Although LPS stimulation and bacterial infections have been shown to shift cellular metabolism towards a more glycolytic phenotype in various cell types (9–16), a number of studies have reported that human macrophages do not increase glycolysis in response to bacterial stimuli (35, 36). To assess the potential of targeting host metabolic pathways to treat intracellular bacterial infections through HDT, we evaluated the effect of Stm and Mtb on cellular metabolism in primary human macrophages. Extracellular acidification rates (ECAR) and oxygen consumption rates (OCR) were measured as indicators of glycolytic and OXPHOS energy phenotypes, respectively, using a Seahorse XFe96 Extracellular Flux Analyzer(experimental outline in Figure 1A). Macrophages were pretreated with bacterial lysates, which we used as a proxy for live infections, due to safety restrictions in the Seahorse XFe96 Extracellular Flux Analyzer laboratory. At baseline, M1 displayed a higher general metabolic activity compared to M2 as evidenced by elevated basal glycolysis and OXPHOS levels (Figures 1B, C; note the different scales on the y-axes). Basal glycolysis was increased in a dose-dependent manner upon 4 hours stimulation with 1 or 10 µg/ml Stm or Mtb lysate, both in M1 and in M2 compared to unstimulated macrophages (Figures 1B, C and Supplemental Figure 1). OXPHOS was significantly increased upon stimulation with 10 µg/ml Mtb lysate in M2, but not upon stimulation with Stm lysate. The maximum respiration and glycolytic capacity were significantly increased upon stimulation with 10 µg/ml Mtb lysate in M1 and M2 or M2 only, respectively, but not upon stimulation with Stm lysate or a low concentration (1 µg/ml) of Mtb lysate. Calculation of the ECAR/OCR ratios to determine the overall metabolic shift induced in human M1 and M2 following stimulation with Stm or Mtb demonstrated a bacterially-induced shift towards a more glycolytic phenotype (Figure 1D).

To assess modulation of metabolism by live bacteria, extracellular lactate levels (as an indicator of glycolysis) were determined in macrophages infected with live Stm and Mtb (Figure 1E). M1 and M2 secreted significantly higher levels of lactate upon Stm infection compared to uninfected cells, supporting infection-induced glycolysis. Live Mtb infection significantly increased lactate production in M2 compared to uninfected cells, but surprisingly not in M1. The absence of lactate production by Mtb-infected M1 corroborates recent findings in mouse bone marrow-derived macrophages (BMDMs) and human monocyte-derived macrophages that Mtb is able to actively suppress induction of glycolysis (37, 38).

To confirm induction of a metabolic shift at the transcriptional level, we studied mRNA expression levels of the glycolytic key gene hexokinase 2 (HK2) and the TCA cycle key genes succinate dehydrogenase complex flavoprotein subunit A (SDHA) and isocitrate dehydrogenase 2 (IDH2) in M1 and M2. HK2 mRNA levels were significantly increased 4 hours post Stm infection in M2 (Figure 1F), but were not altered in Stm- or Mtb-infected M1(Figure 1B). SDHA and IDH2 mRNA levels were downregulated in Stm- and Mtb-infected M1 and M2, albeit to different extents, suggesting decreased activity of the TCA cycle pathway. Taken together, our data suggest that Stm infections are able to shift human M1 and M2 metabolism towards a more glycolytic phenotype.

