Abstract

Stress is integral to tumour evolution, and cancer cell survival depends on stress management. We found that cancer-associated stress chronically activates the bioenergetic sensor AMP kinase (AMPK) and, to survive, tumour cells hijack an AMPK-regulated stress response pathway conserved in normal cells. Analysis of The Cancer Genome Atlas data revealed that AMPK isoforms are highly expressed in the lethal human cancer glioblastoma (GBM). We show that AMPK inhibition reduces viability of patient-derived GBM stem cells (GSCs) and tumours. In stressed (exercised) skeletal muscle, AMPK is activated to cooperate with CREB1 (cAMP response element binding protein-1) and promote glucose metabolism. We demonstrate that oncogenic stress chronically activates AMPK in GSCs that coopt the AMPK–CREB1 pathway to coordinate tumour bioenergetics through the transcription factors HIF1α and GABPA. Finally, we show that adult mice tolerate systemic deletion of AMPK, supporting the use of AMPK pharmacological inhibitors in the treatment of GBM.

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Change history

  • 06 September 2018

    In the version of this Article originally published, in ref. 34 the first author’s name was spelled incorrectly. The correct reference is: Rodón, L. et al. Active CREB1 promotes a malignant TGFβ2 autocrine loop in glioblastoma. Cancer Discov. 10, 1230–1241 (2014). This has now been amended in all online versions of the Article.

  • 08 August 2018

    In the version of this Article originally published, the competing interests statement was missing. The authors declare no competing interests; this statement has now been added in all online versions of the Article.

References

  1. 1.

    Hardie, D. G. AMP-activated protein kinase: maintaining energy homeostasis at the cellular and whole-body levels. Annu. Rev. Nutr. 34, 31–55 (2014).

  2. 2.

    Carling, D., Thornton, C., Woods, A. & Sanders, M. J. AMP-activated protein kinase: new regulation, new roles? Biochem. J. 445, 11–27 (2012).

  3. 3.

    Dasgupta, B. & Chhipa, R. R. Evolving lessons on the complex role of AMPK in normal physiology and cancer. Trends Pharmacol. Sci. 37, 192–206 (2016).

  4. 4.

    Iseli, T. J. et al. AMP-activated protein kinase beta subunit tethers alpha and gamma subunits via its C-terminal sequence (186–270). J. Biol. Chem. 280, 13395–13400 (2005).

  5. 5.

    Shaw, R. J. et al. The tumor suppressor LKB1 kinase directly activates AMP-activated kinase and regulates apoptosis in response to energy stress. Proc. Natl Acad. Sci. USA 101, 3329–3335 (2004).

  6. 6.

    Hawley, S. A. et al. Calmodulin-dependent protein kinase kinase-beta is an alternative upstream kinase for AMP-activated protein kinase. Cell Metab. 2, 9–19 (2005).

  7. 7.

    Carling, D., Zammit, V. A. & Hardie, D. G. A common bicyclic protein kinase cascade inactivates the regulatory enzymes of fatty acid and cholesterol biosynthesis. FEBS Lett. 223, 217–222 (1987).

  8. 8.

    Inoki, K., Zhu, T. & Guan, K. L. TSC2 mediates cellular energy response to control cell growth and survival. Cell 115, 577–590 (2003).

  9. 9.

    Gwinn, D. M. et al. AMPK phosphorylation of raptor mediates a metabolic checkpoint. Mol. Cell 30, 214–226 (2008).

  10. 10.

    Faubert, B. et al. AMPK is a negative regulator of the Warburg effect and suppresses tumor growth in vivo. Cell Metab. 17, 113–124 (2013).

  11. 11.

    Rios, M., Foretz, M. & Viollet, B. et al. AMPK activation by oncogenesis is required to maintain cancer cell proliferation in astrocytic tumors. Cancer Res. 73, 2628–2638 (2013).

  12. 12.

    Liu, X. et al. Discrete mechanisms of mTOR and cell cycle regulation by AMPK agonists independent of AMPK. Proc. Natl Acad. Sci. USA 111, E435–E444 (2014).

  13. 13.

    Swinnen, J. V. et al. Mimicry of a cellular low energy status blocks tumor cell anabolism and suppresses the malignant phenotype. Can. Res. 65, 2441–2448 (2005).

  14. 14.

