Adaptation to the environment and extraction of energy are essential for survival. Some species have found niches and specialized in using a particular source of energy, whereas others—including humans and several other mammals—have developed a high degree of flexibility1. A lot is known about the general metabolic fates of different substrates but we still lack a detailed mechanistic understanding of how cells adapt in their use of basic nutrients2. Here we show that the closely related fasting/starvation-induced forkhead transcription factors FOXK1 and FOXK2 induce aerobic glycolysis by upregulating the enzymatic machinery required for this (for example, hexokinase-2, phosphofructokinase, pyruvate kinase, and lactate dehydrogenase), while at the same time suppressing further oxidation of pyruvate in the mitochondria by increasing the activity of pyruvate dehydrogenase kinases 1 and 4. Together with suppression of the catalytic subunit of pyruvate dehydrogenase phosphatase 1 this leads to increased phosphorylation of the E1α regulatory subunit of the pyruvate dehydrogenase complex, which in turn inhibits further oxidation of pyruvate in the mitochondria—instead, pyruvate is reduced to lactate. Suppression of FOXK1 and FOXK2 induce the opposite phenotype. Both in vitro and in vivo experiments, including studies of primary human cells, show how FOXK1 and/or FOXK2 are likely to act as important regulators that reprogram cellular metabolism to induce aerobic glycolysis.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
RNA-seq datasets are available at the Gene Expression Omnibus (GEO) website under accession number GSE114258. Proteomics data for 3T3-L1 cells overexpressing or knocked down for FOXK1 and FOXK2 can be found in Supplementary Tables 1–4. Source Data for animal experiments can be found in the online version of the paper. Uncropped images of immunoblots can be found in Supplementary Fig. 1. All other data are available from the corresponding authors upon reasonable request.
Campbell, B. Human Evolution (Aldine Transactions, 2009).
Goodpaster, B. H. & Sparks, L. M. Metabolic flexibility in health and disease. Cell Metab. 25, 1027–1036 (2017).
Palm, W. & Thompson, C. B. Nutrient acquisition strategies of mammalian cells. Nature 546, 234–242 (2017).
Thacker, S. V., Nickel, M. & DiGirolamo, M. Effects of food restriction on lactate production from glucose by rat adipocytes. Am. J. Physiol. 253, E336–E342 (1987).
DiGirolamo, M., Newby, F. D. & Lovejoy, J. Lactate production in adipose tissue: a regulated function with extra-adipose implications. FASEB J. 6, 2405–2412 (1992).
Wu, P. et al. Starvation and diabetes increase the amount of pyruvate dehydrogenase kinase isoenzyme 4 in rat heart. Biochem. J. 329, 197–201 (1998).
Jeoung, N. H. et al. Role of pyruvate dehydrogenase kinase isoenzyme 4 (PDHK4) in glucose homoeostasis during starvation. Biochem. J. 397, 417–425 (2006).
Hui, S. et al. Glucose feeds the TCA cycle via circulating lactate. Nature 551, 115–118 (2017).
Holness, M. J. & Sugden, M. C. Regulation of pyruvate dehydrogenase complex activity by reversible phosphorylation. Biochem. Soc. Trans. 31, 1143–1151 (2003).
Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).
Han, H., Pappin, D. J., Ross, P. L. & McLuckey, S. A. Electron transfer dissociation of iTRAQ labeled peptide ions. J. Proteome Res. 7, 3643–3648 (2008).
Kamburov, A., Cavill, R., Ebbels, T. M., Herwig, R. & Keun, H. C. Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA. Bioinformatics 27, 2917–2918 (2011).
Kim, J. W. & Dang, C. V. Cancer’s molecular sweet tooth and the Warburg effect. Cancer Res. 66, 8927–8930 (2006).
Cederbaum, S. D. et al. Sensitivity to carbohydrate in a patient with familial intermittent lactic acidosis and pyruvate dehydrogenase deficiency. Pediatr. Res. 10, 713–720 (1976).
Bonnefont, J. P. et al. α-ketoglutarate dehydrogenase deficiency presenting as congenital lactic acidosis. J. Pediatr. 121, 255–258 (1992).