To study whether the increased glycolysis contributes to host defense, we first treated Stm- and Mtb-infected M1 and M2 with glycolysis inhibitor 2-deoxy-D-glucose (2-DG), since earlier reports showed that 2-DG increased the burden of Mtb in murine BMDMs and in mouse lungs (39, 40). PKA inhibitor H-89 was used as positive control and in agreement with previous findings (41, 42) significantly lowered outgrowth of Stm, while being less effective in reducing Mtb bacterial load (Figures 2A, B). Surprisingly, treatment of human M1 with 2-DG significantly decreased outgrowth of intracellular Stm, but only decreased intracellular Mtb in M2 at the highest concentration (5 mM). Importantly, 2-DG did not show cytotoxicity in both macrophage subsets (Figure 2C). The finding that 2-DG treatment did not (M1) or only moderately (M2) affect Mtb bacterial load in primary human macrophages contrasts with earlier reports, which concluded that 2-DG treatment increased the burden of Mtb in murine BMDMs and in mouse lungs (39, 40). However, 2-DG has recently been described to decrease Legionella pneumophila survival independently of inhibition of glycolysis in mouse BMDMs (43) and also, high concentrations of 2-DG have been shown to inhibit not only glycolysis but also OXPHOS in mouse BMDMs (44). Therefore, in order to be able to evaluate the effect of glycolysis independently from the use of 2-DG, we determined bacterial survival after substitution of D-glucose by D-galactose in the cell culture medium. Substitution of D-glucose by D-galactose reduces glycolytic flux and thus could independently validate our findings (44). Indeed, the absence of glucose significantly decreased Stm outgrowth in M1, but not M2, and tended to increase intracellular Mtb levels (Figures 2D, E). These data suggest that inhibition of glycolysis impacts Stm and Mtb in opposite directions; it can inhibit Stm outgrowth, while it can promote Mtb survival as described (39, 40). Also of importance, these data suggest that pathways other than inhibition of glycolysis are responsible for the antibacterial effect of 2-DG.

Next, we aimed to identify metabolic key genes that are modulated during Stm and Mtb macrophage infections. We performed computational analysis of published RNAseq data by Blischak et al. (45) to study the expression of metabolic genes (i.e. all genes in Reactome pathways ‘Glucose metabolism’ and ‘TCA cycle and respiratory electron transport’) in human M2 macrophages infected for 18 hours with Stm or Mtb (MOI of 5) compared to their time-matched controls (mock infection). Stm infection had a more prominent effect on the regulation of glycolytic genes and genes involved in the TCA cycle and respiratory electron transport compared to Mtb (Supplemental Figure 2). Two key glycolytic genes, enolase (ENO) 2 and HK2, were significantly upregulated 18 hours post Stm or Mtb infection. In contrast, most genes involved in the TCA cycle and respiratory electron transport were downregulated upon Stm and Mtb infection.

We hypothesized that metabolic genes that are modulated during Stm and Mtb infections may provide novel targets for HDT. Surprisingly, among the most up- and downregulated metabolic genes during Stm and Mtb infection, two PDK isoforms were regulated in opposite directions: PDK4 was strongly downregulated upon Stm infection, whereas PDK1 was strongly upregulated upon Mtb infection (Supplemental Figure 3). The differential expression of PDK isoforms is interesting, because PDKs are regulators of the metabolic checkpoint PDC, linking glycolysis with TCA cycle activity and OXPHOS (Figure 3A). More detailed kinetic analysis of the expression levels of all PDK isoforms (PDK1-4) and other components of the PDC (PDP1, PDP2, PDHX, PDHA1 and PDHB) showed that several PDK isoforms and catalytic subunits of the PDC were significantly regulated upon infection with Stm, in contrast to infection with Mtb (Figure 3B), suggesting that pyruvate metabolism was modulated in human macrophages during Stm infection.

To confirm and extend the data by Blischak et al. to both M1 and M2 infection models, we measured the expression changes of genes involved in activation of the PDC (i.e.: PDK1-4, PDP1-2) upon infection with Stm and Mtb in both M1 and M2. In agreement with the data obtained by Blischak et al., gene expression levels of PDK2, PDK4 and PDP2 were significantly downregulated in Stm-infected human M1 and M2 (Figure 3C). A similar trend could be observed in Mtb-infected macrophage subsets, albeit not significant (Figure 3D). Taken together, these data indicate that pyruvate metabolism is markedly modulated during Stm infections and to a lesser extent during Mtb infections in human M1 and M2, as shown by a downregulation of PDK2, PDK4 and PDP2 at the transcriptional level.