    Tang, Y. C., Williams, B. R., Siegel, J. J. & Amon, A. Identification of aneuploidy-selective antiproliferation compounds. Cell 144, 499–512 (2011).

  15. 15.

    Dowling, R. J., Zakikhani, M., Fantus, I. G., Pollak, M. & Sonenberg, N. Metformin inhibits mammalian target of rapamycin-dependent translation initiation in breast cancer cells. Cancer Res. 67, 10804–10812 (2007).

  16. 16.

    Guo, D. et al. The AMPK agonist AICAR inhibits the growth of EGFRvIII-expressing glioblastomas by inhibiting lipogenesis. Proc. Natl Acad. Sci. USA 106, 12931–12937 (2009).

  17. 17.

    Yan., M. et al. The tumor suppressor folliculin regulates AMPK-dependent metabolic transformation. J. Clin. Invest. 124, 2640–2650 (2014).

  18. 18.

    Laderoute., K. R. et al. 5′-AMP-activated protein kinase (AMPK) supports the growth of aggressive experimental human breast cancer tumors. J. Biol. Chem. 289, 22850–22864 (2014).

  19. 19.

    D’Amico, D. et al. Non-canonical Hedgehog/AMPK-mediated control of polyamine metabolism supports neuronal and nedulloblastoma cell growth. Dev. Cell. 35, 21–35 (2015).

  20. 20.

    Bungard., D. et al. Signaling kinase AMPK activates stress-promoted transcription via histone H2B phosphorylation. Science 329, 1201–1205 (2010).

  21. 21.

    Laderoute., K. R. et al. 5′-AMP-activated protein kinase (AMPK) is induced by low-oxygen and glucose deprivation conditions found in solid-tumor microenvironments. Mol. Cell. Biol. 26, 5336–5347 (2006).

  22. 22.

    Liu., L. et al. Deregulated MYC expression induces dependence upon AMPK-related kinase 5. Nature 483, 608–612 (2012).

  23. 23.

    Shackelford., D. B. et al. mTOR and HIF-1alpha-mediated tumor metabolism in an LKB1 mouse model of Peutz-Jeghers syndrome. Proc. Natl Acad. Sci. USA 106, 11137–11142 (2009).

  24. 24.

    Kishton., R. J. et al. AMPK is essential to balance glycolysis and mitochondrial metabolism to control T-ALL cell stress and survival. Cell Metab. 23, 649–662 (2016).

  25. 25.

    Wu., S. et al. AMPK-mediated increase of glycolysis as an adaptive response to oxidative stress in human cells: implication of the cell survival in mitochondrial diseases. Biochem. Biophys. Acta 1822, 233–247 (2012).

  26. 26.

    E. Doménech, E. et al. AMPK and PFKFB3 mediate glycolysis and survival in response to mitophagy during mitotic arrest. Nat. Cell Biol. 17, 1304–1316 (2015).

  27. 27.

    Almeida, A. et al. Nitric oxide switches on glycolysis through the AMP protein kinase and 6-phosphofructo-2-kinase pathway. Nat. Cell Biol. 6, 45–51 (2004).

  28. 28.

    Fumarola, C. et al. Effects of sorafenib on energy metabolism in breast cancer cells: role of AMPK-mTORC1 signaling. Breast Cancer Res. Treat. 141, 67–78 (2013).

  29. 29.

    Reszec, J. et al. The expression of hypoxia-inducible factor-1 in primary brain tumors. Int J. Neurosci. Sep. 123, 657–662 (2013).

  30. 30.

    Mayer, A. et al. Differential expression of HIF-1 in glioblastoma multiforme and anaplastic astrocytoma. Int J. Oncol. Oct. 41, 1260–1270 (2012).

  31. 31.

    Barresi, V. et al. p-CREB expression in human gliomas: potential use in the differential diagnosis between astrocytoma and oligodendroglioma. Human Pathol. 46, 231–238 (2015).

  32. 32.

    Li, Z. et al. Hypoxia-inducible factors regulate tumoregenic capacity of glioma stem cells. Cancer Cell 15, 501–513 (2009).

  33. 33.

    Fan, Y. et al. Profilin-1 phosphorylation directs angiocrine expression and glioblastoma progression through HIF1a accumulation. Nat. Cell Biol. 16, 445–456 (2014).

  34. 34.