Jackson, C. B. et al. Mutations in SDHD lead to autosomal recessive encephalomyopathy and isolated mitochondrial complex II deficiency. J. Med. Genet. 51, 170–175 (2014).
Raimundo, N. et al. Differential metabolic consequences of fumarate hydratase and respiratory chain defects. Biochim. Biophys. Acta 1782, 287–294 (2008).
Mookerjee, S. A., Nicholls, D. G. & Brand, M. D. Determining maximum glycolytic capacity using extracellular flux measurements. PLoS ONE 11, e0152016 (2016).
Zhang, S., Hulver, M. W., McMillan, R. P., Cline, M. A. & Gilbert, E. R. The pivotal role of pyruvate dehydrogenase kinases in metabolic flexibility. Nutr. Metab. (Lond.) 11, 10 (2014).
Sugden, M. C. & Holness, M. J. Mechanisms underlying regulation of the expression and activities of the mammalian pyruvate dehydrogenase kinases. Arch. Physiol. Biochem. 112, 139–149 (2006).
Roche, T. E. et al. Distinct regulatory properties of pyruvate dehydrogenase kinase and phosphatase isoforms. Prog. Nucleic Acid Res. Mol. Biol. 70, 33–75 (2001).
Subhash, S. & Kanduri, C. GeneSCF: a real-time based functional enrichment tool with support for multiple organisms. BMC Bioinformatics 17, 365 (2016).
Cao, Y., Rathmell, J. C. & Macintyre, A. N. Metabolic reprogramming towards aerobic glycolysis correlates with greater proliferative ability and resistance to metabolic inhibition in CD8 versus CD4 T cells. PLoS ONE 9, e104104 (2014).
He, L. et al. mTORC1 promotes metabolic reprogramming by the suppression of GSK3-dependent Foxk1 phosphorylation. Mol. Cell 70, 949–960e944 (2018).
Gladden, L. B. Lactate metabolism: a new paradigm for the third millennium. J. Physiol. (Lond.) 558, 5–30 (2004).
Brooks, G. A. Cell-cell and intracellular lactate shuttles. J. Physiol. (Lond.) 587, 5591–5600 (2009).
Pellerin, L. et al. Evidence supporting the existence of an activity-dependent astrocyte-neuron lactate shuttle. Dev. Neurosci. 20, 291–299 (1998).
Smith, D. et al. Lactate: a preferred fuel for human brain metabolism in vivo. J. Cereb. Blood Flow Metab. 23, 658–664 (2003).
Cederberg, A., Grände, M., Rhedin, M., Peng, X. R. & Enerbäck, S. In vitro differentiated adipocytes from a Foxc2 reporter knock-in mouse as screening tool. Transgenic Res. 18, 889–897 (2009).
Silver, D. P. & Livingston, D. M. Self-excising retroviral vectors encoding the Cre recombinase overcome Cre-mediated cellular toxicity. Mol. Cell 8, 233–243 (2001).
Ohsumi, J. et al. Troglitazone prevents the inhibitory effects of inflammatory cytokines on insulin-induced adipocyte differentiation in 3T3-L1 cells. Endocrinology 135, 2279–2282 (1994).
Lee, S. et al. Berberine inhibits rat vascular smooth muscle cell proliferation and migration in vitro and improves neointima formation after balloon injury in vivo: Berberine improves neointima formation in a rat model. Atherosclerosis 186, 29–37 (2006).
Bartesaghi, S. et al. Thermogenic activity of UCP1 in human white fat-derived beige adipocytes. Mol. Endocrinol. 29, 130–139 (2015).
Pike Winer, L. S. & Wu, M. Rapid analysis of glycolytic and oxidative substrate flux of cancer cells in a microplate. PLoS ONE 9, e109916 (2014).
Shi, J. & Kandror, K. V. Study of glucose uptake in adipose cells. Methods Mol. Biol. 456, 307–315 (2008).
Rune, A. et al. Evidence against a sexual dimorphism in glucose and fatty acid metabolism in skeletal muscle cultures from age-matched men and post-menopausal women. Acta Physiol. 197, 207–215 (2009).
Al-Khalili, L. et al. Insulin action in cultured human skeletal muscle cells during differentiation: assessment of cell surface GLUT4 and GLUT1 content. Cell. Mol. Life Sci. 60, 991–998 (2003).
