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MCR-ALS analysis of 1H NMR spectra by segments to study the zebrafish exposure to acrylamide

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Abstract

Metabolomics is currently an important field within bioanalytical science and NMR has become a key technique for drawing the full metabolic picture. However, the analysis of 1H NMR spectra of metabolomics samples is often very challenging, as resonances usually overlap in crowded regions, hindering the steps of metabolite profiling and resonance integration. In this context, a pre-processing method for the analysis of 1D 1H NMR data from metabolomics samples is proposed, consisting of the blind resolution and integration of all resonances of the spectral dataset by multivariate curve resolution-alternating least squares (MCR-ALS). The resulting concentration estimates can then be examined with traditional chemometric methods such as principal component analysis (PCA), ANOVA-simultaneous component analysis (ASCA), and partial least squares-discriminant analysis (PLS-DA). Since MCR-ALS does not require the use of spectral templates, the concentration estimates for all resonances are obtained even before being assigned. Consequently, the metabolomics study can be performed without neglecting any relevant resonance. In this work, the proposed pipeline performance was validated with 1D 1H NMR spectra from a metabolomics study of zebrafish upon acrylamide (ACR) exposure. Remarkably, this method represents a framework for the high-throughput analysis of NMR metabolomics data that opens the way for truly untargeted NMR metabolomics analyses.

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Data availability

The data is available upon request.

Abbreviations

1D 1H NMR:

One-dimensional proton nuclear magnetic resonance

AAMA:

N-Acetyl-S-(carbamoylethyl)-L-cysteine

ACR:

Acrylamide

ANOVA:

Analysis of variance

ASCA:

ANOVA-simultaneous component analysis

AXP:

Adenosine nucleotides

BATMAN:

Bayesian automated metabolite analyzer for NMR

C:

Concentrations matrix in the MCR-ALS analysis

D:

Input matrix in the MCR-ALS analysis

DSS:

2,2-Dimethyl-2-silapentane-5-sulfonate

GABA:

Gamma-aminobutyric acid

GUI:

Graphical user interface

MCR-ALS:

Multivariate curve resolution-alternating least squares

NMR:

Nuclear magnetic resonance

NOESY:

Nuclear Overhauser Effect SpectroscopY

PC1:

First principal component

PC2:

Second principal component

PCA:

Principal component analysis

PLS-DA:

Partial least squares-discriminant analysis

PQN:

Probabilistic quotient normalization

SCA:

Simultaneous component analysis

ST :

Spectrum matrix in the MCR-ALS analysis

SVD:

Single value decomposition

UDP:

Uridine diphosphate

References

  1. Boiteau RM, Hoyt DW, Nicora CD, Kinmonth-Schultz HA, Ward JK, Bingol K. Structure elucidation of unknown metabolites in metabolomics by combined NMR and MS/MS prediction. Metabolites. 2018;8(1):8.

    PubMed Central  Google Scholar 

  2. Dona AC, Kyriakides M, Scott F, Shephard EA, Varshavi D, Veselkov K, et al. A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments. Comput Struct Biotechnol. 2016;14:135–53.

    CAS  Google Scholar 

  3. Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol. 2016;17:451.

    PubMed  PubMed Central  CAS  Google Scholar 

  4. Bingol K. Recent advances in targeted and untargeted metabolomics by NMR and MS/NMR methods. High-Throughput. 2018;7(2):9.

    PubMed Central  CAS  Google Scholar 

  5. Cappello T, Maisano M, Mauceri A, Fasulo S. 1H NMR-based metabolomics investigation on the effects of petrochemical contamination in posterior adductor muscles of caged mussel Mytilus galloprovincialis. Ecotoxicol Environ Saf. 2017;142(Supplement C):417–22.

    PubMed  CAS  Google Scholar 

  6. Nagato EG, D'eon JC, Lankadurai BP, Poirier DG, Reiner EJ, Simpson AJ, et al. 1H NMR-based metabolomics investigation of Daphnia magna responses to sub-lethal exposure to arsenic, copper and lithium. Chemosphere. 2013;93(2):331–7.