    Rodón, L. et al. Active CREB1 promotes a malignant TGFβ2 autocrine loop in glioblastoma. Cancer Discov. 10, 1230–1241 (2014).

  35. 35.

    Chow, L. M. et al. Cooperativity within and among Pten, p53, and Rb pathways induces high-grade astrocytoma in adult brain. Cancer Cell 19, 305–316 (2011).

  36. 36.

    Chae, Y. C. et al. Control of tumor bioenergetics and survival stress signaling by mitochondrial HSP90s. Cancer Cell 22, 331–344 (2012).

  37. 37.

    Ros, S. et al. Functional metabolic screen identifies 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 4 as an important regulator of prostate cancer cell survival. Cancer Discov. 2, 328–343 (2012).

  38. 38.

    Fan, Q. et al. Akt and autophagy cooperate to promote survival of drug-resistance glioma. Sci. Signal. 3, ra81 (2010).

  39. 39.

    Fiedler, T. et al. Arginine deprivation by arginine deiminase of Streptococcus pyogenes controls primary glioblastoma growth in vitro and in vivo. Can. Biol. Ther. 16, 1047–1055 (2015).

  40. 40.

    Shirwany, N. A. & Zou, M. H. AMPK: a cellular metabolic and redox sensor. A minireview. Front Biosci. 19, 447–474 (2014).

  41. 41.

    Jeon, S. M., Chandel, N. S. & Hay, N. AMPK regulates NADPH homeostasis to promote tumour cell survival during energy stress. Nature 485, 661–665 (2012).

  42. 42.

    Denko, N. C. Hypoxia, HIF1 and glucose metabolism in the solid tumour. Nat. Rev. Cancer 8, 705–713 (2008).

  43. 43.

    Liu, W., Shen, S. M., Zhao, X. Y. & Chen, G. Q. Targeted genes and interacting proteins of hypoxia inducible factor-1. Int J. Bioche Mol. Biol. 3, 165–178 (2012).

  44. 44.

    Benita, Y. et al. An integrative genomics approach identifies Hypoxia Inducible Factor-1 (HIF-1)-target genes that form the core response to hypoxia. Nucleic Acids Res. 37, 4587–4602 (2009).

  45. 45.

    Kelly, D. P. & Scarpulla, R. C. Transcriptional regulatory circuits controlling mitochondrial biogenesis and function. Genes Dev. 18, 357–368 (2004).

  46. 46.

    Bruni, F., Polosa, P. L., Gadaleta, M. N., Cantatore, P. & Roberti, M. Nuclear respiratory factor 2 induces the expression of many but not all human proteins acting in mitochondrial DNA transcription and replication. J. Biol. Chem. 285, 3939–3948 (2010).

  47. 47.

    Larsson, N. G. et al. Mitochondrial transcription factor A is necessary for mtDNA maintenance and embryogenesis in mice. Nat. Genet. 18, 231–236 (1998).

  48. 48.

    Cantó, C. AMPK regulates energy expenditure by modulating NAD+ metabolism and SIRT1 activity. Nature 458, 1056–1060 (2009).

  49. 49.

    Levesque, M. J. & Raj, A. Single-chromosome transcriptional profiling reveals chromosomal gene expression regulation. Nat. Methods 10, 246–248 (2013).

  50. 50.

    Selak, M. A. et al. Succinate links TCA cycle dysfucntion to oncogenesis by inhibiting HIF-α prolyl hydroxylases. Cancer Cell 1, 77–85 (2005).

  51. 51.

    Kaelin, W. G. The Von Hippel-Lindau tumor suppressor protein: O2 sensing and cancer. Nat. Rev. Cancer 8, 865–873 (2008).

  52. 52.

    Seidel, S. et al. A hypoxic niche regulates glioblastoma stem cells through hypoxia inducible factor 2 alpha. Brain 133, 983–995 (2010).

  53. 53.

    Dickinson, A. et al. The regulation of mitochondrial copy number in glioblastoma cells. Cell Death Diff. 20, 1644–1653 (2013).

  54. 54.

    Sakamoto, K. et al. Deficiency of LKB1 in skeletal muscle prevents AMPK activation and glucose uptake during contraction. EMBO J. 24, 1810–1820 (2005).

  55. 55.

