T lymphocytes are key drivers of the chronic inflammatory process that leads to rheumatoid arthritis (RA), a prototypic autoimmune syndrome manifesting with destruction of synovial joints, accelerated cardiovascular disease, and shortened life expectancy (Weyand and Goronzy, 2006; Naz and Symmons, 2007; Goronzy and Weyand, 2009). CD4 T cells are the major cellular component in synovitis, where they form complex tertiary lymphoid architectures and provide help for the production of signifying autoantibodies (Takemura et al., 2001; Goronzy and Weyand, 2005; Seyler et al., 2005). RA occurs in genetically predisposed hosts. The strongest inherited risk derives from genes in the MHC class II region, intimately connected to the antigen recognition process of CD4 T cells (Kochi et al., 2010). Patients with RA have a phenotype of premature immune aging, exemplified in the accumulation of CD4+CD28− T cells, contraction of T cell diversity, and shortening of T cell telomeres (Schmidt et al., 1996; Koetz et al., 2000; Weyand et al., 2009). The responsiveness of CD4 T cells to activating signals is altered in RA patients, with some tolerance defects originating in membrane-proximal signaling events (Singh et al., 2012). RA T cells express low levels of ataxia telangiectasia mutated, a protein kinase involved in sensing DNA double-strand breaks, orchestrating cell cycle checkpoints and facilitating DNA damage repair (Shao et al., 2009). In response to unattended DNA lesions and genomic stress, RA T cells chronically activate the JNK–stress kinase pathway (Shao et al., 2010). Chronic T cell activation in RA imposes cellular energy demands that deviate from conditions where most T cells are in a resting state. Exposure to antigen elicits rapid and extensive clonal expansion, and T cells respond to their fairly unique energy needs by greatly enhancing metabolic activities and up-regulating aerobic glycolysis (Heikamp and Powell, 2012; MacIver et al., 2013), as well as autophagy (Fox et al., 2005; Walsh and Bell, 2010). This shift from a primarily respiratory energetic pathway to a less conservative but more strident glycolytic metabolism with lactate production (known as the Warburg effect), coupled with increased glucose uptake, is used by proliferating cells to promote the efficient conversion of glucose into the macromolecules needed to construct new cells (Pearce, 2010; Wang et al., 2011). Triggering of the T cell antigen receptor not only leads to rapid cell replication and clonal expansion, it also induces the T cell differentiation program (Wang and Green, 2012), including the synthesis of large amounts of effector cytokines and a shift in T cell trafficking patterns. Notably, functionally distinct T cell subsets are characterized by distinct metabolic programs (Finlay and Cantrell, 2011; Michalek et al., 2011). The metabolic fate of glucose and the pathways to which it is committed is tightly regulated by a cascade of enzymes and metabolites (Mor et al., 2011). Cells catabolize glucose through glycolysis; some tissues use it to build glycogen. Under conditions of high glucose flux, cells can divert glucose to the pentose phosphate pathway (PPP). A key event in the glycolytic breakdown of glucose is the phosphorylation of fructose 6-phosphate to fructose 1,6 bisphosphate through 6-phosphofructo-1-kinase (PFK1), an irreversible reaction which commits glucose to glycolysis. As a gatekeeper in the metabolic degradation of glucose, PFK1 is controlled by downstream metabolites, most importantly by its allosteric activator