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Glycogen metabolism is dispensable for tumour progression in clear cell renal cell carcinoma

Nature Metabolism volume 3, pages 327–336 (2021)Cite this article

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Abstract

Glycogen accumulation is a highly consistent, distinguishable characteristic of clear cell renal cell carcinoma (ccRCC)1. While elevated glycogen pools might be advantageous for ccRCC cells in nutrient-deprived microenvironments to sustain tumour viability, data supporting a biological role for glycogen in ccRCC are lacking. Here, we demonstrate that glycogen metabolism is not required for ccRCC proliferation in vitro nor xenograft tumour growth in vivo. Disruption of glycogen synthesis by CRISPR-mediated knockout of glycogen synthase 1 (GYS1) has no effect on proliferation in multiple cell lines, regardless of glucose concentrations or oxygen levels. Similarly, prevention of glycogen breakdown by deletion or pharmacological inhibition of glycogen phosphorylase B (PYGB) and L (PYGL) has no impact on cell viability under any condition tested. Lastly, in vivo xenograft experiments using the ccRCC cell line, UMRC2, reveal no substantial changes in tumour size or volume when glycogen metabolism is altered, largely mimicking the phenotype of our in vitro observations. Our findings suggest that glycogen build-up in established ccRCC tumour cells is likely to be a secondary, and apparently dispensable, consequence of constitutively active hypoxia-inducible factor 1-alpha (HIF-1α) signalling.

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Fig. 1: Glycogen synthesis and breakdown are hyperactive in ccRCC tumours.
Fig. 2: Elevated levels of the glycogen synthesis enzyme GYS1 in ccRCC tumours does not affect proliferation in vitro.
Fig. 3: ccRCC tumour cells do not rely on glycogen breakdown for growth in vitro despite glycolytic entry of glycogen-derived glucose.
Fig. 4: Genetic perturbation of glycogen metabolism does not alter ccRCC xenograft progression in vivo.

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Data availability

The human patient ccRCC tumour and normal tissue RNA-seq dataset was obtained from the TCGA at https://www.cbioportal.org (see the TCGA RNA-seq analysis for further details). The human patient ccRCC tumour and normal metabolomics dataset was obtained from Hakimi et al.17 (https://www.cell.com/cancer-cell/comments/S1535-6108(15)00468-7#secsectitle0145). All data that support the findings of this study are available from the corresponding author upon request. Source data are provided with this paper.

References

  1. Ericsson, J. L., Seljelid, R., & Orrenius, S. Comparative light and electron microscopic observations of the cytoplasmic matrix in renal carcinomas. Virchows Arch. Pathol. Anat. Physiol. Klin. Med. 341, 204–223 (1966).

    Article  CAS  Google Scholar 

  2. National Cancer Institute Surveillance, Epidemiology, and End Results Program. Cancer Sta Facts: Kidney and Renal Pelvis Cancer (NCI, 2018).

  3. Sanchez, D. J. & Simon, M. C. Genetic and metabolic hallmarks of clear cell renal cell carcinoma. Biochim. Biophys. Acta Rev. Cancer 1870, 23–31 (2018).

    Article  CAS  Google Scholar 

  4. Gordan, J. D. et al. HIF-α effects on c-Myc distinguish two subtypes of sporadic VHL-deficient clear cell renal carcinoma. Cancer Cell 14, 435–446 (2008).

    Article  CAS  Google Scholar 

  5. Wallace, E. M. et al. A small-molecule antagonist of HIF2α is efficacious in preclinical models of renal cell carcinoma. Cancer Res. 76, 5491–5500 (2016).

    Article  CAS  Google Scholar 

  6. Courtney, K. D. et al. Phase I dose-escalation trial of PT2385, a first-in-class hypoxia-inducible factor-2α antagonist in patients with previously treated advanced clear cell renal cell carcinoma. J. Clin. Oncol. 36, 867–874 (2018).

    Article  CAS  Google Scholar 

  7. Zhang, Y. et al. Addressing metabolic heterogeneity in clear cell renal cell carcinoma with quantitative Dixon MRI. JCI Insight 2, e94278 (2017).

    Article  Google Scholar 

  8. Riscal, R., Skuli, N. & Simon, M. C. Even cancer cells watch their cholesterol!. Mol. Cell. 76, 220–231 (2019).

    Article  CAS  Google Scholar 

  9. Qiu, B. et al. HIF2α-dependent lipid storage promotes endoplasmic reticulum homeostasis in clear-cell renal cell carcinoma. Cancer Discov. 5, 652–667 (2015).