To study the effect of pyruvate metabolism during Stm and Mtb infections in more detail, we aimed to modulate this pathway through chemical inhibition of PDK using dichloroacetate (DCA). To validate inhibition of PDKs as the mechanistic target of DCA, we first examined whether DCA treatment in Stm and Mtb-infected macrophage subsets resulted in dephosphorylation of PDH at serine residues 293 and 300, which corresponds with increased PDC activation. Pyruvate, a natural PDK inhibitor, was added as a positive control for PDH dephosphorylation. Quantification of the western blot protein band intensities revealed that both DCA treatment and addition of pyruvate resulted in PDH dephosphorylation at serine residues 293 and 300 in Stm and Mtb-infected M1 and M2 compared to DMSO (Figure 4A). In summary, these data corroborate earlier findings (46, 47) that DCA treatment increases PDC activity, validating PDKs as functional targets of DCA.

Since DCA has been reported to shift metabolism from glycolysis to OXPHOS in various cell lines (32, 46, 48–50), we next evaluated whether DCA could reverse the Stm- and Mtb-induced metabolic shift towards glycolysis that was predominantly observed in human M1 macrophages (Figure 1C). First, we studied gene expression levels of metabolic key genes (HK2, PDK2, PDK4, IDH2, SDHA) and downstream targets of succinate accumulation (HIF1A and VEGF) (19, 22, 51), as an indication of TCA cycle activity, in human macrophages (Figure 4B). DCA did not significantly modify metabolic gene expression at the transcriptional level, but VEGF was significantly increased by DCA in Stm-infected M1, in accordance with increased TCA cycle activity (Figure 4C). Next, we studied the effect of DCA on OCR and ECAR levels in macrophages that were prestimulated with Stm or Mtb lysate (Figure 4D). DCA treatment resulted in increased OCR levels and decreased ECAR levels in M1, consistent with a classical DCA-induced shift from glycolysis to OXPHOS (Figure 4E). Strikingly, a shift from glycolysis to OXPHOS could not be detected in M2, indicating that PDC activation does not necessarily lead to glycolytic reprogramming and that this is cell-type specific.

In conclusion, DCA can be used as a chemical tool to study the effect of PDC activation on Stm and Mtb infections in human macrophages, which is accompanied by metabolic rewiring from glycolysis to OXPHOS in a M1 cell type specific manner.

To study whether PDC activation can modulate effector functions of human macrophages, we assessed the production of 41 chemokines/cytokines by multiplex beads assay. Clearly, Stm-infection markedly induced the production of pro-inflammatory cytokines, anti-inflammatory cytokines and chemokines, which contrasted with the lower magnitude changes seen for Mtb-infected macrophages (Figure 5A and Supplemental Table 1). DCA treatment did not have any major impact on overall chemokine/cytokine secretion profiles of both M1 and M2, although several individual chemokines and cytokines were significantly modified by DCA in M1 and M2, dependent on the cell-type and the infection type (Stm versus Mtb) (Figure 5B and Supplemental Table 1).

Since PDC activation by DCA can promote OXPHOS, which is associated with mitochondrial ROS production (46, 52), we next assessed the effect of DCA on ROS production by M1 and M2. Interestingly, DCA significantly induced ROS production in both M1 and M2 and even was a more potent ROS inducer compared to positive control phorbol 12-myristate 13-acetate (PMA) (Figures 5C, D). Surprisingly, ROS production was increased immediately after administration of DCA, even before the first measurement could be taken. ROS production was still observed 1h post DCA stimulation in M1, but reached plateau levels in M2, suggesting that DCA-induced ROS production was halted. DCA-induced ROS production could be partially inhibited by N-acetylcysteine (NAC), which is a scavenger of ROS.