fructose 2,6-bisphosphate (F2,6BP; Van Schaftingen et al., 1980). F2,6BP can enhance glycolysis even in the presence of glucose and can overcome the inhibitory effects of ATP, effectively uncoupling the glycolytic flux from cellular bioenergetics (Okar et al., 2001). Cellular levels of F2,6BP are essentially set by the bifunctional enzyme 6-phosphofructo-2-kinase/fructose-2, 6-bisphosphatase (PFKFB), which catalyzes both the production and degradation of F2,6BP through its kinase and phosphatase functions (Okar et al., 2001; Rider et al., 2004). The family of PFKFBs includes four isoenzymes, PFKFB1–4, which are regulated through diverse mechanisms, including tissue-specific expression, alternative splicing, alternative promoter usage, and enzymatic regulation through covalent and allosteric interactions. Rapidly proliferating cells, including tumors, have the inducible isoform of 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3), which allows them to promptly attend to heightened energy demands (Chesney et al., 1999). Numerous human malignancies have high expression of PFKFB3 (Atsumi et al., 2002; Bando et al., 2005; Kessler et al., 2008). PFKFB3 generates F2,6BP and, hence, critically regulates the glycolytic rate under normal and pathophysiological conditions (Okar and Lange, 1999; Seo et al., 2011; Telang et al., 2012). PFKFB3 is distinguished from the other isoenzymes by its high kinase to bisphosphatase ratio, emphasizing its critical role in giving energy-stressed cells access to the essential biofactor F2,6BP (Yalcin et al., 2009b; Colombo et al., 2010). Given the energetic and biosynthetic demands of T cells undergoing activation, they also resort to autophagy, a tightly regulated catabolic mechanism maintaining homeostasis through recycling building blocks for basal macromolecular synthesis and eliminating damaged proteins or organelles (Pua et al., 2009; Jia et al., 2011; Kovacs et al., 2012). During autophagy, portions of the cytoplasm are sequestered in double-membrane vesicles, the autophagosomes, and then degraded after fusing with lysosomes, committing macromolecules to the recycling process. A screen of yeast mutants incapable of surviving nitrogen starvation has identified a network of autophagy-related (Atg) genes all involved in the formation of autophagosomes (Tsukada and Ohsumi, 1993). A mammalian homolog of yeast Atg8 is the microtubule-associated protein 1 light chain 3 (LC3). LC3 undergoes posttranscriptional modification by a ubiquitination-like process to transfer into a soluble form, LC3-I. LC3-I is then lapidated with phosphatidylethanolamine into LC3-II, which associates with the outer and inner autophagosome membranes (Kabeya et al., 2000; Tanida et al., 2004). Accordingly, LC3 levels provide a good quantification of autophagic activity (Kabeya et al., 2000). In this study, we have investigated how the metabolic competence of T lymphocytes is reprogrammed in a disease setting that exposes these long-lived cells to a chronic inflammatory milieu and how metabolic reprogramming affects T cell survival. We found that naive CD4 T cells from RA patients are in a state of energy deprivation, mostly because they are unable to fully mobilize aerobic glycolysis. They divert glucose to the PPP, as indicated by the excess production of NADPH, resulting in the decline of cellular reactive oxygen species (ROS) levels. They also fail to tap into autophagy to access internal biosynthetic precursors. The two defects synergize in maintaining the energy deprivation of the T cells and are linked