    Thomson, D. M. et al. Skeletal muscle and heart LKB1 deficiency causes decreased voluntary running and reduced muscle mitochondrial marker enzyme expression in mice. Am. J. Physiol. Endocrinol. Metab. 292, E196–E202 (2007).

  56. 56.

    McGee, S. L. & Hargreaves, M. AMPK-mediated regulation of transcription in skeletal muscle. Clin. Sci. 118, 507–518 (2010).

  57. 57.

    Thomson, D. M. et al. AMP-activated protein kinase phosphorylates transcription factors of the CREB family. J. Appl. Physiol. 104, 429–438 (2008).

  58. 58.

    Li, Y., Cummings, R. T., Cunningham, B. R., Chen, Y. & Zhou, G. Homogeneous assays for adenosine 5’-monophosphate-activated protein kinase. Anal. Biochem. 321, 151–156 (2003).

  59. 59.

    Mayr, B. & Montminy, M. Transcriptional regulation by the phosphorylation-dependent factor CREB. Nat. Rev. Mol. Cell Biol. 2, 599–609 (2001).

  60. 60.

    Jang, T. et al. 5’-AMP-activated protein kinase activity is elevated early during primary brain tumor development in the rat. Int. J. Cancer 128, 2230–2239 (2011).

  61. 61.

    Ferreira, D. et al. in Novel Approaches in Biomarkers Discovery and Therapeutic Targets in Cancer (ed. Lopez-Camarillo, C.) Ch. 6 (IntechOpen, London, 2013).

  62. 62.

    Rios, M. et al. Lipoprotein internalisation induced by oncogenic AMPK activation is essential to maintain glioblastoma cell growth. Eur. J. Cancer 50, 3187–3197 (2014).

  63. 63.

    Saito, Y., Chapple, R. H., Lin, A., Kitano, A. & Nakada, D. AMPK protects leukemia-initiating cells in myeloid leukemias from metabolic stress in the bone marrow. Cell Stem Cell 17, 585–596 (2015).

  64. 64.

    Mo, J. S. et al. Cellular energy stress induces AMPK-mediated regulation of YAP and the Hippo pathway. Nat. Cell Biol. 17, 500–510 (2015).

  65. 65.

    Liu, X. et al. LncRNA NBR2 engages a metabolic checkpoint by regulating AMPK under energy stress. Nat. Cell Biol. 18, 431–442 (2016).

  66. 66.

    Papandreu, I. et al. HIF-1 mediates adaptation to hypoxia by actively downregulating mitochondrial oxygen consumption. Cell Metab. 3, 187–197 (2006).

  67. 67.

    Dasgupta, B. & Milbrandt, J. AMP-activated protein kinase phosphorylates retinoblastoma protein to control mammalian brain development. Dev. Cell 16, 256–270 (2009).

  68. 68.

    Pooya, S. et al. The tumour suppressor LKB1 regulates myelination through mitochondrial metabolism. Nat. Comm. 5, 4993 (2014).

  69. 69.

    Komurov, K., Dursun, S., Erdin, S. & Ram, P. T. NetWalker: a contextual network analysis tool for functional genomics. BMC Genom. 13, 282 (2012).

  70. 70.

    Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

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Acknowledgements

We thank S. Kim (University of Alabama) for maintaining and providing primary GSC lines; A. Hinge (Cincinnati Children’s Hospital) for help with flow cytometry; G. Huang (Cincinnati Children’s Hospital) for providing the HRE luciferase reporter construct and for help with ChIP experiments; J. Bridges (Cincinnati Children’s Hospital) for providing constitutive active HIF1α; S. Wells for plenti-CMV-luc plasmid; S. Elledge (Massachusetts Institute of Technology) for providing pInducer plasmid; R. Jones (McGuill University, Canada) for AMPKα2 dominant-negative plasmid; F. Furnari (Lugwig Institute of Cancer Research, San Diego, CA) for pLV EGFRvIII Hygro plasmid; P. Mischel (University of California, San Diego) for U87EGFRvIII glioma cells; D. Carling (Imperial College, London) for providing Compound 991; X. Liu (previously, Cincinnati Children’s Hospital) for assistance with experiments; P. Malik (Cincinnati Children’s Hospital) for providing assistance in determining lentiviral copy number; E. Boscolo (Cincinnati Children’s Hospital) for providing Isolectin B4; B. Viollet (INSERM, Institut Cochin, Paris, France) for providing AMPKα1−/− and AMPKα2lox/lox mice; and A. Kumar (Cincinnati Children’s Hospital) for providing Rosa26 CreER mice (originally from NCI). This work was supported by the CCTST1 Translational Grant Award; Pilot Innovation award, CCHMC; University of Cincinnati Cancer Center Affinity Grant Award, CancerFreeKids and National Institute of Health (1R01NS075291-01A1 and 1R01NS099161-01) (all to B.D.).