    Article  CAS  Google Scholar 

  10. Ackerman, D. et al. Triglycerides promote lipid homeostasis during hypoxic stress by balancing fatty acid saturation. Cell Rep. 24, 2596–2605.e5 (2018).

    Article  CAS  Google Scholar 

  11. Favaro, E. et al. Glucose utilization via glycogen phosphorylase sustains proliferation and prevents premature senescence in cancer cells. Cell Metab. 16, 751–764 (2012).

    Article  CAS  Google Scholar 

  12. Sun, R. C. et al. Nuclear glycogenolysis modulates histone acetylation in human non-small cell lung cancers. Cell Metab. 30, 903–916.e7 (2019).

    Article  CAS  Google Scholar 

  13. Adeva-Andany, M. M., González-Lucán, M., Donapetry-García, C., Fernández-Fernández, C. & Ameneiros-Rodríguez, E. Glycogen metabolism in humans. BBA Clin. 5, 85–100 (2016).

    Article  Google Scholar 

  14. Calì, C., Tauffenberger, A. & Magistretti, P. The strategic location of glycogen and lactate: from body energy reserve to brain plasticity. Front. Cell. Neurosci. 13, 82 (2019).

    Article  Google Scholar 

  15. Rousset, M., Zweibaum, A. & Fogh, J. Presence of glycogen and growth-related variations in 58 cultured human tumor cell lines of various tissue origins. Cancer Res. 41, 1165–1170 (1981).

    CAS  PubMed  Google Scholar 

  16. Li, B. et al. Fructose-1,6-bisphosphatase opposes renal carcinoma progression. Nature 513, 251–255 (2014).

    Article  CAS  Google Scholar 

  17. Hakimi, A. A. et al. An integrated metabolic atlas of clear cell renal cell carcinoma. Cancer Cell 29, 104–116 (2016).

    Article  CAS  Google Scholar 

  18. Zois, C. E. & Harris, A. L. Glycogen metabolism has a key role in the cancer microenvironment and provides new targets for cancer therapy. J. Mol. Med. (Berl.) 94, 137–154 (2016).

    Article  CAS  Google Scholar 

  19. Munro, S., Ceulemans, H., Bollen, M., Diplexcito, J. & Cohen, P. T. W. A novel glycogen-targeting subunit of protein phosphatase 1 that is regulated by insulin and shows differential tissue distribution in humans and rodents. FEBS J. 272, 1478–1489 (2005).

    Article  CAS  Google Scholar 

  20. Shen, G.-M., Zhang, F.-L., Liu, X.-L. & Zhang, J.-W. Hypoxia-inducible factor 1-mediated regulation of PPP1R3C promotes glycogen accumulation in human MCF-7 cells under hypoxia. FEBS Lett. 584, 4366–4372 (2010).

    Article  CAS  Google Scholar 

  21. Yang, R. et al. Loss of protein targeting to glycogen sensitizes human hepatocellular carcinoma cells towards glucose deprivation mediated oxidative stress and cell death. Biosci. Rep. 35, e00207 (2015).

    Article  Google Scholar 

  22. Jurczak, M. J. et al. Transgenic overexpression of protein targeting to glycogen markedly increases adipocytic glycogen storage in mice. Am. J. Physiol. Endocrinol. Metab. 292, E952–E963 (2007).

    Article  CAS  Google Scholar 

  23. Greenberg, C. C., Meredith, K. N., Yan, L. & Brady, M. J. Protein targeting to glycogen overexpression results in the specific enhancement of glycogen storage in 3T3-L1 adipocytes. J. Biol. Chem. 278, 30835–30842 (2003).

    Article  CAS  Google Scholar 

  24. Bhanot, H. et al. Pathological glycogenesis through glycogen synthase 1 and suppression of excessive AMP kinase activity in myeloid leukemia cells. Leukemia 29, 1555–1563 (2015).

    Article  CAS  Google Scholar 

  25. Varnier, M., Leese, G. P., Thompson, J. & Rennie, M. J. Stimulatory effect of glutamine on glycogen accumulation in human skeletal muscle. Am. J. Physiol. 269, E309–E315 (1995).