to the same molecular underpinning: the inability to up-regulate the glycolytic rate-limiting enzyme PFKFB3. This enzyme, mechanistically linked to the Warburg effect in cancer cells, is robustly induced by TCR activation, in anticipation of the enormous energy demands that result from clonal expansion and effector molecule synthesis. PFKFB3 silencing in healthy CD4 T cells mimics the conditions encountered in RA and forced overexpression of PFKFB3 in RA T cells repairs the glycolytic insufficiency and the autophagic activity. As a functional consequence of impaired glucose metabolism, reduced autophagy, and reduced ROS levels, RA T cells become much more sensitive to limitations in glucose utilization and are prone to undergo apoptosis. The metabolic reprogramming in naive RA T cells exposes the patients to chronic T cell loss, exhaustion of their T cell reserve, and the development of lymphopenia, now recognized as a risk factor for autoimmunity. RESULTS Reduced glucose consumption in RA CD4 T cells TCR triggering initiates an activation cascade that leads to robust T cell proliferation, clonal expansion, rapid mobility, and effector molecule production, all associated with a sudden increase in metabolic demand. To meet the biosynthetic needs, T cells undergoing activation switch their glucose metabolism from oxidative phosphorylation to aerobic glycolysis (Finlay, 2012). To compare glycolytic flux in healthy CD4 T cells and in autoimmune-prone T cells from RA patients, we quantified glucose utilization (Fig. 1 A), lactate production (Fig. 1 B), and generation of intracellular ATP levels (Fig. 1 C) in naive CD4 T cells 72 h after stimulation. All three parameters were reduced in RA T cells by 20–30%. The lowered lactate and ATP production in RA T cells was maintained over a wide range of TCR signal strengths which was modified by titrating the ratio of anti-CD3–coated beads per T cell from 0.25 to 2. The failure of RA T cells to consume glucose at a level of normal T cells was associated with a significant increase in apoptosis (Fig. 1 D). The proportion of 7AAD+ and Annexin V+ cells 3 d after TCR cross-linking was 50% higher in RA T cells. As in previous studies (Fujii et al., 2009; Singh et al., 2009), the frequencies of T cells expressing activation markers was even higher in RA T cells than in control T cells, excluding insufficient stimulation of the patient-derived cells as a cause of reduced glucose consumption. Figure 1. Glucose hypometabolism in RA T cells. Naive CD4 (CD4+CD45RO−) T cells were isolated from RA patients (RA) and age-matched controls (Con) and stimulated with anti-CD3/CD28 microbeads. On day 3, cells were harvested, washed, and recultured for a period of 4 h. Culture medium was collected at 0 and 4 h to measure glucose (A, 14 RA patients and 11 healthy controls) and lactate concentration (B, 21 RA patients and 18 healthy controls). Intracellular ATP was determined in cell pellets (C, 17 RA patients and 18 healthy controls). Frequencies of apoptotic (7AAD+ and Annexin V+) T cells were assessed by flow cytometry (D). Results are shown as box plots. Median, 25th, and 75th percentiles (box), and 10th and 90th percentiles (whiskers) are displayed. T cell apoptosis and caspase activity (E) in relation to glucose availability were evaluated by stimulating T cells for 2 d, washing them and reculturing them in the absence or presence of glucose (10 or 1 mM), n = 3. To block glycolytic activity, 2-deoxy-d-glucose (2-DG) was added to T cell cultures on day 0. T cell expansion (F) was measured after 