Jensen, T. C., Crosson, S. M., Kartha, P. M. & Brady, M. J. Specific desensitization of glycogen synthase activation by insulin in 3T3-L1 adipocytes. Connection between enzymatic activation and subcellular localization. J. Biol. Chem. 275, 40148–40154 (2000).
Zeng, X. et al. Lysine-specific demethylase 1 promotes brown adipose tissue thermogenesis via repressing glucocorticoid activation. Genes Dev. 30, 1822–1836 (2016).
Kazak, L. et al. A creatine-driven substrate cycle enhances energy expenditure and thermogenesis in beige fat. Cell 163, 643–655 (2015).
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).
Harrow, J. et al. GENCODE: producing a reference annotation for ENCODE. Genome Biol. 7 (Suppl. 1), S4 (2006).
Lassmann, T., Hayashizaki, Y. & Daub, C. O. SAMStat: monitoring biases in next generation sequencing data. Bioinformatics 27, 130–131 (2011).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Pandey, R. R. et al. Kcnq1ot1 antisense noncoding RNA mediates lineage-specific transcriptional silencing through chromatin-level regulation. Mol. Cell 32, 232–246 (2008).
Kwon, A. T., Arenillas, D. J., Worsley Hunt, R. & Wasserman, W. W. oPOSSUM-3: advanced analysis of regulatory motif over-representation across genes or ChIP-seq datasets. G3 (Bethesda) 2, 987–1002 (2012).
Lakso, M. et al. Efficient in vivo manipulation of mouse genomic sequences at the zygote stage. Proc. Natl Acad. Sci. USA 93, 5860–5865 (1996).
Liu, P., Jenkins, N. A. & Copeland, N. G. A highly efficient recombineering-based method for generating conditional knockout mutations. Genome Res. 13, 476–484 (2003).
Ishikawa, M., Hosoi, R., Kobayashi, K., Nishimura, T. & Inoue, O. Rolipram depresses [(3)H]2-deoxyglucose uptake in mouse brain and heart in vivo. Eur. J. Nucl. Med. Mol. Imaging 29, 1212–1215 (2002).
Kalland, M. E., Oberprieler, N. G., Vang, T., Taskén, K. & Torgersen, K. M. T cell-signaling network analysis reveals distinct differences between CD28 and CD2 costimulation responses in various subsets and in the MAPK pathway between resting and activated regulatory T cells. J. Immunol. 187, 5233–5245 (2011).
Tadesse, F. G. et al. Unpredicted phenotypes of two mutants of the TcR DMF5. J. Immunol. Methods 425, 37–44 (2015).
Ogilvie, R. W. & Feeback, D. L. A metachromatic dye-ATPase method for the simultaneous identification of skeletal muscle fiber types I, IIA, IIB and IIC. Stain Technol. 65, 231–241 (1990).
Hauner, H., Skurk, T. & Wabitsch, M. Cultures of human adipose precursor cells. Methods Mol. Biol. 155, 239–247 (2001).