    CAS  PubMed  Google Scholar 

  26. Lundsgaard, A.-M., Fritzen, A. M. & Kiens, B. Molecular regulation of fatty acid oxidation in skeletal muscle during aerobic exercise. Trends Endocrinol. Metab. 29, 18–30 (2018).

    Article  CAS  Google Scholar 

  27. Hardin, C. D. & Roberts, T. M. Differential regulation of glucose and glycogen metabolism in vascular smooth muscle by exogenous substrates. J. Mol. Cell. Cardiol. 29, 1207–1216 (1997).

    Article  CAS  Google Scholar 

  28. Vinci, M. et al. Advances in establishment and analysis of three-dimensional tumor spheroid-based functional assays for target validation and drug evaluation. BMC Biol. 10, 29 (2012).

    Article  CAS  Google Scholar 

  29. Ivanov, D. P. et al. Multiplexing spheroid volume, resazurin and acid phosphatase viability assays for high-throughput screening of tumour spheroids and stem cell neurospheres. PLoS ONE 9, e103817 (2014).

    Article  Google Scholar 

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Acknowledgements

We thank the past and present members of the Simon laboratory for their helpful discussions on the project. We are grateful to Y. Daikhin, O. Horyn and I. Nissim (Metabolomics Core Facility, Children’s Hospital of Philadelphia) for the glycogen tracing measurements. This work was supported by a National Institutes of Health National Research Service Award (no. F31CA239514-01 to J.G.) and National Cancer Institute grant nos. P01CA104838 and R35CA220483 to M.C.S.

Author information

Author notes

  1. These authors contributed equally: Hong Xie, Jun Song.

Authors and Affiliations

  1. Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Hong Xie, Jun Song, Jason Godfrey, Romain Riscal, Nicolas Skuli & M. Celeste Simon

  2. Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Hong Xie, Jason Godfrey, Romain Riscal, Nicolas Skuli & M. Celeste Simon

  3. Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA

    Jun Song

  4. Department of Pediatrics, Biochemistry, and Biophysics, University of Pennsylvania, Philadelphia, PA, USA

    Itzhak Nissim

  5. Division of Child Development and Metabolic Disease, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Itzhak Nissim

  6. Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    M. Celeste Simon

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Contributions

H.X., J.S. and M.C.S. conceived the project and designed the experiments. H.X. and J.S. performed most of the experiments described. J.G., R.R. and N.S. helped with the final xenograft and revision work. I.N. performed the mass spectrometry analysis on the glycogen labelling experiment. H.X., J.G. and M.C.S. wrote the manuscript.

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Correspondence to M. Celeste Simon.

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The authors declare no competing interests.

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Peer review information Nature Metabolism thanks Adrian Harris, Scott Welford and the other, anonymous, reviewers for their contribution to the peer review of this work. Primary Handling Editor: George Caputa.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Glycogen synthesis and breakdown are hyperactive in ccRCC tumors.

Glycogen synthesis and breakdown are hyperactive in ccRCC tumors. a, Glycogen quantification of six ccRCC cell lines in replete conditions (10% FBS, 25 mM glucose RPMI) normalized to protein mass; n = 3 technical replicates. Data presented as mean ± SD. ‘H2’: cell lines exclusively expressing HIF-2α. ‘H1H2’: cell lines expressing both HIF-1α and HIF-2α. b, Abundance of glycogen metabolism-related metabolites (glucose-1-phosphate, maltose, maltotriose, and maltotetraose) in n = 138 biologically independent human ccRCC tumor/normal paired samples; data extracted from Hakimi AA, et al16. Data presented as mean ± SEM. c, qRT-PCR of GYS1, PYGB, PYGL, and PYGM in 20 matched ccRCC and adjacent normal kidney tissues; n = 3 technical replicates per tissue sample. Data presented as mean ± SD. Ribosomal subunit 45S RNA (45S) utilized as the endogenous control gene. P values determined by two-tailed Student’s t test. ***, P < 0.001.

Extended Data Fig. 2 Glycogen synthesis enzyme GYS1 is overrepresented in ccRCC and regulated by HIF-1α.