Since we showed that PDC is a target of interest for HDT and DCA activated PDC, resulting in altered metabolism and increased ROS production, we investigated whether DCA could impact bacterial control by human macrophages. Macrophages were treated with DCA or with H-89 as positive control. As expected, H-89 markedly decreased Stm and tended to decrease Mtb in M1 and M2 (41). Exposure of Stm- and Mtb-infected M1 and M2 to 5-20 mM DCA resulted in a profound decrease in Stm but not Mtb outgrowth (Figure 6A). Importantly, cell viability was not affected (Figure 6B). Interestingly, Stm outgrowth was significantly more inhibited by DCA treatment in M1 compared to M2, which is in agreement with the more profound effect of DCA on M1 metabolism (Figure 4E). Furthermore, DCA decreased the percentage of Stm-infected M1 and M2 cells compared to DMSO as shown by flow cytometry using DsRed-expressing Stm (Supplemental Figure 4A). Interestingly, the effect of DCA on inhibiting intracellular Stm outgrowth was already significant after 2 hours of DCA treatment in M2 and after 4 hours in both macrophage subsets, suggesting that DCA induces a host protective response during the early phase of infection (Supplemental Figure 4B).

Not unexpectedly, DCA had little effect on intracellular Mtb in M1 and M2 (Figure 6A). Even a prolonged exposure (up to 72h) of Mtb-infected macrophages to DCA did not result in reduction of Mtb CFU levels (Supplemental Figure 4C), suggesting that Mtb modulates host-signaling pathways in a manner different from Stm, that cannot be redressed by DCA. Potential direct antibiotic properties of DCA were excluded by adding DCA to Stm and Mtb cultures in the absence of human cells, showing that DCA acts via host-directed pathways in human macrophages to control intracellular Stm (Figure 6C).

Taken together, our data suggest that DCA inhibits intracellular Stm infection, but not Mtb infection, in a host-directed manner. DCA reduced Stm levels rapidly after administration, suggesting that DCA is a potent molecule for treatment against Stm.

Dimethyl sulfoxide (DMSO), 2-deoxy-D-glucose (2-DG), D-(+)-glucose, D-(+)-galactose, sodium dichloroacetate (DCA), sodium pyruvate, sodium L-lactate, H-89 dihydrochloride hydrate (H-89), and phorbol 12-myristate 13-acetate (PMA) were obtained from Sigma-Aldrich (Zwijndrecht, The Netherlands). Carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP) was obtained from Seahorse Bioscience (Billerica, MA, USA). FITC Anti-human CD14 monoclonal (M5-E2) and A647 anti-human CD163 monoclonal (RM3/1) were purchased from Sony Biotechnology Inc. (San Jose, California, USA). PE anti-human CD11b monoclonal (ICRF44) was obtained from BD Bioscience (Vianen, The Netherlands). Anti-human PDH-E1a (pSer²⁹³) and anti-human PDH-E1a (pSer³⁰⁰) polyclonal antibodies were purchased from Merck Millipore (Amsterdam, The Netherlands). Anti-human vinculin monoclonal (hVIN-1) was purchased from Sigma-Aldrich and anti-human PDH monoclonal (9H9AF5) from Thermo Fisher Scientific (Landsmeer, The Netherlands). Secondary stabilized peroxidase conjugated antibodies goat anti-rabbit IgG (H+L) and goat anti-mouse IgG (H+L) were purchased from Thermo Fisher Scientific.

