Methionine Supplementation Affects Metabolism and Reduces Tumor Aggressiveness in Liver Cancer Cells

Farida Tripodi, Beatrice Badone, Marta Vescovi, Riccardo Milanesi, Simona Nonnis, Elisa Maffioli, Marcella Bonanomi, Daniela Gaglio, Gabriella Tedeschi, Paola Coccetti, Farida Tripodi, Beatrice Badone, Marta Vescovi, Riccardo Milanesi, Simona Nonnis, Elisa Maffioli, Marcella Bonanomi, Daniela Gaglio, Gabriella Tedeschi, Paola Coccetti

Abstract

Liver cancer is one of the most common cancer worldwide with a high mortality. Methionine is an essential amino acid required for normal development and cell growth, is mainly metabolized in the liver, and its role as an anti-cancer supplement is still controversial. Here, we evaluate the effects of methionine supplementation in liver cancer cells. An integrative proteomic and metabolomic analysis indicates a rewiring of the central carbon metabolism, with an upregulation of the tricarboxylic acid (TCA) cycle and mitochondrial adenosine triphosphate (ATP) production in the presence of high methionine and AMP-activated protein kinase (AMPK) inhibition. Methionine supplementation also reduces growth rate in liver cancer cells and induces the activation of both the AMPK and mTOR pathways. Interestingly, in high methionine concentration, inhibition of AMPK strongly impairs cell growth, cell migration, and colony formation, indicating the main role of AMPK in the control of liver cancer phenotypes. Therefore, regulation of methionine in the diet combined with AMPK inhibition could reduce liver cancer progression.