72 h, n = 4. T cell responsiveness was compared in RA and control CD4+CD45RO− T cells 48 and 72 h after stimulation. Expression of the lineage commitment transcription factors T-bet, Gata-3, FoxP3, and RORγt was measured by RT-PCR after 48 h (G, 10 RA patients and 11 healthy controls). T cell proliferation was quantified by CFSE dilution and representative histograms are shown (H). Proliferation indices from four to eight RA patients and seven age-matched controls are presented (I). Frequencies of IL-2–producing T cells were quantified by cytometric analysis of intracellular staining (J). Results from 4 RA patients and 4 age-matched controls are shown as mean ± SEM. *, P 2 wk (Fig. 2 B). In RA T cells, poststimulation PFKFB3 peaked after 72 h but remained below levels in control T cells over the entire activation cycle. Bypassing the TCR and stimulating T cells with 20 ng/ml PMA and 200 µg/ml ionomycin provided a potent PFKFB3-inducing signal, albeit not quite as efficient as TCR ligation (Fig. 2 D). Reduced PFKFB3 induction in RA T cells was maintained after TCR-independent activation. Figure 2. PFKFB3 induction is suppressed in RA T cells. Naive CD4 (CD4+CD45RO−) T cells were isolated from RA patients and age-matched controls and stimulated with anti-CD3/CD28 microbeads for 3 d. Glycolysis-related gene transcripts were quantified by qPCR (A, n = 3). Kinetics of the expression of PFKFB3 in T cells following TCR ligation were monitored by RT-PCR over 12 d (B, 6 RA patients and 6 healthy controls) or by Western blotting over 4 d (C). Naive CD4 T cells were stimulated with either anti-CD3/CD28 microbeads or 20 ng/ml PMA and 200 µg/ml ionomycin and PFKFB3 transcript levels were monitored over 72 h in six samples. PMA/ionomycin-induced PFKFB3 transcripts from 5 RA patients and 6 controls are shown (D). Protein levels of PFKFB3 were quantified by Western blotting. Representative data for 2 patients and 2 controls are shown. Quantification of band densities from five independent experiments in 10 patients and 10 control samples are given as mean ± SEM (E). Expression of PFKFB3 in CD4+CD45RO+ memory T cells was quantified in 10 controls and 5 RA patients by RT-PCR (F). Activation-induced up-regulation of PFKFB3 was compared in control T cells, RA T cells, and SLE T cells on day 3 after TCR ligation. PFKFB3 mRNA levels were determined by RT-PCR in n = 16 RA patients, n = 33 controls, and n = 11 SLE patients. Results are given as mean ± SEM (G). PFKFB3 transcripts quantified by qPCR on day 3 after T cell stimulation were correlated with RA disease activity (DAS28; r2 = 0.020, P = 0.306; H). Expression of AMPK family members and mTOR was determined by qPCR and Western blotting, respectively. Data from 6 RA patients and 6 age-matched controls are presented as mean ± SEM and representative immunoblots are shown (I). Transcripts of PFKFB1, 2, and 4 were quantified by qPCR over 6 d (J). Activation-induced up-regulation of PFKFB1, 2, and 4 were compared in control and RA T cells (n = 8 each) on day 3 after TCR ligation. Results are given as mean ± SEM (K). *, P 50 cells. Representative pictures are shown and data from three to four independent experiments are presented as mean ± SEM. Bars, 50 µM. CD4+CD45RO− T cells from RA patients were transfected with PFKFB3 or control plasmids and tested for apoptotic susceptibility by culturing them in the absence and presence of 2.5 µM 3-MA. Frequencies of 7AAD+ and Annexin V+ cells were determined cytometrically (I). Representative dot plots are presented. Frequencies of apoptotic