    PubMed  CAS  Google Scholar 

  7. Puig-Castellví F, Pérez Y, Piña B, Tauler R, Alfonso I. Comparative analysis of 1H NMR and 1H–13C HSQC NMR metabolomics to understand the effects of medium composition in yeast growth. Anal Chem. 2018;90(21):12422–30.

    PubMed  Google Scholar 

  8. Tomassini A, Vitalone A, Marini F, Praticò G, Sciubba F, Bevilacqua M, et al. 1H NMR-based urinary metabolic profiling reveals changes in nicotinamide pathway intermediates due to postnatal stress model in rat. J Proteome Res. 2014;13(12):5848–59.

    PubMed  CAS  Google Scholar 

  9. Ruan LY, Fan JT, Hong W, Zhao H, Li MH, Jiang L, et al. Isoniazid-induced hepatotoxicity and neurotoxicity in rats investigated by 1H NMR based metabolomics approach. Toxicol Lett. 2018;295:256–69.

    PubMed  CAS  Google Scholar 

  10. Fathi F, Oskouie AA, Tafazzoli M, Naderi N, Sohrabzedeh K, Fathi S, et al. Metabonomics based NMR in Crohn’s disease applying PLS-DA. Gastroenterol Hepatol Bed Bench. 2013;6(Suppl 1):S82–6.

    PubMed  PubMed Central  Google Scholar 

  11. Bro R, Smilde AK. Principal component analysis. Anal Methods. 2014;6(9):2812–31.

    CAS  Google Scholar 

  12. Smilde AK, Jansen JJ, Hoefsloot HCJ, Lamers RJAN, van der Greef J, Timmerman ME. ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data. Bioinformatics. 2005;21(13):3043–8.

    PubMed  CAS  Google Scholar 

  13. Barker M, Rayens W. Partial least squares for discrimination. J Chemom. 2003;17(3):166–73.

    CAS  Google Scholar 

  14. Tauler R, Kowalski B, Fleming S. Multivariate curve resolution applied to spectral data from multiple runs of an industrial-process. Anal Chem. 1993;65.

  15. Ebrahimi P, Larsen FH, Jensen HM, Vogensen FK, Engelsen SB. Real-time metabolomic analysis of lactic acid bacteria as monitored by in vitro NMR and chemometrics. Metabolomics. 2016;12(4):77.

    Google Scholar 

  16. Winning H, Larsen FH, Bro R, Engelsen SB. Quantitative analysis of NMR spectra with chemometrics. J Magn Reson. 2008;1:26–32.

    Google Scholar 

  17. Abdollahi H, Tauler R. Uniqueness and rotation ambiguities in multivariate curve resolution methods. Chemom Intell Lab Syst. 2011;108(2):100–11.

    CAS  Google Scholar 

  18. Puig-Castellví F, Alfonso I, Tauler R. Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach. Anal Chim Acta. 2017;964(Supplement C):55–66.

    PubMed  Google Scholar 

  19. Karakach TK, Knight R, Lenz EM, Viant MR, Walter JA. Analysis of time course 1H NMR metabolomics data by multivariate curve resolution. Magn Reson Chem. 2009;47(S1):S105–17.

    PubMed  CAS  Google Scholar 

  20. Montoliu I, Martin FPJ, Collino S, Rezzi S, Kochhar S. Multivariate modeling strategy for intercompartmental analysis of tissue and plasma 1H NMR spectrotypes. J Proteome Res. 2009;8(5):2397–406.

    PubMed  CAS  Google Scholar 

  21. Röhnisch HE, Eriksson J, Müllner E, Agback P, Sandström C, Moazzami AA. AQuA: an automated quantification algorithm for high-throughput NMR-based metabolomics and its application in human plasma. Anal Chem. 2018;90(3):2095–102.

    PubMed  Google Scholar 

  22. Tardivel PJC, Canlet C, Lefort G, Tremblay-Franco M, Debrauwer L, Concordet D, et al. ASICS: an automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra. Metabolomics. 2017;13(10):109.