Author information

Author notes

    • Zaza Khuchua

    Present address: Sechenov University, Department of Biochemistry, Moscow, Russian Federation

    • Rishi Raj Chhipa

    Present address: Department of Pathology and Laboratory Medicine, University of Pennsylvania, PA, USA

Affiliations

  1. Division of Oncology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    • Rishi Raj Chhipa
    • , Qiang Fan
    • , Jane Anderson
    • , Ranjithmenon Muraleedharan
    • , Lionel M. Chow
    •  & Biplab Dasgupta
  2. Division of Molecular and Cardiovascular Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    • Yan Huang
    •  & Zaza Khuchua
  3. Division of Pathology and Laboratory Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    • Georgianne Ciraolo
  4. Division of Center for Autoimmune Genomics and Etiology and Biomedical Informatics and Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    • Xiaoting Chen
    •  & Matthew T. Weirauch
  5. Division of Experimental Hematology and Cancer Biology, Cincinnati, OH, USA

    • Ronald Waclaw
    • , Nancy Ratner
    •  & Kakajan Komurov
  6. Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    • Matthew Kofron
    •  & Matthew T. Weirauch
  7. Department of Pathology and Laboratory Medicine, University of Cincinnati, Cincinnati, OH, USA

    • Ady Kendler
  8. Department of Neurosurgery, Brain Tumor Center, University of Cincinnati Neuroscience Institute and Mayfield Clinic, Cincinnati, OH, USA

    • Christopher McPherson
  9. Department of Neurosurgery, University of Alabama, Cincinnati, OH, USA

    • Ichiro Nakano
  10. Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    • Nupur Dasgupta

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Contributions

B.D. and R.C. conceived the experiments, analysed data and wrote the manuscript. R.C. performed most of the experiments. Q.F., J.A. and R.M. performed additional experiments. G.C. helped with electron microscopy. N.R, R.W. and L.M.C. provided valuable reagents and experimental suggestions. N.R. also helped in manuscript editing. Y.H. performed and Z.K. supervised mitochondrial complex activity measurements. M.K. helped with smFISH experiments. M.W. and X.C. provided bioinformatic support for transcription factor analysis. A.K. helped with identification of GBM patient samples. C.M. performed surgery and helped coordinate tissue procurement from the operating room. I.N. provided a few primary GSC lines. N.D. and K.K. provided bioinformatic support for RNA-seq and TCGA data analysis. N.D. also performed statistical analysis.

Corresponding author

Correspondence to Biplab Dasgupta.

Integrated supplementary information

  1. Supplementary Figure 1 Oncogenic stress-associated high expression of AMPK in glioblastoma.

    a, Kaplan-Meier survival plots of LGG patients expressing phosphorylated ACC (Source: TCGA). b, Kaplan-Meier survival plots of GBM patients expressing AMPK subunits (Source: TCGA). c, Western blot (WB) of pAMPK and pACC in Human GBM and normal human brain tissue. X indicates tissue degradation. Actin was used as loading control. d, e, IHC of pACC, DAPI (nuclei) and the microglia marker IBA-1 in human GBM tissue (d), and xenograft of the human GBM line 326 in NSG mice (e). Scale bar = 50 μm (d) and 100μM (e). f, Oncogenesis-associated stress markers are high in primary GSC lines relative to normal human astrocytes (NHA). WB showing markers of genotoxic stress (pATM), and endoplasmic reticulum (ER) stress (BIP and sXBP1). Actin was used as loading control. g, IHC of γH2AX foci in GSC and NHA. Scale bar 100 μm. Nuclei were stained with DAPI. Scale bar = 20 μm h, Box plots Source: (TCGA) showing expression of ER stress transcripts in GBM and normal brain (HSPA5 is the gene name for BIP). The edges on the boxplots indicate the first and 3rd quartile (25th- and 75th percentile) of the data, with the line in the middle being the median. The whiskers on the boxplots extend another 1.5x of the inter-quartile range (between 25%-75% range of data) from the edges of the boxes, respectively. i, j, WB of sXBP1, BIP, CHOP, pATM in NHA treated with tunicamycin and hydroxyurea (HU). k, WB of pAMPK, AMPK, pACC and ACC in NHA treated with indicated reagents. Actin was used as loading control. l, m, Quantification of band intensities in the same order as in (k). n, WB of indicated proteins in NHA expressing oncogenic EGFRvIII, constitutively active KRASG12V or PTEN shRNA. Note: PTEN appears to be significantly downregulated in EGFRvIII and KRASG12V cells. o, p, Quantification of band intensities in (n). All western blots represent data from 2 (f, n, k), or 3 (c, I, j) independent repeats. Scanned images of unprocessed blots are shown in Supplementary Fig. 9.