cells from three independent experiments are given as mean ± SEM. *, P 45 min), swollen joints (>3), tender joints (>6), and sed rate (>28 mm). Cells and culture. CD4 naive T cell subsets were purified from PBMC using CD45RO and CD4 microbeads (Miltenyi Biotec). Subset purity was monitored by flow cytometry and reached >95%. Naive CD4 (CD4+CD45RO−) T cells (1.0 × 105/well) were stimulated with CD3/CD28-coated beads (Life Technologies) at a ratio of 1:1. Cells were enumerated by flow cytometry or trypan blue exclusion. For cell proliferation assays, naive CD4 T cells were labeled with CFSE and stimulated with anti-CD3/CD28 beads (1:1 ratio). T cell proliferation was determined by flow cytometry after 48 and 72 h. To detect intracellular IL-2, cells were harvested on day 3 after TCR ligation, and incubated with PMA and ionomycin in the presence of brefeldin A for 5 h before intracellular staining with anti–IL-2 antibodies as previously described (Li et al., 2012). For experiments examining the impact of PFKFB3 on T cell growth and survival, cells were harvested 48 h after stimulation and treated with N-BrEt (provided by J.D. Foster, University of North Dakota, Grand Forks, ND) in the absence/presence of 10 mM glucose for an additional 48 h. Frequencies of apoptotic T cells were measured by cytometric analysis of 7AAD and PE-Annexin V (BD). Apoptotic cells with high caspase activity were detected with MitoCasp kits (Cell Technology, Inc.). Increased cell death rates were calculated as follows: delta percentage = % dead cell at 20 µg/ml N-BrEt − % dead cell at 0 µg/ml N-BrEt. In the autophagy inhibition experiments, CD4 T cells were activated for 48 h and then treated with indicated doses of the autophagy inhibitor 3-MA (Sigma-Aldrich) for an additional 48 h. In the PFKFB3 rescue experiments, 3-MA was added to the transfected cells after 24 h and cells were cultured for 1 d more. ROS dependence of apoptosis induction was tested by treating CD4 T cells with the ROS scavenger Tempol (Sigma-Aldrich) for 24 h. Plasmids. pIRES-PFKFB3-GFP (PFKFB3) plasmids containing the complete PFKFB3 coding sequence (GenBank accession no. NM_004566) were generated by PCR using CD4 T cells cDNA for template amplification. The following primers were used: 5′-CGAAGATGCCGTTGGAACTGA-3′ (forward) and 5′-TGGAATGGAACCGACACGTCT-3′ (reverse), with EcoRI and SalI restriction sites at the NH2 and COOH terminals, respectively. An EcoRI–SalI PFKFB3 expression cassette was subcloned into pIRES-GFP plasmid (Takara Bio Inc.). The pIRES-H2Kk plasmid (courtesy of J. Tahvanainen, Turku University, Turku, Finland) has been previously described (Tahvanainen et al., 2006). For silencing of the PFKFB3 gene, pSUPER-shPFKFB3-GFP (shPFK) plasmids or pSuper-shPFKFB3-H2Kk plasmids were constructed that contained the 19-nt sequence CTGAAACTGACGCCTGTCG, derived from the mRNA transcript of PFKFB3. Plasmid transfections. Naive CD4 T cells were stimulated with anti-CD3/CD28 beads for 48 h and then transfected with empty-pIRES-GFP plasmids or pIRES-PFKFB3-GFP (PFKFB3) plasmids. Transfection efficiencies were monitored by measuring the frequency of GFP-positive cells using flow cytometer. To knock down PFKFB3 expression, 3 µg pSuper-PFKFB3-GFP/neo plasmids (shPFK) or control vector were transfected into activated CD4 T cells using the Amaxa Nucleofector system and the Human T cell Nucleofector kit (Lonza). Cells were cultured for 24 h before further experiments. To assess autophagic activity, cells were cotransfected with GFP-LC3 plasmids and pIRES-PFKFB3-H2Kk or