    Google Scholar 

  23. Cañueto D, Gómez J, Salek RM, Correig X, Cañellas N. rDolphin: a GUI R package for proficient automatic profiling of 1D 1H-NMR spectra of study datasets. Metabolomics. 2018;14(3):24.

    PubMed  Google Scholar 

  24. Hao J, Liebeke M, Astle W, De Iorio M, Bundy JG, Ebbels TMD. Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN. Nat Protoc. 2014;9(6):1416–27.

    PubMed  CAS  Google Scholar 

  25. Khakimov B, Mobaraki N, Trimigno A, Aru V, Engelsen SB. Signature Mapping (SigMa): an efficient approach for processing complex human urine 1H NMR metabolomics data. Anal. Chim. Acta, 2020, In press, 1–10. https://doi.org/10.1016/j.aca.2020.02.025.

  26. Faria M, Ziv T, Gómez-Canela C, Ben-Lulu S, Prats E, Novoa-Luna KA, et al. Acrylamide acute neurotoxicity in adult zebrafish. Sci Rep. 2018;8:1–14.

    Google Scholar 

  27. Dearfield KL, Abernathy CO, Ottley MS, Brantner JH, Hayes PF. Acrylamide: its metabolism, developmental and reproductive effects, genotoxicity, and carcinogenicity. Mutat Res-Rev Genet. 1988;195(1):45–77.

    CAS  Google Scholar 

  28. Tareke E, Rydberg P, Karlsson P, Eriksson S, Törnqvist M. Analysis of acrylamide, a carcinogen formed in heated foodstuffs. J Agric Food Chem. 2002;50(17):4998–5006.

    PubMed  CAS  Google Scholar 

  29. Garland TO, Patterson MWH. Six cases of acrylamide poisoning. Br Med J. 1967;4:134–8.

    PubMed  PubMed Central  CAS  Google Scholar 

  30. Tepe Y, Çebi A. Acrylamide in environmental water: a review on sources, exposure, and public health risks. Expos Health. 2017.

  31. Duke TJ, Ruestow PS, Marsh GM. The influence of demographic, physical, behavioral, and dietary factors on hemoglobin adduct levels of acrylamide and glycidamide in the general U.S. population. Crit Rev Food Sci Nutr. 2018;58(5):700–10.

    PubMed  CAS  Google Scholar 

  32. Raldúa D, Casado M, Prats E, Faria M, Puig-Castellví F, Pérez Y, et al. Targeting redox metabolism: the perfect storm induced by acrylamide poisoning in the brain. Sci Rep. 2020;10:312.

    PubMed  PubMed Central  Google Scholar 

  33. Savorani F, Tomasi G, Engelsen SB. Icoshift: a versatile tool for the rapid alignment of 1D NMR spectra. J Magn Reson. 2010;202(2):190–202.

    PubMed  CAS  Google Scholar 

  34. Dieterle F, Ross A, Schlotterbeck G, Senn H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR Metabonomics. Anal Chem. 2006;78(13):4281–90.

    PubMed  CAS  Google Scholar 

  35. de Meyer T, Sinnaeve D, van Gasse B, Tsiporkova E, Rietzschel E, de Buyzere M, et al. NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm. Anal Chem. 2008;80(10):3783–90.

    PubMed  Google Scholar 

  36. Jacob D, Deborde C, Lefebvre M, Maucourt M, Moing A. NMRProcFlow: a graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics. Metabolomics. 2017;13:36.

    PubMed  PubMed Central  CAS  Google Scholar 

  37. Abdi H. Singular Value decomposition (SVD) and generalized singular value decomposition (GSVD). In: Salkind NJ, editor. Encyclopedia of measurement and statistics. SAGE Publications: 2007;907–912.

  38. Jaumot J, de Juan A, Tauler R. MCR-ALS GUI 2.0: new features and applications. Chemom Intell Lab Syst. 2015;140:1–12.

    CAS  Google Scholar 

  39. Zwanenburg G, Hoefsloot HCJ, Westerhuis JA, Jansen JJ, Smilde AK. ANOVA–principal component analysis and ANOVA–simultaneous component analysis: a comparison. J Chemom. 2011;25:561–7.