  2. Supplementary Figure 2 AMPK is essential for optimal growth of primary GBM orthotopic xenografts in mice.

    a, b, Luciferase imaging of mice to monitor tumour growth of GSC lines expressing AMPKβ1 or nontarget (NT) shRNA at indicated days. (n = 8-15 mice per line). Primary GSC lines AC17, 1123 were infected with NT or AMPKβ1 shRNA for two days and 10,000 Trypan blue negative live cells were transplanted into the cortex of NSG mice 48 hours after in vitro lentiviral transduction (when AMPK β1shRNA had negligible effect on viability). c, Quantification of tumour luminescence. Total range of tumour luminescence of mice was quantified three weeks after cell transplantation. (n = 20 mice / genotype). *p ≤ 0.0001. d, Luciferase imaging of mice to monitor tumour growth of a GSC line expressing AMPK α1α2 shRNA or nontarget (NT) shRNA at indicated days. (n = 6 mice per genotype). e, Kaplan-Meier survival data of mice in (d). f, Kaplan-Meier survival data of mice transplanted with U87EGFRvIII GBM serum line expressing NT or AMPK β1 shRNA. (n = 4 mice per genotype). g, Expression of cumate-inducible GFP in 293T cells. Scale bar 100μm. h, WB showing expression of cumate-inducible mouse AMPKβ1 (subcloned in the SparQ vector) in 293T cells. i, IHC pf pAMPK and pACC in NT and AMPKβ1 shRNA expressing tumours at indicated days. (T = tumour; N = normal brain). Scale bar 100 μm. j, Q-RTPCR of human AMPK β1 (using human-specific primers) in tumour cells isolated from NT shRNA or AMPKβ1 shRNA tumours. (n = 3). *p = 0.0007. k, WB of AMPKβ1/β2 and pAMPK in tumour-derived cells as in (j). Error bars; mean +/- S.D. Statistical significance in (c, j); two-tailed t-test. n values represent independent experiments. Source data are available in Supplementary Table 4. All western blots represent data from 2 (h) or 3 (k) independent repeats. Unprocessed blots are shown in Supplementary Fig. 9.

  3. Supplementary Figure 3 GSC death occurs independent of AMPK-regulated mTOR and autophagy pathways.

    a.WB of pULK1 (AMPK target) and autophagy substrate LC3 in AMPK-silenced GSCs. ULK1 and actin are used as loading controls. b, WB of autophagy substrates LC3 and P62 in NT or AMPKβ1shRNA expressing cells treated with Bafilomycin A showing autophagy flux. c, Viability of GSCs and HepG2 liver cancer line treated with autophagy inhibitors Bafilomycin and Chloroquine, or infected with NT or ATG13 shRNAs. (Average of two independent experiments). d, Superoxide detection by flow cytometry in NT or AMPKβ1 shRNA expressing GSCs using MitoSox Red. (Average of two independent experiments). e, Viability of GSCs expressing NT or AMPKβ1 shRNA treated with the reducing agent N-acetylcysteine (NAC) or the ACC inhibitor TOFA. (Average of two independent experiments). f, ATP levels in GSC lines expressing AMPKβ1 shRNA or NT shRNA at indicated times. ATP value was normalized to protein. (n = 3). *p ≤ 0.002; + ≤ 0.004. g, Viability of GSCs expressing NT or AMPKβ1 shRNA treated with the mTORC1 inhibitor rapamycin (5nM). (n = 3). *p ≤ 0.01; + ≤ 0.004. h, WB of stem cell markers Musashi and Nanog in GSCs expressing NT and AMPKβ1 shRNA. Error bars; mean +/- S/d. Statistical significance in above experiments was assessed using two-tailed t-test. n values represent independent experiments. Source data are available in Supplementary Table 4. All western blots represent data from 2-3 independent repeats. Unprocessed blots are shown in Supplementary Fig. 9.

