Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 7 (1), 12486

Role of the Transforming Growth Factor-β in Regulating Hepatocellular Carcinoma Oxidative Metabolism

Affiliations

Role of the Transforming Growth Factor-β in Regulating Hepatocellular Carcinoma Oxidative Metabolism

Jitka Soukupova et al. Sci Rep.

Abstract

Transforming Growth Factor beta (TGF-β) induces tumor cell migration and invasion. However, its role in inducing metabolic reprogramming is poorly understood. Here we analyzed the metabolic profile of hepatocellular carcinoma (HCC) cells that show differences in TGF-β expression. Oxygen consumption rate (OCR), extracellular acidification rate (ECAR), metabolomics and transcriptomics were performed. Results indicated that the switch from an epithelial to a mesenchymal/migratory phenotype in HCC cells is characterized by reduced mitochondrial respiration, without significant differences in glycolytic activity. Concomitantly, enhanced glutamine anaplerosis and biosynthetic use of TCA metabolites were proved through analysis of metabolite levels, as well as metabolic fluxes from U-13C6-Glucose and U-13C5-Glutamine. This correlated with increase in glutaminase 1 (GLS1) expression, whose inhibition reduced cell migration. Experiments where TGF-β function was activated with extracellular TGF-β1 or inhibited through TGF-β receptor I silencing showed that TGF-β induces a switch from oxidative metabolism, coincident with a decrease in OCR and the upregulation of glutamine transporter Solute Carrier Family 7 Member 5 (SLC7A5) and GLS1. TGF-β also regulated the expression of key genes involved in the flux of glycolytic intermediates and fatty acid metabolism. Together, these results indicate that autocrine activation of the TGF-β pathway regulates oxidative metabolism in HCC cells.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Glycolytic and oxidative metabolism in different HCC cell lines. (a) Immunofluorescence analysis of E-cadherin (green) and Vimentin (green). DAPI (blue). Scale bar represents 50 μM. (b) mRNA expression levels of TGF-β detected by qRT-PCR and normalized to PLC/PRF/5. Mean ± SD (n = 3). (c) OCR was analyzed at basal condition by high resolution respirometry (Oxygraph 2k) in DMEM supplemented with 10% FBS in 5 × 105 cells in suspension. Results are represented as OCR pmol/seg/ml of the first 10 min of respiration after stabilization. (d,e) Lactate production and glucose consumption was analyzed after 48 hours of cell culture in DMEM supplemented with 10% FBS and normalized to cell count. (f) Cells were treated with 2-DG in a range of concentration (0–10 mM). Cell viability was analyzed after 72 hours by crystal violet staining and normalized to un-treated control. (g) Cells were treated with metformin in a range of concentration (0–10 mM). Cell viability was analyzed after 72 hours by crystal violet staining and normalized to un-treated control. Mean ± SD (n = 3, p values are explained in the table below figure).
Figure 2
Figure 2
Metabolomic and transcriptomic analysis of PLC/PRF/5 and SNU449 cells: glucose and fatty acid metabolism. (a) Schematic diagram of selected metabolites of the glycolytic pathway presented as in the KEGG database (http://www.genome.jp/kegg/). The level of metabolites is depicted by a box plot with whiskers (min to max). Welch’s two-sample t-test was used to identify biochemicals that differed significantly between experimental groups (n = 5 for each group). *p < 0.05, **p < 0.01, ***p < 0.001. (b) Expression of selected genes related to the glycolytic pathway. Values < 1 indicate lower expression and values > 1 indicate higher expression, SNU449 as compared to PLC/PRF/5. (n = 3, p value indicated in the right column). (c) Expression of selected genes related to fatty acid β-oxidation and fatty acid synthesis was detected by qRT-PCR and normalized to PLC/PRF/5. Mean ± SD. (n at least 3). *p < 0.05, **p < 0.01, ***p < 0.001.
Figure 3
Figure 3
Metabolomic and transcriptomic analysis of PLC/PRF/5 and SNU449 cells: differences in the TCA cycle and glutamine metabolism. (a) Left: Schematic diagram of the TCA cycle as presented in the KEGG database (http://www.genome.jp/kegg/). The level of metabolites is depicted by a box plot with whiskers (min to max). Welch’s two-sample t-test was used to identify metabolites that differed significantly between experimental groups (n = 5 for each group). *p < 0.05, **p < 0.01, ***p < 0.001. Right: Expression of FH and selected genes related to the glutamine metabolism pathway. Values < 1 indicate lower expression and values > 1 indicate higher expression, SNU449 as compared to PLC/PRF/5. (n = 3, p value indicated in the right column). (b) Metabolites from the glutamine/glutamate pathway presented in fold comparing SNU449 to PLC/PRF/5. Welch’s two-sample t-test was used to identify metabolites that differed significantly between experimental groups (n = 5 for each group, p value indicated in the right column). (c) PLC/PRF/5 cells and SNU449 were cultivated in DMEM medium (25 mM glucose) without glutamine supplemented with 10% FBS. Cell viability was analyzed after 24, 48 and 72 hours by crystal violet staining and normalized to control (2 mM glutamine). Mean ± SD (n = 3), **p < 0.01, ***p < 0.001.
Figure 4
Figure 4