Primary human macrophages were obtained as described earlier (25). Briefly, human peripheral blood mononuclear cells (PBMCs) were isolated from buffy coats obtained from healthy donors with informed consent by density gradient centrifugation over Ficoll/amidotrizoaat (Pharmacy, LUMC, The Netherlands). CD14+ monocytes were isolated by magnetic cell sorting using anti-CD14-coated microbeads (Miltenyi Biotec, Auburn, CA) and cultured at 37°C/5% CO₂ in Gibco Roswell Park Memorial Institute (RPMI) 1640 medium (Thermo Fisher Scientific) supplemented with 10% FBS, 2 mM L-Alanyl-L-Glutamine (PAA, Linz, Austria), 100 units/ml penicillin, 100 µg/ml streptomycin and either 5 ng/ml granulocyte-macrophage colony-stimulating factor (GM-CSF, Thermo Fisher Scientific) to promote M1-differentiation or 50 ng/ml macrophage colony-stimulating factor (M-CSF, R&D Systems, Abingdon, United Kingdom) to promote M2-differentiation. After 6 days of differentiation, macrophages were detached by trypsinization and gentle scraping, counted and seeded for downstream application in RPMI 1640 medium (10% FBS and 2 mM L-Alanyl-L-Glutamine). For experiments, cells were incubated overnight at 37°C/5% CO₂. In parallel, M1 and M2 macrophage phenotypes were validated based on surface marker expression (M1: CD14^low, CD163^low, CD11b^high; M2: CD14^high, CD163^high, CD11b^low) by flow cytometry based on cytokine production (IL-10 and IL-12) following overnight LPS stimulation (100 ng/ml) as described before (81). Cells were plated on round-bottom 96 wells plates in a concentration of 75,000 cells/well in RPMI 1640 and washed in 150 µl PBS/0.1% BSA. Fc receptors were blocked by adding 50 µl of 5% human serum (Sanquin, Amsterdam, the Netherlands) for 10 minutes. Cells were washed and stained with antibodies against CD14, CD163 and CD11b for 30 minutes at 4°C in the dark, washed twice and fluorescence intensity was measured on a Accuri™ C6 flow cytometer (BD Bioscience).

DsRed-expressing-Salmonella enterica serovar Typhimurium (Stm) strain SL1344 (42) was recovered from frozen stock and cultured at 37°C in Difco™ Luria-Bertani (LB) broth (BD Bioscience) containing 100 µg/ml ampicillin (Sigma-Aldrich). DsRed-expressing-Mycobacterium tuberculosis H37Rv (42) was cultured at 37°C in Difco™ Middlebrook 7H9 broth (BD Bioscience, Vianen, The Netherlands) containing 10% ADC (Becton Dickinson, Breda, The Netherlands), 0.5% Tween-80 (Sigma-Aldrich) and 2% Glycerol (Sigma-Aldrich). Stm and Mtb lysates were generated by harvesting log-phase liquid Stm and Mtb cultures. Cultures were washed twice and resuspended in PBS. 1 ml glass beads (0.1 mm, BioSpec Products, Breda, The Netherlands) were added to 1 ml bacterial suspension in PBS and lysates were generated using the Mini-BeadBeater-1 (BioSpec Products). After 5 minutes of incubation, cell lysates were transferred to a 1.5 ml Eppendorf tubes and protein concentration was quantified by Pierce™ BCA protein assay kit according to manufacturer’s instructions (Thermo Fisher Scientific).

Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were determined as a measure of mitochondrial respiration and glycolysis, respectively, by using the Agilent Seahorse XFe96 Extracellular Flux Analyzer (Seahorse Bioscience) according to manufacturer’s instructions and as described before (82). Briefly, cells were plated in a Seahorse XF cell culture microplate at a density of 30,000 cells/well and incubated overnight at 37°C/5% CO₂ in RPMI 1640 medium (10% FBS, 2 mM L-Alanyl-L-Glutamine). Cells were stimulated with 1 or 10 µg/ml Stm or Mtb lysate and incubated for 4 hours at 37°C/5% CO₂. Plates were washed with glucose-free RPMI 1640 medium (Sigma-Aldrich) containing 5% FBS and incubated for 1h at 37°C without CO₂. To determine the effect of chemical compounds on OCR and ECAR, cells were stimulated with 10 µg/ml Stm or Mtb lysate and inserted in the Seahorse XFe96 Analyzer directly. After insertion into the Seahorse XFe96 Analyzer, wells were sequentially injected with final concentrations of 10 mM D-glucose with or without chemical compounds, 1 µM oligo (ATP synthase inhibitor) and 2 µM FCCP (mitochondrial uncoupling agent) after 18, 54 and 72 minutes, respectively. Basal glycolysis was calculated as the difference between lowest ECAR measurement pre-D-glucose injection and highest ECAR measurement post-D-glucose injection. The glycolytic capacity was calculated as the difference between lowest ECAR measurement pre-D-glucose injection and highest ECAR measurement post-oligo injection. OXPHOS was calculated as the difference between highest OCR measurement pre-oligo injection and lowest OCR measurement post-oligo injection. Maximal respiration was calculated as the difference between lowest OCR measurement pre-FCCP injection and highest OCR measurement post-FCCP injection. Basal glycolysis was divided by OXPHOS to calculate ECAR/OCR ratios.