  4. Supplementary Figure 4 GSC bioenergetics is downregulated in AMPK-silenced GSCs.

    a, b, Molecular network of most downregulated glycolysis genes (a) and mitochondrial genes (b) in two AMPKβ1-sienced GSCs. Node colour denotes extent of downregulation (darker = greater downregulation). Ingenuity Pathway was also used to confirm NetWalker analysis. c, Heatmap showing differentially expressed genes in indicated pathways in AMPKβ1 shRNA GSCs relative to NT shRNA expressing GSCs. d. Q-RTPCR analysis of HIF1α, GABPA and their target genes in GSCs using a second AMPKβ1 shRNA (Average of two independent experiments). +p = 0.001; * 0.01; $* 0.002; # 0.006; ** 0.003; $0.02; +* 0.05. e, WB of indicated proteins in GSCs expressing NT or AMPKβ1shRNA. f, Glucose uptake (at 30 min) in GSCs expressing NT or AMPKβ1shRNA. . (n = 3). g, WB of GLUT3 in GSCs expressing NT or AMPKβ1shRNA. h, WB of PGC1α in nontarget and AMPKβ1 shRNA expressing GSCs. i, WB using anti-acetyl antibody following immunoprecipitation with PGC1α antibody in control (NT) and AMPK silenced GSC. j, WB showing overexpression of Myc-tagged PGC1α in GSCs. k, Q-PCR analysis of ND4 and β actin DNA in control and AMPK-silenced GSCs overexpressing PGC1α. (Average of two independent experiments). ns = nonsignificant. l, Oxygen consumption rate (OCR) in control and AMPK-silenced GSCs overexpressing PGC1α. (Average of two independent experiments). m, Q-RT-PCR analysis of ND4 and β actin RNA in control and AMPK-silenced GSCs treated with SIRT1720 for 48h. (Average of two independent experiments). n, OCR in control and AMPK-silenced GSCs treated with SIRT1720. (Average of two independent experiments). Error bars; mean +/- S.D. Statistical significance in above experiments was assessed using two-tailed t-test. n values represent independent experiments. Source data are available in Supplementary Table 4. All western blots represent data from 2 (e, h, I, j) or 3 (e, g) independent repeats. Unprocessed blots are shown in Supplementary Fig. 9.

  5. Supplementary Figure 5 Glucose metabolism is diminished in AMPK-silenced GBM cells.

    a-h, GSC10 cells were fed with U13C glucose and 13C carbon incorporation in the metabolites of glycolysis and TCA cycle was quantitated by HPLC/Mass spectrometry. Shown are several metabolites of glycolysis (G6P/F6P, G3P), pentose phosphate pathway (6PG, R5P/X5P, S7P) and TCA cycle (Suc, Mal) that show reduced incorporation of 13C glucose carbon at indicated times. Note: Aspartate is generated from the TCA cycle metabolite oxaloacetate. G6P/F6P, glucose 6-phosphate/fructose 6-phosphate; G3P, glucose 3-phosphate; 6-PG, 6-phosphogluconate; R5P/X5P, ribulose 5-phosphate/xylulose 5-phosphate; S7P, sedoheptulose 7-phosphate; Suc; succinate; Mal, malate; Asp, aspartate. Duplicate samples were processed. ** p = 0.003 (M+6 G6P/F6P; M+6 and M+5 6PG); # p = 0.001 (M+ 2 G3P); * p ≤ 0.05 (M+5 R5P/X5P; M+7 and M+6 S7P; M+2 Suc; M+3 Mal; M+3 and M+2 Asp). Source data are available in Supplementary Table 4.

