Identifying unknown metabolites using NMR-based metabolic profiling techniques

Abstract

Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardized methods for structure elucidation of candidate disease biomarkers. Here we describe a system for identifying molecular species derived from nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping studies, with detailed information on sample preparation, data acquisition and data modeling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM) and Resolution-Enhanced (RED)-STORM to identify other signals in the NMR spectra relating to the same molecule. It also uses two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multi-dimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take 2 or 3 days. This approach to biomarker discovery is efficient and cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. It requires a basic understanding of MATLAB to use the statistical spectroscopic tools and analytical skills to perform solid phase extraction (SPE), liquid chromatography (LC) fraction collection, LC-NMR-mass spectroscopy and one-dimensional and two-dimensional NMR experiments.

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Fig. 1: Overview of our system for metabolite identification based on a combination of analytical and statistical workflows.
Fig. 2: Results from a metabolome-wide association study (the INTERMAP epidemiological study) that identified ascorbic acid linked to BMI in human urine (samples analyzed by sequential 1D NMR, STOCSY, STORM, 2D NMR and spiking of standard).
Fig. 3: Results from a study that identified 2PY and 4PY in urine samples from C57BL/6 mice (samples analyzed by sequential 1D NMR, STOCSY, 2D NMR, SPE, 2D NMR and spiking of standard).
Fig. 4: Results from statistical spectroscopic strategies used for the identification of NAcSPCSO, a biomarker of onion intake, in human urine (samples analyzed by sequential 1D NMR, STORM, RED-STORM, LC-NMR-MS, 2D NMR and spiking of standard).
Fig. 5: Results from an LC-NMR-MS study that identified NAcSPCSO, a biomarker of onion intake, in human urine (samples analyzed by sequential LC-NMR-MS and 2D-NMR analysis).

Code availability

CA-PLS (and PLS, OSC-PLS): The code for executing the PLS, covariate-adjusted (O)PLS and simple orthogonal PLS/PLS-DA is provided in https://bitbucket.org/jmp111/capls/src/. This can be executed in a MATLAB environment.

STORM (and STOCSY): The code for executing both the STOCSY and STORM algorithms is provided in https://bitbucket.org/jmp111/storm/src. These can be executed in a MATLAB environment.

RED-STORM: The code for executing the RED-STORM algorithm is provided in https://bitbucket.org/jmp111/redstorm/src/. This can be executed in a MATLAB environment.

References

  1. 1.

    Holmes, E., Wilson, I. D. & Nicholson, J. K. Metabolic phenotyping in health and disease. Cell 134, 714–717 (2008).

    CAS  PubMed  Google Scholar 

  2. 2.

    Nicholson, J. K., Lindon, J. C. & Holmes, E. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29, 1181–1189 (1999).

    CAS  PubMed  Google Scholar 

  3. 3.

    Fiehn, O. Metabolomics - the link between genotypes and phenotypes. Plant Mol. Biol. 48, 155–171 (2002).

    CAS  PubMed  Google Scholar 

  4. 4.

    Nicholson, J. K. & Wilson, I. D. High-resolution proton magnetic-resonance spectroscopy of biological-fluids. Prog. Nucl. Mag. Res. Spectr. 21, 449–501 (1989).

    CAS  Google Scholar 

  5. 5.

    Nicholson, J. K. et al. Proton-nuclear-magnetic-resonance studies of serum, plasma and urine from fasting normal and diabetic subjects. Biochem. J. 217, 365–375 (1984).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Bales, J. R., Higham, D. P., Howe, I., Nicholson, J. K. & Sadler, P. J. Use of high-resolution proton nuclear magnetic resonance spectroscopy for rapid multi-component analysis of urine. Clin. Chem. 30, 426–432 (1984).

    CAS  PubMed  Google Scholar 

  7. 7.

    Wilson, I. D., Wade, K. E. & Nicholson, J. K. Analysis of biological-fluids by high-field nuclear magnetic-resonance spectroscopy. Trac Trend Anal. Chem. 8, 368–374 (1989).

    CAS  Google Scholar 

  8. 8.

    Belton, P. S. et al. Use of high-field H-1 NMR spectroscopy for the analysis of liquid foods. J. Agric. Food Chem. 44, 1483–1487 (1996).

    CAS  Google Scholar 

  9. 9.

    Cloarec, O. et al. Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic H-1 NMR data sets. Anal. Chem. 77, 1282–1289 (2005).