Ongoing Stm cultures were diluted one day before infection and again at 2-3 hours prior to infection to reach log phase (OD₆₀₀ between 0.4-0.6) during infection. Mtb cultures were diluted one day prior to infection to reach log phase (OD₆₀₀ between 0.4-0.6) during infection. Cells were infected with Stm or Mtb with a multiplicity of infection (MOI) of 10. Accuracy of the MOI was validated by plating serial dilutions of the Stm inoculum on Difco™ LB agar plates (BD Bioscience) or the Mtb inoculum on Difco™ Middlebrook 7H10 agar (BD Bioscience) plates containing 10% OADC (Becton Dickinson) and 5% glycerol. Cells seeded 1 day prior to infection in flat-bottom 96-well plates were inoculated with 100 µl bacterial suspension and cells seeded in flat-bottom 24-well plates with 500 µl bacterial suspension in RPMI 1640 (10% FBS, 2 mM L-Alanyl-L-Glutamine). Plates were centrifuged for 3 minutes at 800 rpm and incubated for 20 minutes for Stm infections and 1 hour for Mtb infections at 37°C/5% CO₂. Extracellular bacteria were removed by washing and incubation with fresh RPMI 1640 (10% FBS, 2 mM L-Alanyl-L-Glutamine) containing 30 µg/ml gentamicin sulphate (Lonza BioWhittaker, Basel, Switzerland) for 10 minutes and cells were incubated in gentamicin sulphate-containing medium (5 µg/ml) in the presence or absence of chemical compounds or an equal amount of vehicle control DMSO (0.1% v/v) for 4 hours or overnight at 37°C/5% CO₂.

Cell supernatants were harvested and kept at -20°C until further use. 10 µl of 100 mM sodium l-lactate standard or sample was added in triplicate to a flat bottom 96-well plate (Greiner Bio-One, Alphen a/d Rijn, The Netherlands). 200 µl of reaction mix (0.74 mM NAD, Roche Applied Science; 0.4 mM glycine, Sigma-Aldrich; 0.4 M hydrazine hydrate, Sigma-Aldrich) and 2 µl of three times diluted L-Lactate Dehydrogenase (LDH) from rabbit muscle (Roche Applied Science, Bayern, Germany) was added to each well to allow conversion of lactate to pyruvate by LDH and trapping of the newly formed pyruvate: Lactate + NAD⁺ <–> Pyruvate + NADH + H⁺. Plates were incubated on a shaker for 90 minutes at RT and NADH levels were measured spectrophotometrically at 340 nm using a SpectraMax i3x plate reader (Molecular Devices, CA, USA) before and after incubation as an indicator of lactate production.

Intracellular bacterial burden was determined by colony forming unit (CFU) assays as described previously (81). Cells were washed once with PBS and lysed in 0.05% sodium dodecyl sulfate (SDS) solution (Thermo Fisher Scientific). Serially diluted cell lysates were plated on LB agar (Stm) or on 7H10 agar containing 10% OADC and 5% Glycerol (Mtb). Plates were incubated at 37°C and CFUs were counted from triplicate wells.

The number and percentage of dead cells based on plasma membrane integrity of the adherent cell population was quantified by analysis of microscopy images. Cells in 96-well flat bottom plates (30,000 cells/well) were stained with 2 µg/ml propidium iodide (PI, Sigma-Aldrich) and 2 µg/ml Hoechst 33342 (H3570, Sigma-Aldrich) in 40 µl/well phenol red-free RPMI 1640 (Sigma-Aldrich) supplemented with 10% FBS and 2 mM L-Alanyl-L-Glutamine and incubated for 5 minutes at room temperature (RT). Cells were imaged using a Leica AF6000 LC fluorescence microscope (Leica Microsystems, Wetzlar, Germany) combined with a 10x dry objective. Total and dead cell numbers were quantified by respectively counting the nuclei and the number of PI-positive cells using Fiji/ImageJ software (83).