We thank G. Petersson for technical support and T. Gnad for help with glucose uptake measurements in human adipocytes; R. Palmer, B. Hallberg and J. Tetteh Siaw for support and advice regarding cancer cell lines; and J.-O. Jansson for supplying adipose tissue from Ob/Ob mice. S.E. is supported by the Swedish Research Council (2014–2516), The Knut and Alice Wallenberg Foundation, Sahlgrenska’s University Hospital (LUA-ALF), and Novo Nordisk Foundation. K.T. is supported by the Norwegian Cancer Society.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, Glucose uptake in 3T3-L1 adipocytes (on day 11) in response to doxycycline-induced expression of FOXK1 or FOXK2, with or without 100 nM insulin. Cells were differentiated and expression of FOXK1 or FOXK2 was induced by doxycycline at indicated days (d) during differentiation; n = 4. b, Glycerol release from 3T3-L1 adipocytes with knockdown of Foxk1 or Foxk2 compared with empty vector or shGFP; n = 4. c, Incorporation of glucose into glycogen in L6 myotubes in response to doxycycline-induced expression of FOXK1 or FOXK2; n = 3. d, Foxk2 mRNA levels in MEF adipocytes derived from wild type mice or mice lacking one Foxk2 allele (Foxk2fl/+-cre) or both Foxk2 alleles (Foxk2fl/fl-cre). e, Glucose uptake in MEF adipocytes from wild-type, Foxk2fl/+-cre or Foxk2fl/fl-cre mice; n = 4. f, Western blot of GLUT4 and GLUT1 in 3T3‐L1 preadipocytes (d = 0) and adipocytes (d = 7 and d = 11) in response to overexpression of FOXK1 or FOXK2. FOXK1 and FOXK2 were identified by their Flag tags. g–j, Glucose uptake induced by FOXK1 or FOXK2. Comparison of glucose uptake measurements using [3H]-2-deoxyglucose or [14C]-glucose; n = 4. Experiments repeated three times. k, Glucose depletion from tissue culture medium by 3T3-L1 adipocytes after 48 h incubation. n = 3 biological independent samples; experiments repeated three times. Data shown are averages of three independent replicas. l, m, Expression of FOXK1 and FOXK2 in WAT from wild-type or Ob/Ob mice. Retroperitoneal WAT: wild-type n = 9, Ob/Ob n = 7; gonadal WAT: wild-type n = 10, Ob/Ob n = 7) n, Leptin levels in 3T3-L1 adipocytes overexpressing or knocked down for FOXK1 or FOXK2; n = 3. o, Rate of glucose uptake and lactate secretion into tissue culture medium of 3T3-L1 adipocytes during 48 h with induced expression of FOXK1 or FOXK2. p, Lactate production rate of 3T3-L1 adipocytes during 48 h with overexpression of FOXK1 or FOXK2. q, Glucose depletion rate of 3T3-L1 adipocytes during 48 h with overexpression of FOXK1 or FOXK2. o–q, n = 3 biological independent samples, experiments were repeated three times. Data shown are an average of three independent replicas. This set of experiments (n = 3 and repeated 3 times) was performed twice with similar results. Throughout figure, experiments replicated at least three times with the exception of c (twice) and l–n (once). Representative experiments are shown if not indicated otherwise. Unpaired two-sided Student’s t-test; ***P < 0.001, ** P < 0.01, *P < 0.05. For gel source data, see Supplementary Fig. 1. Source Data
Extended Data Fig. 2 Characterization of FOXK1 and FOXK2 and metabolic changes induced by FOXK1 and FOXK2 in adipocytes and muscle cells.
a, FOXK1 and FOXK2 showing amino acid sequence similarities, forkhead associated domain (FHA), winged helix domain (WHD) and nuclear localization signal (NL). Cross-species amino acid comparison shows ~90% similarity between human and mouse for FOXK1, and ~95% for FOXK2. b, c, Relative mRNA levels for Foxk1 (b) and Foxk2 (c) in the SVF and in mature adipocytes (Ad). Experiment performed once, n = 3. d, Glycerol release in 3T3‐L1 adipocytes with increased expression of FOXK1 or FOXK2 with or without 20 μM isoprenaline, n = 6. e, Glucose uptake in differentiating C2C12 myocytes (on day 8) in response to doxycycline-induced expression of FOXK1 or FOXK2 initiated at indicated days (d) during differentiation, n = 6. A representative experiment out of three (d) or two (e) are shown. Data shown as mean ± s.d., n shows biological independent experiments. Unpaired two-sided Student’s t-test; ***P < 0.001, **P < 0.01, *P < 0.05. Source Data
Extended Data Fig. 3 Knockdown efficiency of shFoxk1-1, shFoxk1-2, shFoxk2-1 and shFoxk2-2, and complementation test of FOXK1 and FOXK2.