    CAS  PubMed  Google Scholar 

  10. 10.

    Posma, J. M. et al. Subset optimization by reference matching (STORM): an optimized statistical approach for recovery of metabolic biomarker structural information from 1H NMR spectra of biofluids. Anal. Chem. 84, 10694–10701 (2012).

    CAS  PubMed  Google Scholar 

  11. 11.

    Posma, J. M. et al. Integrated analytical and statistical two-dimensional spectroscopy strategy for metabolite identification: application to dietary biomarkers. Anal. Chem. 89, 3300–3309 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Nicholson, J. K., Foxall, P. J. D., Spraul, M., Farrant, R. D. & Lindon, J. C. 750-Mhz H-1 and H-1-C-13 Nmr-spectroscopy of human blood-plasma. Anal. Chem. 67, 793–811 (1995).

    CAS  PubMed  Google Scholar 

  13. 13.

    Beckonert, O. et al. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc. 2, 2692–2703 (2007).

    CAS  PubMed  Google Scholar 

  14. 14.

    Dona, A. C. et al. A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments. Comput. Struct. Biotechnol. J. 14, 135–153 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Godejohann, M., Tseng, L. H., Braumann, U., Fuchser, J. & Spraul, M. Characterization of a paracetamol metabolite using on-line LC-SPE-NMR-MS and a cryogenic NMR probe. J. Chromatogr. A 1058, 191–196 (2004).

    CAS  PubMed  Google Scholar 

  16. 16.

    Shockcor, J. P. et al. Combined HPLC, NMR spectroscopy, and ion-trap mass spectrometry with application to the detection and characterization of xenobiotic and endogenous metabolites in human urine. Anal. Chem. 68, 4431–4435 (1996).

    CAS  PubMed  Google Scholar 

  17. 17.

    Coles, S. J., Day, N. E., Murray-Rust, P., Rzepa, H. S. & Zhang, Y. Enhancement of the chemical semantic web through the use of InChI identifiers. Org. Biomol. Chem. 3, 1832–1834 (2005).

    CAS  PubMed  Google Scholar 

  18. 18.

    Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3, 211–221 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Wishart, D. S. Computational strategies for metabolite identification in metabolomics. Bioanalysis 1, 1579–1596 (2009).

    CAS  PubMed  Google Scholar 

  20. 20.

    Ellinger, J. J., Chylla, R. A., Ulrich, E. L. & Markley, J. L. Databases and software for NMR-based metabolomics. Curr. Metabol. https://doi.org/10.2174/2213235X11301010028 (2013).

  21. 21.

    Wishart, D. S. et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 37, D603–D610 (2009).

    CAS  PubMed  Google Scholar 

  22. 22.

    Ulrich, E. L. et al. BioMagResBank. Nucleic Acids Res. 36, D402–D408 (2008).

    CAS  PubMed  Google Scholar 

  23. 23.

    Akiyama, K. et al. PRIMe: a Web site that assembles tools for metabolomics and transcriptomics. Silico Biol. 8, 339–345 (2008).

    CAS  Google Scholar 

  24. 24.

    Wishart, D. S. Quantitative metabolomics using NMR. Trac Trend Anal. Chem. 27, 228–237 (2008).

    CAS  Google Scholar 

  25. 25.

    Simpson, A. J., McNally, D. J. & Simpson, M. J. NMR spectroscopy in environmental research: from molecular interactions to global processes. Prog. Nucl. Magn. Reson. Spectr. 58, 97–175 (2011).

    CAS  Google Scholar 

  26. 26.

    Dalisay, D. S. & Molinski, T. F. NMR quantitation of natural products at the nanomole scale. J. Nat. Prod. 72, 739–744 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Dona, A. C. et al. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Anal. Chem. 86, 9887–9894 (2014).

    CAS  PubMed  Google Scholar 

  28. 28.

    Kumar, D. Nuclear magnetic resonance (NMR) spectroscopy for metabolic profiling of medicinal plants and their products. Crit. Rev. Anal. Chem. 46, 400–412 (2016).

    CAS  PubMed  Google Scholar 

  29. 29.

    Fonville, J. M. et al. Evaluation of full-resolution J-resolved 1H NMR projections of biofluids for metabonomics information retrieval and biomarker identification. Anal. Chem. 82, 1811–1821 (2010).

    CAS  PubMed  Google Scholar 

  30. 30.