13C isotopomer distribution from fully labeled glutamine/glucose. (a) PLC/PRF/5 and SNU449 cells were exposed to 4 mM fully labeled glutamine (U-13C5-Glutamine) in a medium containing 25 mM glucose and 10% dialyzed FBS. (bd) PLC/PRF/5 and SNU449 cells were exposed to 25 mM fully labeled glucose (U-13C6-Glucose) in a medium containing 4 mM glutamine and 10% dialyzed FBS. As 13C from glutamine/glucose is distributed among various metabolites, their mass increases proportionally to the number of incorporated carbons. This increase in mass was detected by GC-MS. Isotope distribution in metabolites is marked as m + x, where the m stands for natural mass of the metabolite and the x indicates number of incorporated 13C carbons. Mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001.
Figure 5
Figure 5
Seahorse analysis of OXPHOS and glycolysis in HCC cells. Role of the TGF-β pathway. (a,b) OCR normalized to protein content in PLC, TβT-PLC (a) and SNU449sh-, SNU449shTβRI cells (b) incubated 30 minutes prior experiment in XF assay medium supplemented with 5 mM glucose and 2 mM glutamine and consecutively injected with oligomycin (1 μM), FCCP (1.5 μM), antimycin (1 μM) and rotenone (1 μM). Continuous OCR values (pmoles/min/µg protein) are shown. Mitochondrial functions were analysed as explained in Supplementary materials and methods. The % of ATP-linked OCR was calculated as ATP-linked OCR/basal OCR. Mean ± SEM (n at least 6 from three independent experiments). *p < 0.05, **p < 0.01, ***p < 0.001. (c,d) ECAR in PLC, TβT-PLC (c) and SNU449sh-, SNU449shTβRI cells (d) incubated 30 minutes prior experiment in XF assay medium supplemented with 2 mM glutamine and consecutively injected with glucose (10 mM), oligomycin (1 μM) and deoxyglucose (50 mM). Mean ± SEM (n at least 9 from three independent experiments). Continuous ECAR values (mpH/min/µg protein) are shown. Glycolytic functions were analysed as explained in Supplementary materials and methods. Mean ± SEM (n = 9 from three independent experiments). Glucose consumption (mg/105 cells) was measured after 48 hours of culture in DMEM supplemented with 10% FBS and normalized to cell number. Mean ± SD (n = 3).
Figure 6
Figure 6
Role of the TGF-β pathway on glutamine metabolism in HCC cells. TβT-PLC cells were compared to PLC cells. SNU449sh- cells were compared to SNU449shTβRI cells. (a) Metabolites from the glutamine/glutamate metabolism pathway presented in fold. Welch’s two-sample t-test was used to identify metabolites that differed significantly between experimental groups (n = 5 for each group, p value indicated in the right column). (b) The level of respective metabolites that changed significantly in both conditions is depicted by a box plot with whiskers (min to max). Mean ± SD (n = 5 for each group); *p < 0.05, **p < 0.01, ***p < 0.001. (c) Expression of selected genes related to the glutamine metabolism. Values < 1 indicates lower expression and values > 1 indicates higher expression, as compared to respective controls (n = 3, p value indicated in the right column).
Figure 7
Figure 7
Role of TGF-β in the regulation of glucose metabolism. (a) Left: Lactate levels between PLC and TβT-PLC are depicted by a box plot with whiskers (min to max). Welch’s two-sample t-test was used to identify biochemicals that differed significantly between experimental groups (n = 5 for each group). Right: Expression of LDHA and LDHB. Values < 1 indicate lower expression and values > 1 indicate higher expression, TβT-PLC versus PLC (n = 3, p value indicated in the right column). (b) Left: Schematic diagram of selected metabolites of the glycolytic pathway presented as in the KEGG database (http://www.genome.jp/kegg/). The level of respective metabolites between SNU449sh- and SNU449shTβRI is depicted by a box plot with whiskers (min to max). Welch’s two-sample t-test was used to identify biochemicals that differed significantly between experimental groups (n = 5 for each group). *p < 0.05 Right: Expression of selected genes related to the specific parts of the pathway. Values < 1 indicate lower expression and values > 1 indicate higher expression, SNU449sh- versus SNU449shTβRI (n = 3, p value indicated in the right column). (c) Expression of PKLR and PKM2 in TβT-PLC versus PLC and in SNU449sh- versus SNU449shTβRI. Values < 1 indicate lower expression and values > 1 indicate higher expression. (n = 3, p value indicated in the right column).

Similar articles

See all similar articles

Cited by 6 articles

See all "Cited by" articles

References

    1. Fabregat I, et al. TGF-β signalling and liver disease. FEBS J. 2016;283(12):2219–2232. doi: 10.1111/febs.13665. - DOI - PubMed
    1. Giannelli G, et al. The rationale for targeting TGF-β in chronic liver diseases. Eur J Clin Invest. 2016;46(6):349–361. doi: 10.1111/eci.12596. - DOI - PubMed
    1. Pavlova N, Thomson C. The emerging hallmarks of cancer metabolism. Cell Metab. 2016;23(1):27–47. doi: 10.1016/j.cmet.2015.12.006. - DOI - PMC - PubMed
    1. Liberti M, Locasale J. The Warburg Effect: How Does it Benefit Cancer Cells? Trends Biochem Sci. 2016;41(3):211–218. doi: 10.1016/j.tibs.2015.12.001. - DOI - PMC - PubMed
    1. Llovet, J., Villanueva, A., Lachenmayer, A. & Finn, R. Advances in targeted therapies for hepatocellular carcinoma in the genomic era. Nat Rev Clin Oncol12(7) (2015). - PubMed

Publication types

MeSH terms

Feedback