3T3-L1 adipocytes were transduced to express shRNA targeting Foxk1 and Foxk2. a, Proteins and RNA were prepared and protein levels were determined by western blot using specific antibodies against FOXK1, FOXK2 and β-actin. b, mRNA levels were determined using quantitative real-time PCR, n = 3. Experiment performed once. 3T3-L1 adipocytes were transduced to overexpress FOXK1, FOXK2, shFoxk1 and shFoxk2 in the combinations listed. c, 2-DG uptake was determined under basal and insulin-stimulated conditions (100 nM), n = 4. d, Lactate secreted into medium, n = 4. c, d, Representative experiments out of two are shown. e, Knockdown efficiency and selectivity of shPdk1 and shPdk4 tested in 3T3-L1 adipocytes using quantitative real-time PCR, n = 3. Experiment performed once. f, g, Lactate production in 3T3-L1 adipocytes transduced with shRNA targeting Pdk1 or Pdk4 either alone or in combination with FOXK1 or FOXK2 expression vectors. n = 3. Representative experiments out of two are shown. Data shown as mean ± s.d., n shows biological independent experiments. Unpaired two-sided Student’s t-test; ***P < 0.001, **P < 0.01, *P < 0.05. For gel source data, see Supplementary Fig. 1.
Extended Data Fig. 4 Bioenergetic changes induced by FOXK2 in 3T3-L1 adipocytes, FOXK2-induced 2-DG uptake under several inhibitor treatments, and role of FOXK1 and FOXK2 in T cell activation.
a, Glycolytic analysis of 3T3-L1 upon FOXK2 overexpression (FOXK2) or suppression (shFoxk2) compared to empty vector and shCtrl controls, respectively. Data are averages of four independent experiments, n = 12. b, Left, kinetic ECAR response of 3T3-L1 adipocytes overexpressing FOXK2 to glucose (10 mM), oligomycin (1 µM) and 2-DG (100 mM). A representative experiment out of three is shown, n = 3. Right, dissection of glycolysis and glycolytic capacity in 3T3-L1 adipocytes overexpressing FOXK2. Data are averages of four independent experiments, n = 4. The assay medium was substrate-free base medium supplemented with 2 mM glutamine. c, 2-DG uptake by 3T3-L1 adipocytes under basal conditions with overexpression of FOXK2 or an empty vector control after treatment with salirasib 25 μM for 2 h and rapamycin 100 nM for 16 h or 2 h. Representative experiment out of two is shown, n = 3. d–g, Human peripheral blood CD3+ T cells from 3 donors (n = 3) were transduced with Flag-tagged FOXK1 or FOXK2 vectors. Transduced cells were incubated in X-vivo 15 medium supplemented with 5% human serum and IL-2 for 3 days before Ki-67 staining and analysis by flow cytometry. Cells were gated on CD4 or CD8 and Flag-tag (FOXK1 or FOXK2 overexpression) before expression of the proliferation marker Ki-67 was analysed. Experiments were performed three times. Data shown as mean ± s.d. Unpaired two-sided Student’s t-test; ***P < 0.001, **P < 0.01, *P < 0.05.
a–k, Western blot experiments for proteins involved in the glycolytic pathway in 3T3‐L1 adipocytes overexpressing or knocked down for FOXK1 or FOXK2. a, HK2; b, PFKM; c, PKM‐M2; d, ALDOA; e, LDHA; f, MCT1; g, PDK1; h, PDK4; i, PDP1; j, GLUD1. k, Phosphorylation of Ser293, Ser232, and Ser300 of the E1α regulatory subunit using specific antisera. Lower parts show PDC complex after immunoprecipitation by PDH Immunocapture Kit and silver staining. Representative experiments from independent replicate samples are shown. l, Schematic view of FOXK1 and FOXK2 regulation sites in the glycolytic pathway. For gel source data, see Supplementary Fig. 1.
a, Correlation between expression profiles of 3T3-L1 adipocytes overexpressing or knocked down for FOXK1 or FOXK2. x- and y-axes denote log2-transformed expression in RPKM (reads per kilobase of transcript per million). The correlation and P values are derived from Pearson’s correlation test. Precise P values are presented from Pearson’s correlation tests. b, c, Overlaps (b) between genes upregulated and downregulated by FOXK1 and FOXK2 knockdown (left) and overexpression (right) profiles and heat maps (c) showing logFC values of overlapping genes from FOXK1 and FOXK2 knockdown (left) and overexpression (right) profiles. d, Commonly deregulated genes or proteins from RNA-seq (FOXK1, n = 3 and FOXK2, n = 2) and proteome (FOXK1, n = 4 and FOXK2, n = 2) profiles of knockdown and overexpression. The P values for overlaps were calculated using hypergeometric test by assuming all expressed protein-coding genes from this study as background (n = 17,038). e, f, Gene enrichment (biological process) analysis of commonly deregulated genes from FOXK1 and FOXK2 overexpression or knockdown in RNA-seq profiles (e) and proteome profiles (f). Axes denote –log10P from the GeneSCF tool. The Fisher’s exact test P values obtained using GeneSCF functional enrichment tool. g, Regulation of glycolytic pathway genes or proteins in RNA-seq and proteome profiles. RNA-seq data for overexpression or knockdown experiments depicted here were performed and analysed in three biological replicas (FOXK1, n = 3) or two biological replicas (FOXK2, n = 2). Proteomics data for overexpression or knockdown experiments depicted here were performed and analysed in four biological replicas (FOXK1, n = 4) or two biological replicas (FOXK2, n = 2).