Glycogen synthesis enzyme GYS1 is overrepresented in ccRCC and regulated by HIF-1α. a, UMRC2 and RCC4 (H1H2) ccRCC cells transduced with two independent shRNAs against HIF1A (shHIF1A_52 and shHIF1A_9), EPAS1 (shHIF2A_6 and shHIF2A_7), or a SCR (scrambled shRNA) control. qRT-PCR and Western blot for GYS1 shown. PDK1 and CCND1 included as positive controls for HIF-1α and HIF-2α suppression, respectively. For qRT-PCR, TBP and ACTB utilized as endogenous control genes, and relative mRNA expression determined by normalizing to expression in SCR samples; n = 3 technical replicates. Data are presented as mean ± SD. b, Glycogen quantification in UMRC2 cells transduced with indicated shRNAs after 4 days; n = 3 technical replicates. Data are presented as mean ± SD. Relative glycogen amount determined by normalizing to levels in SCR samples. c, EPAS1 (HIF-2A) depleted by two independent shRNAs (shHIF2A_6 and shHIF2A_7) in 786-O (H2) ccRCC cells, GYS1 expression shown by qRT-PCR; n = 3 technical replicates. Data are presented as mean ± SD. d, Schematic representation of GYS regulation by PP1 and PPP1R3 (see text for details). e, Normalized RNA-seq reads of PPP1R3B and PPP1R3C in stage-stratified ccRCC (n = 428) and normal kidney (n = 66) samples; n denotes biologically independent human tissue samples. RNA-seq data obtained from TCGA. Box plots (min. to max. all points): center=median, bounds=25th and 75th percentiles, whiskers=5th and 95th percentiles. P values determined by two-tailed Student’s t test. ***, P < 0.001.

Source data

Extended Data Fig. 3 Glycogen is dispensable for ccRCC cell growth in vitro.

Glycogen is dispensable for ccRCC cell growth in vitro. a, RCC4, UOK101, and 786-O ccRCC cells transduced with two independent sgRNAs against GYS1 (sg1 and sg3) or a control sgRNA (sgC). Western blot analysis performed 7 days after virus infection to assess GYS1 expression. b, Glycogen levels measured in cells described in a on day 7 after virus infection; n = 3 technical replicates. Data presented as mean ± SD. Relative glycogen amount was determined by normalizing to glycogen level in sgC cells. c, Representative images and relative volumes of spheroids formed by 786-O cells described in a after 19 days culture; n = 24 biologically independent spheroids. Data presented as mean ± SEM. Relative volume was determined by normalizing to that of sgC spheroids. Numbers denote average relative volumes. d, Growth curves for cells described in a cultured in medium containing 1% FBS combined with indicated glucose and oxygen concentrations; n = 6 biologically independent cell populations. Data presented as mean ± SEM. Relative absorbance was determined by normalizing to values at Day 0. P values determined by two-tailed Student’s t test. ***, P < 0.001.

Source data

Extended Data Fig. 4 PYGL is not required for glycogen breakdown and in vitro ccRCC cell growth.

PYGL is not required for glycogen breakdown and in vitro ccRCC cell growth. a,b, Protein assessment of 786-O and UMRC2 ccRCC cells transduced with three independent or two pooled sgRNAs against PYGL (sg1, sg3, sg4, or sg3 + 4) or a control sgRNA (sgC). Samples collected at 7 (a) or 6,8 (b) days after lentiviral infection. c, UMRC2 cells cultured in glucose-free medium for indicated time points, glycogen extracted and quantified. Relative glycogen amount determined by normalizing to glycogen level in cells at 0 hour. c, Cells described in b cultured in medium with 25 mM glucose or starved in glucose-free medium for 6 hours, glycogen extracted and quantified. Relative glycogen amount determined by normalizing to glycogen level in sgC cells cultured in medium with 25 mM glucose. e, Cells described in b cultured in 0.5% O2 for indicated time points, glycogen extracted and quantified. Relative glycogen amount determined by normalizing to glycogen level in sgC cells cultured in 21% O2. f, Growth curves for cells described in b cultured in indicated conditions; n = 6 biologically independent cell populations. Relative absorbance determined by normalizing to values at Day 0. g, Protein assessment of pooled sgRNAs 1 + 4 targeting PYGB (sgPYGB) or overexpression of PYGB (PYGB OE), upper and bottom panels respectively. sgC: control (guide targeting LacZ); sgPYGB/L: double knockout. h, Growth assays of UMRC2 under the indicated conditions. Live cell numbers were measured by Trypan Blue exclusion, and finalized values adjusted for dilution; n = 3 biologically independent cell populations. PYGB knockout and PYGB overexpression (upper and bottom panels respectively). Parental refers to uninfected UMRC2 cells. Sidak’s multiple comparison test was used to determine significance (ns, P > 0.05; *, 0.05 < P < 0.005; **, 0.005 < P < 0.0005; ***, P < 0.0005). I, qRT-PCR on UMRC2 cells for glycogen phosphorylase isoforms; n = 3 technical replicates. Data presented as mean ±SD. Ribosomal subunit 45S RNA (45S) utilized as the endogenous control gene. For all glycogen measurements, data from n = 3 technical replicates and presented as mean ± SD. For all growth curves, data are presented as mean ± SEM. All other P values determined by two-tailed Student’s t test. ***, P < 0.001.