Compounds were diluted in Difco™ LB broth or in Difco™ Middlebrook 7H9 broth and added (100 µl/well) to 100 µl/well bacterial culture (OD₆₀₀ of 0.1) in flat bottom 96-well plates. Gentamycin (50 µg/ml) and rifampicin (20 µg/ml) were added as positive controls to inhibit Stm and Mtb growth, respectively. Absorbance was measured directly after plating and after 16-18 hours for Stm growth and at day 2, 5, 8 and 12 for Mtb growth at 550 nm on a Mithras LB 940 plate reader (Berthold Technologies, Bad Wildbad, Germany).

300,000 cells/well in a flat bottom 24 wells plate were lysed in 350 µl TRIzol™ reagent (Thermo Fisher Scientific). RNA was isolated according to manufacturers’ instructions and diluted to 50 ng/µl. cDNA and 14-cycle preamplification was performed according to Fluidigm protocols (Biomark Fluidigm, South San Francisco, CA, United States). Briefly, cDNA was synthesized by addition of 1 µl reverse transcription master mix to 3 µl of RNase-free water and 1 µl of 50 ng/µl RNA per sample, and incubated using the following thermal protocol: 5 min at 25°C, 30 min at 42°C, 5 min at 85°C and then stored at -20°C until use. For preamplication, 1.25 µl of cDNA was added to 1 µl of Preamp Fluidigm Master Mix, 1.25 µl of Pooled TaqMan primer mix and 1.5 µl of RNase-free water per sample, and incubated using the following thermal protocol: 2 min at 85°C, 14 cycles of 15s at 95°C and 4 min at 60°C and stored at -20°C until use. For quantitative real-time PCR reactions, 1.35 µl preamplified cDNA, 1.5 µL 2X Taqman Gene Expression PCR Master Mix and 0.15 µL 20X GE Sample Reagent was used per reaction. Flex Six fluidics chips were primed with control line fluid and loaded with 3 µl preamplified samples in the appropriate inlets. All Real-Time PCR reactions were performed in the BioMark real-time PCR system using the GE FLEXSix Standard v1 thermal protocol. Cycle threshold (Ct) values were calculated using BioMark Real-time PCR Analysis software. Changes of expression values were calculated as the log2 Fold Change (FC) between the target gene and the reference gene GAPDH. The following FAM-MGB Taqman primer sets were used: GAPDH (Hs02758991_g1), PDK1 (Hs01561847_m1), PDK2 (Hs00176865_m1), PDK3 (Hs00178440_m1), PDK4 (Hs01037712_m1), PDP1 (Hs01081518_s1), PDP2 (Hs01934174_s1), HK2 (Hs00606086_m1), SDHA (Hs00188166_m1), IDH2 (Hs00953879_m1), HIF1A (Hs00153153_m1) and VEGFA (Hs00900055_m1).