a–i, Schematic of predicted binding sites and real-time PCR results for FOXK1 and FOXK2 target genes: Hk2 (a), Pfkm (b), Aldoa (c), Pkm (d), Mct1 (e), Pdk1 (f), Pdk4 (g), Pdp1 (h), and Glud1 (i). Mct1 site #2 is not a FOXK1/FOXK2 binding site and serves as a negative control. Input and ChIP cycle threshold (Ct) values were normalized separately to empty vector control as 1. a, Hk2 ChIP–PCR, n = 2 for control input and FOXK2 ChIP, n = 3 for others; b, Pfkm ChIP-PCR, n = 2 for control input, n = 3 for others; c, Aldoa ChIP–PCR, sites #1 and #3, n = 2 for FOXK1 ChIP, n = 3 for others, site #2, n = 3; d, Pkm ChIP–PCR, n = 2 for control input, n = 3 for others; e, Mct1 ChIP–PCR, site #1, n = 2 for control input, n = 3 for others, site #2, n = 2 for ChIPs, n = 3 for inputs; f, Pdk1 ChIP–PCR, n = 2 for FOXK1 ChIP, n = 3 for others; g, Pdk4 ChIP–PCR, site #1, n = 2 for control and FOXK2 ChIP, n = 3 for others, site #2, n = 2 for control ChIP, n = 3 for others; h, Pdp1 ChIP–PCR, n = 2 for ChIPs, n = 3 for inputs; i, Glud1 ChIP-PCR, n = 3. Experiments replicated once. Unpaired two-sided Student’s t-test, ***P < 0.001, **P < 0.01, *P < 0.05.
This file contains Supplementary Note 1.
Supplementary Figure 1. This file contains gel source data shown in this study. Red rectangle parts were shown in the extended data figures. Primary antibodies used are indicated besides the images.
Supplementary Figure 2. T-cells flow cytometry gating strategy.
This file contains Supplementary Table 1. Proteomics data of 3T3-L1 cells with control and FOXK1 over-expression (OE). Paired Two-sided T-test was performed and there was no adjustments/p-value corrections made.
This file contains Supplementary Table 2. Proteomics data of 3T3-L1 cells with control and Foxk1 knock-down with shRNA (KD). Paired Two-sided T-test was performed and there was no adjustments/p-value corrections made.
This file contains Supplementary Table 3. Proteomics data of 3T3-L1 cells with control and FOXK2 over-expression (OE). Paired Two-sided T-test was performed and there was no adjustments/p-value corrections made.
This file contains Supplementary Table 4. Proteomics data of 3T3-L1 cells with control and Foxk2 knock-down with shRNA (KD). Paired Two-sided T-test was performed and there was no adjustments/p-value corrections made.
About this article
Cite this article
Sukonina, V., Ma, H., Zhang, W. et al. FOXK1 and FOXK2 regulate aerobic glycolysis. Nature 566, 279–283 (2019). https://doi.org/10.1038/s41586-019-0900-5
FOXK1 Promotes Proliferation and Metastasis of Gallbladder Cancer by Activating AKT/mTOR Signaling Pathway
Frontiers in Oncology (2020)
A miR-210-3p regulon that controls the Warburg effect by modulating HIF-1α and p53 activity in triple-negative breast cancer
Cell Death & Disease (2020)
Protein & Cell (2020)
Cell Discovery (2020)
Biochemistry (Moscow), Supplement Series A: Membrane and Cell Biology (2020)