Beyond the Warburg Effect: Oxidative and Glycolytic Phenotypes Coexist within the Metabolic Heterogeneity of Glioblastoma

Abstract

Glioblastoma (GBM) is the most aggressive primary brain tumor, with a median survival at diagnosis of 16-20 months. Metabolism represents a new attractive therapeutic target; however, due to high intratumoral heterogeneity, the application of metabolic drugs in GBM is challenging. We characterized the basal bioenergetic metabolism and antiproliferative potential of metformin (MF), dichloroacetate (DCA), sodium oxamate (SOD) and diazo-5-oxo-L-norleucine (DON) in three distinct glioma stem cells (GSCs) (GBM18, GBM27, GBM38), as well as U87MG. GBM27, a highly oxidative cell line, was the most resistant to all treatments, except DON. GBM18 and GBM38, Warburg-like GSCs, were sensitive to MF and DCA, respectively. Resistance to DON was not correlated with basal metabolic phenotypes. In combinatory experiments, radiomimetic bleomycin exhibited therapeutically relevant synergistic effects with MF, DCA and DON in GBM27 and DON in all other cell lines. MF and DCA shifted the metabolism of treated cells towards glycolysis or oxidation, respectively. DON consistently decreased total ATP production. Our study highlights the need for a better characterization of GBM from a metabolic perspective. Metabolic therapy should focus on both glycolytic and oxidative subpopulations of GSCs.

Keywords: energy metabolism; gene expression profiling; glioblastoma; glycolysis; oxidative phosphorylation; therapeutics.

Figure 1

(a) Heatmap of the scaled enrichment score (ES) obtained by Gene Set Variation Analysis (GSVA) with the samples grouped by their gene expression subtype (proneural, classical or mesenchymal) including those with high content of non-tumor tissue (low cellularity). (b) Clinically, standard imaging techniques such as 18F-FDG PET-MRI can classify tumors into low glucose uptake (upper) and high glucose uptake (lower). Upper images: Right thalamic glioblastoma shows patchy contrast enhanced areas on 3DT1 (right side) and no uptake of 18F-FDG PET (medium and left side). Lower images: Parietal recurrent glioblastoma in the left hemisphere shows heterogeneous enhancement on axial three-dimensional T1-weighted imaging (3DT1) and extensive uptake of 18F-FDG PET (right and medium side), despite high uptake in surrounding normal brain tissue. (c) Representative optical microscopy images of cellular morphology. Scale bar = 100 µm. Under each cell line, average distribution of total ATP production from extracellular acidification rate (ECAR)-linked ATP production and oxygen consumption rate (OCR)-linked ATP production in basal (non-treated) conditions. (d) Seahorse XF Energetic Map. GBM18 and GBM38 clustered together as highly glycolytic-like cells. GBM27 displayed the highest variation in the metabolic profiles, with increased mitochondrial respiration, at a similar level to GBM18, but, in comparison, lower glycolysis. U87MG were not as metabolically active as glioma stem cells (GSCs). Data from three independent experiments, each with n = 3, normalized to total protein concentration (µg/mL).

Figure 2

Cancer metabolism at a glance, with experimental in vitro IC50 values for selected metabolic inhibitors. Glucose enters cells and undergoes glycolysis, converted into pyruvate. Cancer cells can divert up to 85% of pyruvate to lactate, regardless of the presence of oxygen (Warburg effect, yielding two net ATP); an estimated 10% of pyruvate goes towards biosynthesis and 5% to OXPHOS [56]. In normal, non-tumoral cells, the majority of pyruvate undergoes OXPHOS (30–32 ATP molecules). To decrease the Warburg effect and facilitate oxidative metabolic reprogramming, PDKs can be inhibited by dichloroacetate (DCA), supporting the entry of pyruvate in the mitochondria, and LDH can be targeted via sodium oxamate (SOD). “Glutamine addiction” can be regulated by glutaminase inhibitors such as 6-diazo-5-oxo-L-norleucine (DON) [57]. Lastly, metformin hydrochloride (MF) has pleiotropic effects: ETC complex I inhibition leads to downstream signaling via AMPK and mTOR [58]. In color-matching boxes, we display concentrations required for 50% viability inhibition (IC50) after 72 h of treatment. One-way ANOVA statistical significance of three biological experiments was calculated with normalized raw fluorometric data; p < 0.05 *; p < 0.01 **; p < 0.001 ***. Abbreviations: ETC (electron transport chain), mTOR (mammalian target of rapamycin), PDK (pyruvate dehydrogenase kinase), PDH (pyruvate dehydrogenase), GLS (glutaminase), LDH (lactate dehydrogenase), GLS (glutaminase), ECAR (extracellular acidification rate), OCR (oxygen consumption rate).