Cells (300,000 cells/well in 24-wells plates) were lysed using 100 µl/well EBSB buffer (10% v/v glycerol, 3% SDS, 100 mM Tris-HCl, pH 6.8) supplemented with cOmplete™ EDTA-free protease inhibitor cocktail (Sigma-Aldrich) and phosphatase inhibitor (PhosSTOP EASYpack Sigma-Aldrich) in the concentration of one tablet each per 10 ml. Samples were boiled for 10 minutes at 95°C and stored at -20°C until use. Sample concentrations were determined using Pierce™ BCA protein assay kit (Thermo Fisher Scientific) according to manufacturer instructions and diluted to equal concentrations in Laemmli sample buffer (Biorad) containing 10% β-mercaptoethanol (Sigma-Aldrich). 12.5 µl of sample per lane was loaded on a 15-well 4–20% Mini-PROTEAN^® TGX™ Precast Protein Gel (Bio-Rad Laboratories, Veenendaal, the Netherlands) and Amersham ECL Rainbow Molecular Weight Marker (Sigma-Aldrich) was added as reference. Proteins were transferred to methanol-activated Immun-Blot PVDF membranes (Biorad) in Tris-glycine buffer (25 mM Tris, 192 mM glycine and 20% methanol). Membranes were blocked for 1 hour in polysorbate 20 tris-buffered saline (TTBS) supplemented with bovine serum albumin fraction V (BSA, 5% w/v, Roche Diagnostics, Almere, The Netherlands) and incubated with the indicated antibodies in 5% BSA/TTBS overnight at 4°C. Membranes were washed for 45 minutes in TTBS and stained with secondary antibodies in 5% BSA/TTBS for 2 hours at RT. Membranes were washed for 30 minutes with TTBS prior to revelation using enhanced chemiluminescence (ECL)™ Prime Western Blotting System reagent (GE Healthcare, Hoevelaken, The Netherlands). Imaging was performed on a ChemiDoc XRS+ (Bio-Rad). Protein bands were quantified using ImageJ/Fiji software (83) and normalized to vinculin.

Procedure was performed as described previously (81). Cells (40,000 cells/well) were seeded in black ultra-thin clear flat bottom 96-well plates (Corning) in RPMI 1640 (10% FBS, 2 mM L-Alanyl-L-Glutamine) and incubated overnight at 37°C/5% CO₂. Cells were washed with 150 µl PBS/well and RPMI 1640 without phenol red was added and background fluorescence was measured using a SpectraMax i3x plate reader (λ ex 493 nm and λ em 522 nm) at 37°C. Cells were washed with 150 µl PBS/well. 50 µl/well of 5 µM CM-H2DCFDA, a general ROS probe, in RPMI 1640 without phenol red was added for 30 minutes at 37°C/5% CO₂. Cells were washed with 100 µl PBS/well and 100 µl/well RPMI 1640 without phenol red was added containing DMSO (0.1% v/v), DCA (10 mM) or PMA (300 nM) as positive control. ROS production was measured kinetically for 60 minutes at 37°C by measuring fluorescence at 522 nm using the SpectraMax i3x.

Cell supernatants were collected and sterilized by centrifugation using a 96-well filter plate containing a 0.2 µm PVDF membrane (Corning). IL-12p40/p70 production was quantified using a human IL-12p40/p70 Enzyme-Linked Immuno Sorbent Assay (ELISA) kit (Thermo Fisher Scientific) according to manufacturer’s instructions. Secretion levels of forty-one cytokine/chemokine analytes were quantified using the MilliPlex Human Cytokine/Chemokine magnetic bead premixed 41-plex kit (Millipore Billerica, MA, USA) according to manufacturer’s instructions. The following analytes were measured on a Bio-Plex 100 with Bio-Plex ManagerTM software v6.1 (Biorad): sCD40L, EGF, FGF-2, Flt3 ligand, Fractalkine (CX3CL1), G-CSF, GM-CSF, GRO (CXCL1), IFN-γ, IFN-α2, IL-1α, IL-1β, IL-1RA, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8 (CXCL8), IL-9, IL-10, IL-12p40, IL-12p70, IL-13, IL-15, IL-17a, IP-10 (CXCL10), MCP-1 (CCL2), MCP-3 (CCL7), MDC (CCL22), MIP-1α (CCL3), MIP-1β (CCL4), PDGF-AB/BB, RANTES (CCL5), TGF-α, TNF-α, TNF-β, VEGF, Eotaxin (CCL11) and PDGF-AA. Cytokines that were produced in quantities lower than 10 pg/ml in all treatment conditions were excluded from analysis.

Data were tested with a paired sample t-test when comparing two groups and RM one-way ANOVA followed by Dunnett’s multiple comparison test, unless mentioned otherwise, when comparing three or more groups. Statistical tests were considered significant when p < 0.05 at 95% confidence interval. All statistical analyses were carried out using GraphPad Prism 8 software (Graphpad Software, San Diego, CA, USA).