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

Ruiz-Aracama A, Peijnenburg A, Kleinjans J, Jennen D, van Delft J, Hellfrisch C, Lommen A - BMC Genomics (2011)

Bottom Line: The observed changes in metabolite levels are discussed in relation to the reported effects of TCDD.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: RIKILT-Institute of Food Safety, Wageningen University and Research Centre, Wageningen, The Netherlands. Ainhoa.ruiz@wur.nl

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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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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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.

Mentions: The same extracts were analyzed by LC-MS in order to obtain more detailed information concerning the differences in polar metabolite levels due to treatment. The LC-MS data were pre-processed and aligned using MetAlign, the program developed in-house [34]. The aligned fingerprint data, in the form of generated spreadsheets, were normalized using the PL/CHCl3 ratio and subjected to 2Log transformation and ANOVA with p < 0.01 followed by PCA (Figure 7A). The separation between samples was dominated by passage number rather than by treatment. In order to determine the effect of TCDD on the polar fraction of the cells, the samples of this dataset were re-grouped with regard to treatment, and an ANOVA (p < 0.01) followed by a PCA was performed after 2Log transformation (Figure 7B). From the peak loadings (additionally surviving a Bonferroni correction) responsible for the separation of samples in this PCA, it is possible to create a list of masses that contributed significantly to the observed PCA separation. This list of masses was screened for molecular ions that showed at least a 1.2 fold change due to the treatment. Table 4 lists the metabolites and their fold change, as determined on the basis of fragmentation data from LC-nanomate-Orbitrap experiments. Table 5 presents the exact experimental masses of these metabolites, together with their obtained fragments. A few metabolites remained unidentified.


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

Ruiz-Aracama A, Peijnenburg A, Kleinjans J, Jennen D, van Delft J, Hellfrisch C, Lommen A - BMC Genomics (2011)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3141663&req=5

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.
Mentions: The same extracts were analyzed by LC-MS in order to obtain more detailed information concerning the differences in polar metabolite levels due to treatment. The LC-MS data were pre-processed and aligned using MetAlign, the program developed in-house [34]. The aligned fingerprint data, in the form of generated spreadsheets, were normalized using the PL/CHCl3 ratio and subjected to 2Log transformation and ANOVA with p < 0.01 followed by PCA (Figure 7A). The separation between samples was dominated by passage number rather than by treatment. In order to determine the effect of TCDD on the polar fraction of the cells, the samples of this dataset were re-grouped with regard to treatment, and an ANOVA (p < 0.01) followed by a PCA was performed after 2Log transformation (Figure 7B). From the peak loadings (additionally surviving a Bonferroni correction) responsible for the separation of samples in this PCA, it is possible to create a list of masses that contributed significantly to the observed PCA separation. This list of masses was screened for molecular ions that showed at least a 1.2 fold change due to the treatment. Table 4 lists the metabolites and their fold change, as determined on the basis of fragmentation data from LC-nanomate-Orbitrap experiments. Table 5 presents the exact experimental masses of these metabolites, together with their obtained fragments. A few metabolites remained unidentified.

Bottom Line: The observed changes in metabolite levels are discussed in relation to the reported effects of TCDD.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: RIKILT-Institute of Food Safety, Wageningen University and Research Centre, Wageningen, The Netherlands. Ainhoa.ruiz@wur.nl

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.

Show MeSH