    CAS  Google Scholar 

  40. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, et al. HMDB 3.0—the human metabolome database in 2013. Nucleic Acids Res. 2013;41(D1):D801–7.

    PubMed  CAS  Google Scholar 

  41. Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, Lin J, et al. BioMagResBank. Nucleic Acids Res. 2008;36(suppl_1):D402–8.

    PubMed  CAS  Google Scholar 

  42. Shi M, Ellingsen Ø, Bathen TF, Høydal MA, Koch LG, Britton SL, et al. Skeletal muscle metabolism in rats with low and high intrinsic aerobic capacity: effect of aging and exercise training. PLoS One. 2018;13(12):e0208703.

    PubMed  PubMed Central  Google Scholar 

  43. Faria M, Prats E, Gómez-Canela C, Hsu C, Arick MA II, Bedrossiantz J, et al. Therapeutic potential of N-acetylcysteine in acrylamide acute neurotoxicity in adult zebrafish. Sci Rep. 2019;9:16467.

    PubMed  PubMed Central  Google Scholar 

  44. da Silva RR, Dorrestein PC, Quinn RA. Illuminating the dark matter in metabolomics. PNAS. 2015;112(41):12549–50.

    PubMed  Google Scholar 

  45. Jones OAH. Illuminating the dark metabolome to advance the molecular characterisation of biological systems. Metabolomics. 2018;14(8):101.

    PubMed  Google Scholar 

  46. Kopp EK, Dekant W. Toxicokinetics of acrylamide in rats and humans following single oral administration of low doses. Toxicol Appl Pharmacol. 2009;235(2):135–42.

    PubMed  CAS  Google Scholar 

  47. McHugh CE, Flott TL, Schooff CR, Smiley Z, Puskarich MA, Myers DD, et al. Rapid, reproducible, quantifiable NMR metabolomics: methanol and methanol: chloroform precipitation for removal of macromolecules in serum and whole blood. Metabolites. 2018;8(4):93.

    PubMed Central  Google Scholar 

  48. Trivedi DK, Hollywood KA, Goodacre R. Metabolomics for the masses: the future of metabolomics in a personalized world. New Horiz Trans Med. 2017;3(6):294–305.

    Google Scholar 

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Code availability

The MCR-ALS GUI can be downloaded from www.mcrals.info.

Funding

The research leading to these results has received funding from the NATO SfP project MD.SFPP 984777.

Author information

Authors and Affiliations

  1. NMR Facility, IQAC-CSIC, Jordi Girona 18-26, 08034, Barcelona, Spain

    Yolanda Pérez

  2. Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, 08034, Barcelona, Spain

    Marta Casado, Demetrio Raldúa, Eva Prats, Benjamín Piña, Romà Tauler & Francesc Puig-Castellví

  3. Department of Biological Chemistry, IQAC-CSIC, Jordi Girona 18-26, 08034, Barcelona, Spain

    Ignacio Alfonso

  4. Université Paris-Saclay, INRAE, AgroParisTech, UMR SayFood, 75005, Paris, France

    Francesc Puig-Castellví

Authors
  1. Yolanda Pérez
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  2. Marta Casado
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  8. Francesc Puig-Castellví
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Contributions

B Piña and D Raldúa designed the experiments. M Casado and Y Pérez performed the experiments. F Puig-Castellví performed the chemometric analysis. All authors have contributed in the manuscript writing and have given approval to its final version.

Corresponding author

Correspondence to Francesc Puig-Castellví.

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The authors declare that they have no conflict of interest.

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All procedures were approved by the Institutional Animal Care and Use Committees at the CID-CSIC and conducted in accordance with the institutional guidelines under a license from the local government (agreement number 9027).

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Pérez, Y., Casado, M., Raldúa, D. et al. MCR-ALS analysis of 1H NMR spectra by segments to study the zebrafish exposure to acrylamide. Anal Bioanal Chem 412, 5695–5706 (2020). https://doi.org/10.1007/s00216-020-02789-0

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