pSuper-shPFKFB3-H2Kk plasmids on day 2 after TCR ligation. At day 4, H2Kk-positive cells were purified with MACSelect Kk MicroBeads (Miltenyi Biotec) and analyzed by fluorescent microscopy (BX41TF; Olympus). Glucose consumption, lactate production, and intracellular ATP concentration. Purified CD4+CD45RO− T cells (2.5 × 105/well) were stimulated for 48 h, washed twice, and then cultured with fresh complete medium containing glucose (1,000 mg/liter). After 4 h of incubation, T cells were counted and intracellular ATP concentrations were measured using ATP assay kits (Abcam). To quantify lactate concentrations, cell-free supernatants were analyzed in triplicate using a lactate assay kit (Abcam). In brief, 20 µl of sample or standard were incubated with 50 µl of lactate reagent solution for 30 min at 37°C, and then the reaction was terminated by adding 50 µl 0.5 M acetic acid. Absorbance was measured at 490 nm. For determination of glucose concentrations, cell-free supernatants were analyzed using a glucose assay kit (Sigma-Aldrich) according to the manufacturer’s instructions. In brief, samples were prepared at a total volume of 50 µl/well with glucose assay buffer and mixed with 50 µl reaction mixture containing 46 µl glucose assay buffer, 2 µl glucose probe, and 2 µl glucose enzyme mix. After 30 min of incubation, absorbance was measured at 570 nm. Glucose consumption was expressed as the decrease in glucose concentration in culture medium over a 4-h culture period. NADPH level and ROS production assay. CD4+CD45RO− T cells from both RA patients and age-matched controls were stimulated for 72 h and then washed with cold PBS. NADPH levels were assayed by using NADPH assay kits (Abnova) according to the manufacturer’s instruction. The assay system is based on a glucose dehydrogenase cycling reaction in which the formed NADPH reduces a formazan reagent, the optical density of which is read at 570 nm. For ROS production assays, freshly isolated CD4+CD45RO− T cells or T cells stimulated with anti-CD3/CD28 microbeads for 72 h were incubated with 10 µM of DHE or DCF (Molecular Probes) for 30 min at 37°C. Cells were washed and analyzed by flow cytometry. In each experiment, calibration beads (Molecular Probes) were included to standardize the fluorescence intensity readings. RT-PCR. Total RNA was extracted with RNeasy kits (QIAGEN) and cDNA was synthesized with AMV-reverse transcriptase (Roche) and random hexamers (Roche). Reaction solutions for the qRT-PCR consisted of 2.0 µl of diluted RT-PCR product, 0.2 µM of each primer, and SYBR green PCR master mix (Thermo Fisher Scientific). The primers used were as follows: PFKFB3, 5′-CTCGCATCAACAGCTTTGAGG-3′ (forward) and 5′-TCAGTGTTTCCTGGAGGAGTC-3′ (reverse); and 18S ribosomal RNA, 5′-GCCTCACTAAACCATCCAA-3′ (forward) and 5′-TCAGTGTTTCCTGGAGGAGTC-3′ (reverse). Copy numbers were calculated by comparing each sample with a standard curve generated by amplifying serially diluted plasmids containing relevant sequences. cDNA copies were adjusted for 1 × 108 ribosomal RNA copies. Other primers used for glycolysis-related genes, AMPKs, mTOR, and autophagic genes are listed in Table 3. All PCR results were normalized to 18S rRNA. Table 3. List of primers Gene name GenBank Forward primer (5′-3′) Reverse primer (5′-3′) AldoA NM_000034 AGATGAGTCCACTGGGAGCAT CACGCCCTTGTCTACCTTGAT AldoC NM_005165 GGATGAGTCTGTAGGCAGCAT GAGTGGTGGTTTCTCCATCAG AMPKα1 NM_006251 TGAAAATGTCCTGCTTGATG TACTTCTGGTGCAGCATAGT AMPKα2 NM_006252 GTTGCGGATCTCCAAATTAT GACGTTAGCATCATAGGAAG AMPKβ1 NM_006253 GCTTGGCACAGTTAACAACATC