Keywords: AMPK; HCC; TCA cycle; growth; metabolomics; migration; proteomics.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Bioinformatic analysis of the proteomic data of HepG2 cells grown for 48 h (CTR), in the presence of Compound C (CTRCC), high methionine (MET) and high methionine and Compound C (METCC). (A) Ingenuity® Pathway Analysis (IPA) analysis carried out on the 46 proteins common to all data sets, whose expression is statistically different, highlighted that 25 out of 46 proteins differentially expressed belong to a network classified as: cancer, protein synthesis, RNA damage and repair. These proteins are shown in magenta among all the proteins belonging to the pathway. Each protein is reported with the corresponding IPA symbol. (B) The upstream regulator analysis, based on prior knowledge of expected effects between transcriptional regulators and their target genes in IPA, shows hepatocyte nuclear factor 4 (HNF4A) (p-value 0.001) which target 12/46 of the proteins differentially expressed. (C) Volcano plots of the comparison MET versus CTR, METCC versus MET, CTRCC versus CTR. In green: proteins upregulated; in red: proteins downregulated; in grey: proteins that are not statistically different (Student’s t-test p-value = 0.05).
Figure 2
Figure 2
High methionine and Compound C induce proteomic changes. (A–C) Functional analysis of the proteins differentially expressed in the comparison MET versus CTR, METCC versus MET, CTRCC versus CTR. Proteins were considered differentially expressed if they were present only in one condition or showed significant t-test difference (Student’s t-test p value = 0.05). (A) GO Biological Processes increased in the three comparison (B) GO Biological Processes decreased in the three comparison. Functional grouping was based on Fischer’s exact test p-value ≤ 0.05 (i.e., −log10 ≥ 1.3) and at least three counts. In each comparison, the terms increased or decreased refer to proteins up or downregulated upon treatments.
Figure 3
Figure 3
Bioinformatic analysis of the proteomic data on HepG2 cells grown for 48 h (CTR), in the presence of Compound C (CTRCC), high methionine (MET) and high methionine and Compound C (METCC). The analysis was carried out by IPA on the proteins differentially expressed among the comparison MET versus CTR, METCC versus CTR and CTRCC versus CTR. Proteins were considered differentially expressed if they were present only in one condition or showed significant t-test difference (Student’s t-test p-value 0.05). Functional grouping was based on Fischer’s exact test p value ≤ 0.05 and at least three counts. The colored bars are a visual representation of the corresponding –log p-value reported abreast.
Figure 4
Figure 4
High methionine and Compound C affect cellular metabolism. (A,B) HepG2 (A) and Huh7 (B) cells were grown for 48 h in regular medium or in the presence of high methionine and/or Compound C. Media from three biological replicates were collected to measure glucose and glutamine consumption, lactate, and glutamate secretion. (C–F) HepG2 cells were grown for 48 h in regular medium (CTR), or in the presence of high methionine and/or Compound C. Metabolomics analysis was performed by GC/MS and LC/MS on five biological replicates, each analyzed in technical duplicate. (C) Metabolic pathways mostly affected in this analysis. The metabolic pathway analysis was performed using the MetaboAnalyst 4.0 tool. The metabolic pathways are represented as circles. Color intensity (white-to-red) and size of each circle reflects increasing statistical significance, based on the p-value [−log(P)] from the pathway enrichment analysis, and pathway impact value derived from the pathway topology analysis, respectively. (D–F) Hierarchical clustering heatmaps from one-way ANOVA analysis of differential metabolites belonging to (D) methionine and redox metabolism, (E) amino acid metabolism, (F) central carbon metabolism. The heatmaps were obtained using the MetaboAnalyst 4.0 tool. The color code scale indicates the normalized metabolite abundance. (G) Histogram of the level of metabolites of the TCA cycle in the four conditions in HepG2 cells, measured in the metabolomics analysis. The level of each metabolite in the control was set to 1. * p < 0.05 compared to control. (H) Seahorse adenosine triphosphate (ATP) rate assay analysis in HepG2 and Huh7 cells, grown for 48 h in regular medium or in the presence of high methionine and/or Compound C. * p < 0.05 for mitoATP compared to control, p < 0.05 for glycoATP compared to control.
Figure 4
Figure 4
High methionine and Compound C affect cellular metabolism. (A,B) HepG2 (A) and Huh7 (B) cells were grown for 48 h in regular medium or in the presence of high methionine and/or Compound C. Media from three biological replicates were collected to measure glucose and glutamine consumption, lactate, and glutamate secretion. (C–F) HepG2 cells were grown for 48 h in regular medium (CTR), or in the presence of high methionine and/or Compound C. Metabolomics analysis was performed by GC/MS and LC/MS on five biological replicates, each analyzed in technical duplicate. (C) Metabolic pathways mostly affected in this analysis. The metabolic pathway analysis was performed using the MetaboAnalyst 4.0 tool. The metabolic pathways are represented as circles. Color intensity (white-to-red) and size of each circle reflects increasing statistical significance, based on the p-value [−log(P)] from the pathway enrichment analysis, and pathway impact value derived from the pathway topology analysis, respectively. (D–F) Hierarchical clustering heatmaps from one-way ANOVA analysis of differential metabolites belonging to (D) methionine and redox metabolism, (E) amino acid metabolism, (F) central carbon metabolism. The heatmaps were obtained using the MetaboAnalyst 4.0 tool. The color code scale indicates the normalized metabolite abundance. (G) Histogram of the level of metabolites of the TCA cycle in the four conditions in HepG2 cells, measured in the metabolomics analysis. The level of each metabolite in the control was set to 1. * p < 0.05 compared to control. (H) Seahorse adenosine triphosphate (ATP) rate assay analysis in HepG2 and Huh7 cells, grown for 48 h in regular medium or in the presence of high methionine and/or Compound C. * p < 0.05 for mitoATP compared to control, p < 0.05 for glycoATP compared to control.
Figure 5
Figure 5
Integration of metabolomics and proteomics analysis. (A) Metabolic and proteomic pathways mostly affected in MET versus CTR. (B) Metabolic and Proteomic pathways mostly affected in CTRCC versus CTR. (C) Metabolic and Proteomic pathways mostly affected in METCC versus CTR. The scatter plots show a summary of the joint evidence from enrichment analysis (p-values) and topology analysis (pathway impact). Dots size and color (white to red) are proportional to the numbers of genes and compounds present in a pathway. (E) List of the 10 more significant pathways in each of the three comparisons. (E) Scatter plot of the common pathways among the list shown in (D).
Figure 6
Figure 6
High methionine activates AMK-activated protein kinase (AMPK), mTOR and Akt pathways. (A) HepG2 were treated with methionine for 24 h and AMPK activation state was assayed by Western analysis, using the pT172-AMPK antibody (against pT172 in the activation loop) and using the anti-pS79-Acc1 antibody (against the target site of AMPK on Acc1). An anti-AMPK total antibody and an anti-vinculin antibody were used as controls. (B) HepG2 and Huh7cells were gown in control medium and 1.5 g/L methionine was added to the cultures at time 0. Samples were collected at the indicated time points to evaluate AMPK activation, using an anti-pT172-AMPK antibody and an anti-pS79-Acc1 antibody. An anti-AMPK total antibody and an anti-tubulin antibody were used as controls. (C) HepG2 and Huh7 cells were gown for 48 h in the absence or presence of Compound C. Then 1.5 g/L methionine was added to the cultures, and samples were collected at the indicated time points to evaluate mTOR activation, using anti-pS6K antibody and Akt activation using anti-pS473-Akt antibody. Anti-Akt total antibody and anti-tubulin antibody were used as controls.
Figure 7
Figure 7
High methionine and Compound C inhibit cancer phenotypes. (A,B) Cell growth of (A) HepG2 and (B) Huh7 was monitored until 72 h in regular medium (CTR) or in the presence of 1.5 g/L methionine (MET) and/or Compound C to obtain partial AMPK inactivation (2 µM Compound C for HepG2 and 1.5 µM for Huh7). The experiments were performed at least in triplicate. (C,D) HepG2 or Huh7 were grown for 24 h in regular medium (CTR), or in the presence of high methionine and/or Compound C. Then they were starved for 24 h in the same medium without serum and migration was evaluated with a transwell assay for 24 h. (E,F) Colony formation assay of HepG2 or Huh7 grown in regular medium or in the presence of high methionine and/or Compound C. Experiments were performed in triplicate. * p < 0.05.
Figure 8
Figure 8
The effect of high methionine and AMPK inhibition is specific for liver cancer cells. (A,B) Gene knockdown for AMPKα/α’ in HepG2 and Huh7 cells was achieved by siRNA. (A) The level of endogenous AMPKα/α’ protein was detected by immunoblot by using anti-AMPK total antibody. anti-tubulin antibody was used as loading control. (B) Cell growth of HepG2 and Huh7 cells transfected with siCNT or siAMPKα/α’ was monitored for 72 h in regular medium (CTR) or in the presence of 1.5 g/L methionine (MET). Cell growth is expressed as a ratio on the growth in control medium. * p < 0.05 compared with siCNT cells. (C) Cell growth of SW480 colorectal cancer cells, A549 lung cancer cells, and MCF7 breast cancer cells was monitored until 72 h in regular medium (CTR) or in the presence of 1.5 g/L methionine (MET) and/or 2 µM Compound C. Cell growth is expressed as a ratio on the growth in control medium. (D) Colony formation assay of SW480, A549, and MCF7 cells grown in regular medium (CTR) or in the presence of high methionine and/or 2 µM Compound C.

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