    Ludwig, C. & Viant, M. R. Two-dimensional J-resolved NMR spectroscopy: review of a key methodology in the metabolomics toolbox. Phytochem. Anal. 21, 22–32 (2010).

    CAS  PubMed  Google Scholar 

  31. 31.

    Viant, M. R. Improved methods for the acquisition and interpretation of NMR metabolomic data. Biochem. Biophys. Res. Commun. 310, 943–948 (2003).

    CAS  PubMed  Google Scholar 

  32. 32.

    Foxall, P. J. D., Parkinson, J. A., Sadler, I. H., Lindon, J. C. & Nicholson, J. K. Analysis of biological-fluids using 600 Mhz proton Nmr-spectroscopy - application of homonuclear 2-dimensional J-resolved spectroscopy to urine and blood-plasma for spectral simplification and assignment. J. Pharm. Biomed. 11, 21–31 (1993).

    CAS  Google Scholar 

  33. 33.

    Liu, M., Nicholson, J. K. & Lindon, J. C. High-resolution diffusion and relaxation edited one- and two-dimensional 1H NMR spectroscopy of biological fluids. Anal. Chem. 68, 3370–3376 (1996).

    CAS  PubMed  Google Scholar 

  34. 34.

    Spraul, M., Nicholson, J. K., Lynch, M. J. & Lindon, J. C. Application of the one-dimensional Tocsy pulse sequence in 750 Mhz H-1-Nmr spectroscopy for assignment of endogenous metabolite resonances in biofluids. J. Pharm. Biomed. 12, 613–618 (1994).

    CAS  Google Scholar 

  35. 35.

    Lindon, J. C., Nicholson, J. K. & Wilson, I. D. Directly coupled HPLC-NMR and HPLC-NMR-MS in pharmaceutical research and development. J. Chromatogr. B 748, 233–258 (2000).

    CAS  Google Scholar 

  36. 36.

    Noda, I. Generalized 2-dimensional correlation method applicable to infrared, Raman, and other types of spectroscopy. Appl. Spectrosc. 47, 1329–1336 (1993).

    CAS  Google Scholar 

  37. 37.

    Robinette, S. L., Lindon, J. C. & Nicholson, J. K. Statistical spectroscopic tools for biomarker discovery and systems medicine. Anal. Chem. 85, 5297–5303 (2013).

    CAS  PubMed  Google Scholar 

  38. 38.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate - a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B Met. 57, 289–300 (1995).

    Google Scholar 

  39. 39.

    Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

    CAS  PubMed  Google Scholar 

  40. 40.

    Elliott, P. et al. Urinary metabolic signatures of human adiposity. Sci. Transl. Med. 7, 285ra262 (2015).

    Google Scholar 

  41. 41.

    Garcia-Perez, I. et al. An analytical pipeline for quantitative characterization of dietary intake: application to assess grape intake. J. Agric. Food Chem. 64, 2423–2431 (2016).

    CAS  PubMed  Google Scholar 

  42. 42.

    Garcia-Perez, I. et al. Bidirectional correlation of NMR and capillary electrophoresis fingerprints: a new approach to investigating Schistosoma mansoni infection in a mouse model. Anal. Chem. 82, 203–210 (2010).

    CAS  PubMed  Google Scholar 

  43. 43.

    Garcia-Perez, I. et al. Urinary metabolic phenotyping the slc26a6 (chloride-oxalate exchanger) null mouse model. J. Proteome Res. 11, 4425–4435 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Andreas, N. J. et al. Multiplatform characterization of dynamic changes in breast milk during lactation. Electrophoresis 36, 2269–2285 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Garcia-Perez, I. et al. Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial. Lancet Diabetes Endo. 5, 184–195 (2017).

    Google Scholar 

  46. 46.

    Posma, J. M. et al. Optimized phenotypic biomarker discovery and confounder elimination via covariate-adjusted projection to latent structures from metabolic spectroscopy data. J. Proteome Res. 17, 1586–1595 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Trygg, J., Holmes, E. & Lundstedt, T. Chemometrics in metabonomics. J. Proteome Res. 6, 469–479 (2007).

    CAS  PubMed  Google Scholar 

  48. 48.

    Baranovicova, E. et al. NMR metabolomic study of blood plasma in ischemic and ischemically preconditioned rats: an increased level of ketone bodies and decreased content of glycolytic products 24 h after global cerebral ischemia. J. Physiol. Biochem. https://doi.org/10.1007/s13105-018-0632-2 (2018).

