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. 2011 May 20:12:251.
doi: 10.1186/1471-2164-12-251.

An untargeted multi-technique metabolomics approach to studying intracellular metabolites of HepG2 cells exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin

Affiliations

An untargeted multi-technique metabolomics approach to studying intracellular metabolites of HepG2 cells exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin

Ainhoa Ruiz-Aracama et al. BMC Genomics. .

Abstract

Background: In vitro cell systems together with omics methods represent promising alternatives to conventional animal models for toxicity testing. Transcriptomic and proteomic approaches have been widely applied in vitro but relatively few studies have used metabolomics. Therefore, the goal of the present study was to develop an untargeted methodology for performing reproducible metabolomics on in vitro systems. The human liver cell line HepG2, and the well-known hepatotoxic and non-genotoxic carcinogen 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), were used as the in vitro model system and model toxicant, respectively.

Results: The study focused on the analysis of intracellular metabolites using NMR, LC-MS and GC-MS, with emphasis on the reproducibility and repeatability of the data. State of the art pre-processing and alignment tools and multivariate statistics were used to detect significantly altered levels of metabolites after exposing HepG2 cells to TCDD. Several metabolites identified using databases, literature and LC-nanomate-Orbitrap analysis were affected by the treatment. The observed changes in metabolite levels are discussed in relation to the reported effects of TCDD.

Conclusions: Untargeted profiling of the polar and apolar metabolites of in vitro cultured HepG2 cells is a valid approach to studying the effects of TCDD on the cell metabolome. The approach described in this research demonstrates that highly reproducible experiments and correct normalization of the datasets are essential for obtaining reliable results. The effects of TCDD on HepG2 cells reported herein are in agreement with previous studies and serve to validate the procedures used in the present work.

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Figures

Figure 1
Figure 1
Total 1H NMR spectral signals of the apolar fraction of HepG2 cells exposed to TCDD (above) or the vehicle control DMSO (below). For both treatments, four spectra were overlaid. The numbers of the signals correspond to those indicated in Table 1.
Figure 2
Figure 2
Ratios between the intensity of some NMR spectral signals. A: Ratio between the intensity of the total NMR spectral signals (TS) and the intensity of the residual CHCl3 signal. B: Ratio between the intensity of the phospholipid signal in the NMR spectra (PL) and the intensity of the residual CHCl3 signal.
Figure 3
Figure 3
PCA plots (after ANOVA p < 0.01) of the apolar fraction of HepG2 analyzed by 1H NMR and pre-processed and aligned using a program developed in-house. A: Spheres with the same colour are technical replicates of the same sample per passage number. DMSO30: green; TCDD30: red; DMSO7: lila; TCDD7: yellow; DMSO11b: light blue; TCDD11b: dark blue; DMSO17: orange; TCDD17: purple; DMSO11b: light green; TCDD11b: white. B: Samples re-grouped with regard to treatment (irrespective of passage number). DMSO: green; TCDD: red.
Figure 4
Figure 4
PCA plots (after ANOVA p < 0.01) after normalization of the apolar fraction of HepG2 cells analyzed by GC-MS. A: Spheres with the same colour are technical replicates of the same sample per passage number. For denotation of the colours, see legend Figure 3. B: Samples re-grouped with regard to treatment (irrespective of passage number). DMSO: green; TCDD: red.
Figure 5
Figure 5
1H NMR spectra overlaid (4-fold) of the polar fraction of HepG2 cells exposed to DMSO (below) and TCDD (above). The following metabolites are identified by using standards, from the literature and/or databases and the numbers of the signals correspond to those indicated in Table 3. 1&2: leucine and isoleucine; 3: valine: 4: threonine: 5: lactale; 6: alanine; 7: putrescine; 8: acetate; 9: N-acetyl-aspartate; 10: glutamate; 11: glutamine; 12: reduced glutathione; 13: oxidized glutathione; 14: acetone; 15: citric acid; 16: creatine and/or phosphocreatine; 17: choline derivatives; 18: taurine; 19: glycine; 20: serine; 21: nucleotides derived from uridine (UMP, UDP, UTP); 22: nucleotides derived from adenosine (AMP, ADP, ATP); 23: NAD+ and/or NADH; 24: tyrosine; 25: formate.
Figure 6
Figure 6
PCA plots (after ANOVA p < 0.01) after normalization of the polar fraction of HepG2 cells analyzed by 1H NMR and pre-processed and aligned using a program developed in-house. A: Spheres with the same colour are technical replicates of the same sample per passage number. For denotation of the colours, see legend to Figure 3. B: Samples re-grouped with regard to treatment (irrespective of passage number). DMSO: green; TCDD: red..
Figure 7
Figure 7
PCA plots (after ANOVA p < 0.01) after normalization of the polar fraction of HepG2 cells analyzed by LC-ToF-MS and pre-processed and aligned using MetAlign (www.metalign.nl). A: Spheres with the same colour are technical replicates of the same sample per passage number. For denotation of the colours, see legend Figure 3. B: Samples re-grouped with regard to treatment (irrespective of passage number). DMSO: green; TCDD: red.

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References

    1. Nicholson JK, Lindon JC, Holmes E. Metabonomics: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999;29:1181–1189. doi: 10.1080/004982599238047. - DOI - PubMed
    1. De Vos RCH, Moco S, Lommen A, Keurentjes JJB, Bino RJ, Hall RD. Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nat Protoc. 2007;2:778–791. doi: 10.1038/nprot.2007.95. - DOI - PubMed
    1. Lommen A, van der Weg G, van Engelen MC, Bor G, Hoogenboom LAP, Nielen MWF. An untargeted metabolomics approach to contaminant analysis: Pinpointing potential unknown compounds. Anal Chim Acta. 2007;584:43–49. doi: 10.1016/j.aca.2006.11.018. - DOI - PubMed
    1. Fernie AR, Schauer N. Metabolomics-assisted breeding: a viable option for crop improvement? Trends Genet. 2008;25:39–48. - PubMed
    1. Griffin JL. Understanding mouse models of disease through metabolomics. Curr Opin Chem Biol. 2006;10:309–315. doi: 10.1016/j.cbpa.2006.06.027. - DOI - PubMed

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