  6. Supplementary Figure 6 Evidence for CREB1 regulation of GABPA and HIF1α.

    a, b, UCSC Genome Browser screenshot showing multiple ENCODE CREB1 ChIP-seq datasets in a variety of cell types. "UCSC Genes" track at the top shows the genomic location of GABPA (a) and HIF1α (b). Thick bars indicate UTRs and coding regions; thin bars indicate introns (arrows show direction of transcription). Coloured tracks below indicate CREB1 ChIP-seq binding signals in various cell types. Higher peaks indicate genomic locations bound by CREB1 in the indicated cell type.

  7. Supplementary Figure 7 Active CREB1 is highly expressed in GBM and is regulated by AMPK.

    a,b, WB of CREB1 and pCREB1 in human GBM compared to normal brain tissue (a), and in primary GSC lines compared to normal human astrocytes (NHA) (b). c, Q-RTPCR analysis of CREB1 in NHA and GSC lines. (n = 3). *p ≤ 0.0005. d, High expression of CREB1, CREB1-binding protein EP300, HIF1α, GABPA and TFAM in GBM relative to normal human brain tissue (Source: TCGA). * p ≤ 0.001;** ≤ 0.005; ++ = 0.01. The edges on the boxplots indicate the first and 3rd quartile (25th- and 75th percentile) of the data, with the line in the middle being the median. The whiskers on the boxplots extend another 1.5x of the inter-quartile range (between 25%-75% range of data) from the edges of the boxes, respectively. e, Kaplan-Meier survival plots of LGG patients expressing CREB1 (Source: TCGA). f, Immunocytochemistry using pCREB1 antibody in NHA treated with the AMPK activating small molecule 991 for 30 minutes. Nuclei were detected with DAPI. Scale bar 50 μm. g, Quantification of nuclear pCREB1 signal intensity in vehicle or 991-treated NHA. *p < 0.0001. (n=36 cells/condition). h, IHC of pAMPK and pCREB in GSC326 tumours. Nuclear staining is shown using DAPI. Scale bar 50 μm. Note: Mainly nuclear pCREB but both nuclear and cytoplasmic pAMPK; nuclear pAMPK co-localizes with pCREB (inset). i, WB of pAMPK and pACC and pCREB1 in AMPK α2, β1, γ1 overexpressing NHA. Note: Stabilization of β1 and β2 is increased in cells overexpressing α2 and γ1. j, Viability of NHA overexpressing AMPK β1 subunit. (n = 3). NS = nonsignificant. k, WB of pCREB1, pACC, pPKA substrates, CREB1 and ACC in NT and AMPKβ1 shRNA expressing GSCs treated with the PKA activator forskolin. Error bars; mean +/- S.D. Statistical significance in above experiments was assessed using Student’s two-tailed t-test, except (d) where Welch’s t test was used. n values present independent experiments unless stated otherwise. Source data are available in Supplementary Table 4. All western blots represent data from 2 (i, k) or 3 (a, b) independent repeats. Scanned images of unprocessed blots are shown in Supplementary Fig. 9.

  8. Supplementary Figure 8 Adult mice with global AMPK deletion has normal lifespan.

    a, WB showing efficiency of Cre-recombination in the indicated tissues. Expression of total and active AMPK in control and AMPK-deleted tissues in mice are shown. β actin was used as loading control. Note that AMPK α subunits are unstable in the absence of β subunits. b, Kaplan-Meier survival curve of control and AMPK β subunit-deleted mice. (n = 12 mice/genotype). c, Body weight of control and AMPK β subunit-deleted mice are shown. Note: AMPK α subunit deleted mice (genotype #2) also had normal lifespan and bodyweight. (n = 12 mice/genotype). Error bars; mean +/- S.D. Statistical significance in above experiments was assessed using two-tailed t-test.

  9. Supplementary Figure 9

    Unprocessed scans of all western blot data.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–9, Supplementary Table and Supplementary Video legends

  2. Reporting Summary

  3. Supplementary Table 1

    Antibody name, vendor, catalogue number and dilution information

  4. Supplementary Table 2

    Silencing RNA ID and sequence information

  5. Supplementary Table 3

    Primer sequence information

  6. Supplementary Table 4

    Supplementary Source data

  7. Supplementary Video 1

    Normal proliferation of GSC10 line in the presence of non-target shRNA

  8. Supplementary Video 2

    Apoptosis of GSC10 line in the presence of AMPK-β1 shRNA

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DOI

https://doi.org/10.1038/s41556-018-0126-z

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