Figure 3

(a) Expression profiles of target enzymes for our selection of metabolic drugs under basal conditions determined by RT-qRT-PCR. Representative results from a minimum of two replicates (n = 2). One-way ANOVA with Tukey correction. p < 0.05 *; p < 0.01 **; p < 0.001 ***. (b) Western Blot analysis at 30 min, 60 min, 2 h, 6 h after MF 72h-IC50 treatment for AMPKα and phospho-Thr172 AMPKα. (c) Western Blot analysis after 6 h of treatment with respective DCA 72h-IC50 doses for phospho-Ser293 PDH-E1 and total PDH-E1.

Figure 4

(a) IC50 values at 72 h and mechanism of action of radiomimetic drug bleomycin (BM). Bleomycin is a large molecule (~1.5 kDa) and cannot freely diffuse cell membranes; it is transported into cells either alone or as a bleomycin-Cu(II) complex, then reduced to bleomycin-Cu(I), which reacts with oxygen leading to DNA strand breaks. Successful chemotherapy with bleomycin is dependent on active transport; however, there is currently no consensus about the uptake mechanism or the transporters involved. Bleomycin-Cu(I) can also dissociate inside the cell to form bleomycin-Fe(II) complexes, transforming into «activated bleomycin species» resulting in DNA fragmentation and chromosomal aberrations. Complexes with zinc (II), iron (II) and cobalt (III) have also been characterized. Calculated IC50, as per inner salt (MTS) assay, with a minimum of two biological replicates. One-way ANOVA with Tukey correction, p < 0.05 *; p < 0.01 **; p < 0.001 ***. (b) Viability profiles relative to control mesenchymal stem cells (hMSCs) treated with all calculated 72h-IC50 doses (n = 2). One-way ANOVA with Dunn’s correction, p < 0.05 *; p < 0.01 **; p < 0.001 ***.

Figure 5

(a–c) Combinatory drug studies between metabolic drugs (MF, DCA, DON) and bleomycin (BM). From top to bottom, three graphs for each cell line comprehensively describe synergy/antagonism. First graph: Bar charts of decrease in viability (% of affected cells) relative to control. Second graph: Combination index (CI) is given as a function of the fraction of affected cells (Fa) by the drug combination with a continuous line. The central dashed line indicates a CI = 1. According to the Chou-Talalay’s Combination Index Theorem, CI = 0.9 to 1.1 indicates an additive effect. CI < 1 is indicative of synergism, whereas CI > 1 indicates antagonism. The vertical bars indicate 95% confidence intervals for CI values based on Sequential Deletion Analysis (SDA); in some cases, SDA values cannot be graphed in CompuSyn software, but they were always calculated and are available in Supplementary File S1. Third graph: The Dose-Reduction Index (DRI) (also known as the Chou-Martin plot) signifies how many folds of dose-reduction for each drug at any given effect (Fa) are allowed in synergistic combination. In blue, metabolic drug DRI index; in red, bleomycin DRI index. DRI = 1 indicates no dose-reduction, DRI > 1 favorable dose-reduction and DRI < 1 no favorable dose-reduction. All experiments were performed in two biological replicates (n = 2).

Figure 6

(a) Changes in metabolic phenotypes after IC50 treatment for 72 h with metformin (MF), dichloroacetate (DCA) and 6-Diazo-5-oxo-L-norleucine (DON). (b) XF ATP Rate Index for GSCs and U87MG. The ATP Rate Index is the ratio of the mitoATP Production Rate divided by glycoATP Production Rate, indicating higher oxidative or glycolytic bioenergetic profile.