  49. 49.

    Scott, I. M. et al. Merits of random forests emerge in evaluation of chemometric classifiers by external validation. Anal. Chim. Acta 801, 22–33 (2013).

    CAS  PubMed  Google Scholar 

  50. 50.

    Cavill, R. et al. Genetic algorithms for simultaneous variable and sample selection in metabonomics. Bioinformatics 25, 112–118 (2009).

    CAS  PubMed  Google Scholar 

  51. 51.

    Di Anibal, C. V., Callao, M. P. & Ruisanchez, I. 1H NMR variable selection approaches for classification. A case study: the determination of adulterated foodstuffs. Talanta 86, 316–323 (2011).

    PubMed  Google Scholar 

  52. 52.

    Wang, T. et al. Automics: an integrated platform for NMR-based metabonomics spectral processing and data analysis. BMC Bioinforma. 10, 83 (2009).

    Google Scholar 

  53. 53.

    Balabin, R. M., Safieva, R. Z. & Lomakina, E. I. Gasoline classification using near infrared (NIR) spectroscopy data: comparison of multivariate techniques. Anal. Chim. Acta 671, 27–35 (2010).

    CAS  PubMed  Google Scholar 

  54. 54.

    Tiwari, P., Rosen, M. & Madabhushi, A. A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS). Med. Phys. 36, 3927–3939 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Fotiou, M. et al. (1)H NMR-based metabolomics reveals the effect of maternal habitual dietary patterns on human amniotic fluid profile. Sci. Rep. 8, 4076 (2018).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Holmes, E., Cloarec, O. & Nicholson, J. K. Probing latent biomarker signatures and in vivo pathway activity in experimental disease states via statistical total correlation spectroscopy (STOCSY) of biofluids: application to HgCl2 toxicity. J. Proteome Res. 5, 1313––1320 (2006).

    PubMed  Google Scholar 

  57. 57.

    Alves, A. C., Rantalainen, M., Holmes, E., Nicholson, J. K. & Ebbels, T. M. Analytic properties of statistical total correlation spectroscopy based information recovery in 1H NMR metabolic data sets. Anal. Chem. 81, 2075–2084 (2009).

    PubMed  Google Scholar 

  58. 58.

    Rodriguez-Martinez, A., Ayala, R., Posma, J. M. & Dumas, M. E. Exploring the genetic landscape of metabolic phenotypes with metaboSignal. Curr. Protoc. Bioinform. 61, 14 14 11–14 14 13 (2018).

    Google Scholar 

  59. 59.

    Wang, Y. et al. Magic angle spinning NMR and 1H-31P heteronuclear statistical total correlation spectroscopy of intact human gut biopsies. Anal. Chem. 80, 1058–1066 (2008).

    CAS  PubMed  Google Scholar 

  60. 60.

    Keun, H. C. et al. Heteronuclear F-19-H-1 statistical total correlation spectroscopy as a tool in drug metabolism: Study of flucloxacillin biotransformation. Anal. Chem. 80, 1073–1079 (2008).

    CAS  PubMed  Google Scholar 

  61. 61.

    Aue, W. P., Karhan, J. & Ernst, R. R. Homonuclear broad-band decoupling and 2-dimensional J-resolved Nmr-spectroscopy. J. Chem. Phys. 64, 4226–4227 (1976).

    CAS  Google Scholar 

  62. 62.

    Nagayama, K., Kumar, A., Wuthrich, K. & Ernst, R. R. Experimental-techniques of two-dimensional correlated spectroscopy. J. Magn. Reson. 40, 321–334 (1980).

    CAS  Google Scholar 

  63. 63.

    Aue, W. P., Bartholdi, E. & Ernst, R. R. 2-Dimensional spectroscopy - application to nuclear magnetic-resonance. J. Chem. Phys. 64, 2229–2246 (1976).

    CAS  Google Scholar 

  64. 64.

    Bodenhausen, G. & Ruben, D. J. Natural abundance N-15 Nmr by enhanced heteronuclear spectroscopy. Chem. Phys. Lett. 69, 185–189 (1980).

    CAS  Google Scholar 

  65. 65.

    Keeler, J. Understanding NMR Spectroscopy 2nd edn (John Wiley & Sons, 2002).

  66. 66.

    Bax, A., Farley, K. A. & Walker, G. S. Increased HMBC sensitivity for correlating poorly resolved proton multiplets to carbon-13 using selective or semi-selective pulses. J. Magn. Reson. Ser. A 119, 134–138 (1996).