TTCGGGTTTGCAGACGTAG AMPKβ2 NM_005399 TACCAGTCAGCTTGGCACA CCTCAGATCGAAACGCAT AMPKγ1 NM_002733 GGACTCCTTTAAACCGCTTG CTTGGACATGAACTCTGGCT AMPKγ2 NM_024429 ATCCAGACACTCCCATCATC GCACTTCACAACACCTTCAA AMPKγ3 NM_017431 CCACATCCTCACACACAAAC GAATCACATCAAAGCGGG ATG5 NM_004849 GACAAAGATGTGCTTCGAGATGTG GTAGCTCAGATGTTCACTCAG ATG7 NM_006395 ACATCATTGCAGAAGTAGCAGCCA ATGCCTGGGCATCCAGTGAACTTC Beclin-1 NM_003766 CAGACAGATGTGGATCACCC ATCAGCCTCTCCTCCTCTAGTG Bim NM_138621 ACGCTTACTATGCAAGGAGGG GGTCTTCGGCTGCTTGGTAAT ENO1 NM_001428 GCCCTGGTTAGCAAGAAACTG TTCTCAACGGCACCAGCTTTG ENO2 NM_001975 AACAGTGAAGCCTTGGAGCTG TCCTCAATGGAGACCACAGGA ENO3 NM_053013 CTATCCTGTGGTCTCCATCGA CTCCTCGATCCTCATGAGTTG GAPDH NM_002046 GGAGTCAACGGATTTGGTCGT TCTCGCTCCTGGAAGATGGT Glut1 NM_006516 GCAGTTTGGCTACAACACTGG TTTCGAGAAGCCCATGAGCAC Glut3 NM_006931 TGGCTACAACACTGGGGTCAT TTGAATTGCGCCTGCCAAAGC GPI NM_000175 ATCAACTACACCGAGGGTCGA CCAATGTTGATGACGTCCGTG HK1 NM_000188 CGAGAGTGACCGATTAGCACT AGACAGGAGGAAGGACACGTT HK2 NM_000189 ACGGAGCTCAACCATGACCAA AAGATCCAGAGCCAGGAACTC LC3B NM_022818 GTCCGACTTATTCGAGAGCAG CTGAGATTGGTGTGGAGACG LDHA NM_005566 GATTCCAGTGTGCCTGTATGG CTACAGAGAGTCCAATAGCCC LDHB NM_002300 GCGTGTGCTATCAGCATTCTG TTCTCTGCACCAGATTGAGCC MTOR NM_004958 AGCCGGTGTTGCAGAGACTT TCAGACCTCACAGCCACAGAA NOXA NM_021127 CCTGGGAAGAAGGCGCG TCAGGTTCCTGTGCAGAAG PDK1 NM_002610 GCTAGGCGTCTGTGTGATTTG AACACCTCTGTTGGCATGGTG PDK2 NM_002611 CAACCAGCACACCCTCATCTT GCCCTCATGGCATTCTTGAAG PDK3 NM_005391 TCGCCGCTCTCC ATCAAACAA CTGAACCAATCCCACTGAAGG PFK1 NM_002626 CTGTACTCATCAGAGGGCAAG TGCCAGCATCTTCAGCATGAG PFKFB1 NM_002625 CTCCATCTACCT TTGCCGACA GCCCTGGGACTGAATGAAGTT PFKFB2 NM_006212 CACCAATACAACCCGGGAGA GCAGCAATGACATCAGGATCA PFKFB4 NM_004567 CCAACTGCCCAACTCTCATTG GCGATACTGGCCAACATTGAA PFKM NM_000289 GAAGAGCACCATGCAGCCAAA TCGTTCCCGAAAGTCCTTGCA PGK1 NM_000291 GGGTCGTTATGAGAGTCGACT AGGTGGCTCATAAGGACTACC PGK2 NM_138733 AAGTCAGCCATGTCAGCACTG GCCTGCTGCTTGTCCATTACA PGM1 NM_002633 TCCAGAGTATCATCTCCACCG ATGATGCAGGATACAGCAGGG PGM2 NM_018290 AGCAGAAGGTTTGCCCGACTT TTCACGCTCCTGTGGAAACAG PGM3 NM_015599 CTCCTGGTGGAGATTGGAGAA CTAAACAGTGCAGTGCCATGC PKM1(exon9) NM_182470 GGGGTTCGGAGGTTTGATGAA AGGTCTGTGGAGTGACTTGAG PKM2 NM_182471 TCTGTACCATTGGCCCAGCTT TGGCTGTGCGCACATTCTTGA PKM2(exon10) NM_002654 GGGGTTCGGAGGTTTGATGAA TTGCAAGTGGTAGATGGCAGC PUMA NM_014417 GGACGACCTCAACGCACAGTA GGCAGGAGTCCCATGATGAGA TIGAR NM_020375 GGACAAAGCAGACCATGCATG ACCCCGTATTTCCTTTCCCGA TPI NM_000365 GTCAGATGAGCTGATTGGGCA GGTGTTGCAGTCTTGCCAGTA Western blotting. Cellular proteins were extracted using an extraction kit from Active Motif, and expression levels were examined following a standard Western blotting protocol. In brief, 50 µg protein of each sample was electrophoresed in 10% or 4–15% Tris-Glycine gel (Bio-Rad Laboratories) and transferred to PVDF blot membrane. The membrane was blocked with 5% nonfat milk (Bio-Rad Laboratories) solution and incubated with primary antibody at 1:1,000 (anti-PFKFB3, Abcam; anti-LC3B, anti-AMPK, and anti-mTOR, Cell Signaling Technology) overnight in the cold room followed by secondary horseradish peroxidase–conjugated anti-Ig antibody (Santa Cruz Biotechnology, Inc.) for 2 h. The enhanced chemiluminescence detection system (GE Healthcare) was used to detect bands with peroxidase activity. β-Actin (1:5,000; Santa Cruz Biotechnology, Inc.) served as internal control. Statistical analysis. All data are presented as mean ± SEM. Data were analyzed using SPSS 10.0 software. Statistical significance was assessed by ANOVA and unpaired Student’s t test as appropriate. A p-value of <0.05 was considered significant.
