agustsson T, Ryden M, Hoffstedt J, van Harmelen V, Dicker A, Laurencikiene J, Isaksson B, Permert J, Arner P (2007) Mechanism of increased lipolysis in cancer cachexia. Cancer Res 67(11):5531–5537.  https://doi.org/10.1158/0008-5472.CAN-06-4585CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Arner P, Langin D Lipolysis in lipid turnover, cancer cachexia, and obesity-induced insulin resistance. Trends Endocrinol Metab 25(5):255–262.  https://doi.org/10.1016/j.tem.2014.03.002CrossRefGoogle Scholar
  45. 45.
    Ellis LD, Westerman MP (1965) Autoimmune hemolytic anemia and cancer. JAMA 193:962–964CrossRefPubMedCentralGoogle Scholar
  46. 46.
    Dieterle F, Ross A, Schlotterbeck G, Senn H (2006) Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal Chem 78(13):4281–4290.  https://doi.org/10.1021/ac051632cCrossRefPubMedGoogle Scholar
  47. 47.
    Matsubara T, Tanaka N, Krausz KW, Manna SK, Kang DW, Anderson ER, Luecke H, Patterson AD, Shah YM, Gonzalez FJ Metabolomics identifies an inflammatory cascade involved in dioxin- and diet-induced steatohepatitis. Cell Metab 16(5):634–644.  https://doi.org/10.1016/j.cmet.2012.10.006CrossRefPubMedCentralGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Biophysics and Structural Genomics DivisionSaha Institute of Nuclear Physics (HBNI)KolkataIndia

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