    CAS  Google Scholar 

  67. 67.

    Bollard, M. E. et al. High-resolution (1)H and (1)H-(13)C magic angle spinning NMR spectroscopy of rat liver. Magn. Reson. Med. 44, 201–207 (2000).

    CAS  PubMed  Google Scholar 

  68. 68.

    Smith, L. M. et al. Statistical correlation and projection methods for improved information recovery from diffusion-edited NMR spectra of biological samples. Anal. Chem. 79, 5682–5689 (2007).

    CAS  PubMed  Google Scholar 

  69. 69.

    Tang, H. R., Wang, Y. L., Nicholson, J. K. & Lindon, J. C. Use of relaxation-edited one-dimensional and two dimensional nuclear magnetic resonance spectroscopy to improve detection of small metabolites in blood plasma. Anal. Biochem. 325, 260–272 (2004).

    CAS  PubMed  Google Scholar 

  70. 70.

    Lenz, E. M. Nuclear magnetic resonance (NMR)-based drug metabolite profiling. Methods Mol. Biol. 708, 299–319 (2011).

    CAS  PubMed  Google Scholar 

  71. 71.

    Ramautar, R., Somsen, G. W. & de Jong, G. J. CE-MS in metabolomics. Electrophoresis 30, 276–291 (2009).

    CAS  PubMed  Google Scholar 

  72. 72.

    Garcia-Perez, I. et al. Metabolic fingerprinting of Schistosoma mansoni infection in mice urine with capillary electrophoresis. Electrophoresis 29, 3201–3206 (2008).

    CAS  PubMed  Google Scholar 

  73. 73.

    Fiehn, O. Metabolomics by gas chromatography-mass spectrometry: combined targeted and untargeted profiling. Curr. Protoc. Mol. Biol. 114, 30 34 31–30 34 32 (2016).

    Google Scholar 

  74. 74.

    Spraul, M., Nicholson, J. K., Lynch, M. J. & Lindon, J. C. Application of the one-dimensional TOCSY pulse sequence in 750 MHz 1H-NMR spectroscopy for assignment of endogenous metabolite resonances in biofluids. J. Pharm. Biomed. Anal. 12, 613–618 (1994).

    CAS  PubMed  Google Scholar 

  75. 75.

    Crockford, D. J. et al. Statistical heterospectroscopy, an approach to the integrated analysis of NMR and UPLC-MS data sets: application in metabonomic toxicology studies. Anal. Chem. 78, 363–371 (2006).

    CAS  PubMed  Google Scholar 

  76. 76.

    Teul, J. et al. Improving metabolite knowledge in stable atherosclerosis patients by association and correlation of GC-MS and 1H NMR fingerprints. J. Proteome Res. 8, 5580–5589 (2009).

    CAS  PubMed  Google Scholar 

  77. 77.

    Posma, J. M., Robinette, S. L., Holmes, E. & Nicholson, J. K. MetaboNetworks, an interactive Matlab-based toolbox for creating, customizing and exploring sub-networks from KEGG. Bioinformatics 30, 893–895 (2014).

    CAS  PubMed  Google Scholar 

  78. 78.

    Quinn, R. A. et al. Molecular networking as a drug discovery, drug metabolism, and precision medicine strategy. Trends Pharmacol. Sci. 38, 143–154 (2017).

    CAS  PubMed  Google Scholar 

  79. 79.

    Gratton, J. et al. Optimized sample handling strategy for metabolic profiling of human feces. Anal. Chem. 88, 4661–4668 (2016).

    CAS  PubMed  Google Scholar 

  80. 80.

    Farrant, R. D., Lindon, J. C. & Nicholson, J. K. Internal temperature calibration for 1H NMR spectroscopy studies of blood plasma and other biofluids. NMR Biomed. 7, 243–247 (1994).

    CAS  PubMed  Google Scholar 

  81. 81.

    Holmes, E. et al. 750 MHz 1H NMR spectroscopy characterisation of the complex metabolic pattern of urine from patients with inborn errors of metabolism: 2-hydroxyglutaric aciduria and maple syrup urine disease. J. Pharm. Biomed. Anal. 15, 1647–1659 (1997).

    CAS  PubMed  Google Scholar 

  82. 82.

    Duarte, I. F. et al. Identification of metabolites in human hepatic bile using 800 MHz 1H NMR spectroscopy, HPLC-NMR/MS and UPLC-MS. Mol. Biosyst. 5, 180–190 (2009).

    CAS  PubMed  Google Scholar 

  83. 83.

    Maaheimo, H., Rabina, J. & Renkonen, O. 1H and 13C NMR analysis of the pentasaccharide Gal beta (1->4)GlcNAc beta (1->3)-[GlcNAc beta (1->6)]Gal beta (1->4)GlcNAc synthesized by the mid-chain beta-(1->6)-D-N-acetylglucosaminyltransferase of rat serum. Carbohydr. Res. 297, 145–151 (1997).

    CAS  PubMed  Google Scholar 

  84. 84.

    Want, E. J. et al. Global metabolic profiling procedures for urine using UPLC-MS. Nat. Protoc. 5, 1005–1018 (2010).

    CAS  PubMed  Google Scholar 

  85. 85.

    Folch, J., Lees, M. & Sloane Stanley, G. H. A simple method for the isolation and purification of total lipides from animal tissues. J. Biol. Chem. 226, 497–509 (1957).

    CAS  PubMed  Google Scholar 

  86. 86.

    Tredwell, G. D., Bundy, J. G., De Iorio, M. & Ebbels, T. M. Modelling the acid/base (1)H NMR chemical shift limits of metabolites in human urine. Metabolomics 12, 152 (2016).

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This article presents independent research funded by the UK National Institute for Health Research (NIHR). The views expressed are those of the authors and not necessarily those of the UK National Health Service (NHS), the NIHR or the UK Department of Health. I.G.-P. is supported by a National Institute for Health Research (NIHR) fellowship (NIHR-CDF-2017-10-032). J.M.P. is supported by a Rutherford Fund Fellowship at Health Data Research UK (MR/S004033/1). G.F. is an NIHR Senior Investigator. P.E. is Director of the Medical Research Council (MRC) Centre for Environment and Health (MR/L01341X/1) and acknowledges support from the NIHR Imperial Biomedical Research Centre and the NIHR Health Protection Research Unit in Health Impact of Environmental Hazards (HPRU-2012-10141). P.E. is supported by the UK Dementia Research Institute, supported by UK DRI Ltd., which is funded by the UK MRC, the Alzheimer’s Society and Alzheimer’s Research UK. INTERMAP is supported by the US National Heart, Lung and Blood Institute (grants R01-HL050490 and R01-HL084228), the Chicago Health Research Foundation and national agencies in Japan (grant [A] 090357003) and the UK (R2019EPH). Infrastructure support was provided by the NIHR Imperial Biomedical Research Centre and the UK MEDical BIOinformatics partnership (MR/L01632X/1). I.G.-P. gratefully acknowledges Olaf Beckonert for his guidance. J.K.N. acknowledges the Australian Government Future Food Systems Cooperative Research Centre (CRC). E.H. is supported by the Department of Jobs, Tourism, Science and Innovation, Government of Western Australian, through the Premier’s Science Fellowship Program.

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Writing—review and editing: I.G.-P., J.M.P., J.L., I.S.C., J.S., G.F., P.E., E.H. and J.K.N. Contributed data: P.E., Q.C., C.L.B. and I.G.-P. Figures and tables: I.G.-P. and C.L.B. Sample analysis: I.G.-P. Statistical analysis and software development: J.M.P. Protocol and workflow design: I.G.-P. Funding acquisition: J.M.P., I.G.-P., E.H., P.E. and J.K.N.

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Correspondence to Elaine Holmes or Jeremy K. Nicholson.

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Key references using this protocol

Elliott, P. et al. Sci. Transl. Med. 7, 285ra262 (2015): https://doi.org/10.1126/scitranslmed.aaa5680

Posma, J. M. et al. Anal. Chem. 89, 3300−3309 (2017): https://doi.org/10.1021/acs.analchem.6b03324

Garcia-Perez, I. et al. Lancet Diabet. Endo. 5, 184−195 (2017): https://doi.org/10.1016/S2213-8587(16)30419-3

Key data used in this protocol

Orbán-Németh, Z. et al. Nat. Protoc. 13, 478−494 (2018): https://doi.org/10.1038/nprot.2017.146

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Garcia-Perez, I., Posma, J.M., Serrano-Contreras, J.I. et al. Identifying unknown metabolites using NMR-based metabolic profiling techniques. Nat Protoc 15, 2538–2567 (2020). https://doi.org/10.1038/s41596-020-0343-3

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