Source data

Extended Data Fig. 5 ccRCC tumor cells do not rely on glycogen breakdown for growth.

ccRCC tumor cells do not rely on glycogen breakdown for growth. a, WT and PYGL KO 786-O cells transduced with a control sgRNA against LacZ (sgC) or combined two sgRNAs targeting PYGB (sgPYGB/L), respectively. Top 50% GFP positive cells sorted for culture. Western blot analysis performed 14 days after virus infection to assess PYGL and PYGB expression. SE, short exposure; LE, long exposure. b, Cells described in a cultured in medium with 25 mM glucose or starved in glucose-free medium for 6 hours, glycogen extracted and quantified. Relative glycogen amount determined by normalizing to glycogen level in sgC cells cultured in medium with 25 mM glucose. c, UMRC2 and 786-O sgC vs. sgPYGL/B ccRCC cells cultured in 0.5% O2 for indicated time points, glycogen extracted and quantified. Relative glycogen amount determined by normalizing to glycogen level in sgC cells cultured in 21% O2. d, Growth curves for UMRC2 cells described in Fig. 3c and 786-O cells described in a cultured in indicated conditions. Relative absorbance determined by normalizing to values at Day 0. e, 786-O cells cultured in medium with 25 mM or 0 mM glucose, treated with indicated concentrations of DMSO or GPi for 6 hours. Glycogen extracted and quantified. Relative glycogen amount determined by normalizing to glycogen level in cells cultured in 25 mM glucose condition. f, UMRC2 and 786-O cells cultured in medium with 25 mM, 2 mM, or 0 mM glucose, treated with DMSO or 10 μM GPi in 21% O2 or 0.5% O2 for 48 hours. Glycogen extracted and quantified. Relative glycogen amount determined by normalizing to glycogen level in cells cultured in 25 mM glucose condition treated with DMSO. g, Growth curves for UMRC2 and 786-O parental cells treated with DMSO, 5 μM, or 10 μM GPi and cultured in indicated conditions. Relative absorbance determined by normalizing to values at Day 0. For all glycogen measurements, data from n = 3 technical replicates and presented as mean ± SD. For all growth curves, data from n = 6 biologically independent cell populations and presented as mean ± SEM. P values determined by two-tailed Student’s t test. ***, P < 0.001.

Source data

Extended Data Fig. 6 Glycogen-derived glucose broadly enters the central carbon pathway during glucose starvation.

Glycogen-derived glucose broadly enters the central carbon pathway during glucose starvation. a, Glycogen labeling experimental design. [U-13C]: uniformly labeled heavy carbon (13 C); GPi: glycogen phosphorylase inhibitor. Small blue or red circles denote free, unlabeled or labeled glucose, respectively. Large blue or red undefined shapes denote unlabeled or labeled glycogen, respectively. b, Sample collection scheme for metabolomic analysis following labeled glycogen breakdown or retention. c, Percentage of labeled glucose (Glucose M + 6) relative to unlabeled glucose in cells over 6 hours of glycogen breakdown or retention; n = 3 biologically independent cell populations. d, Fold change in APE relative to time 0 for indicated metabolites after 3 hours (upper panel) and 6 hours (bottom panel) of glycogen breakdown or retention; n = 3 biologically independent cell populations. For all metabolite measurements, data presented as mean ± SEM. CIT: citrate; SUCC: succinate; FUM: fumarate; MAL: malate; ASP: aspartate; GLU: glutamate; ALA: alanine; GLY: glycine; SER: serine; R5P: ribose-5-phosphate. (+1,2,3 denotes number of 13 C carbons). Sidak’s multiple comparison test was used to determine significance (ns, P > 0.05; *, 0.05 < P < 0.005; **, 0.005 < P < 0.0005; ***, P < 0.0005).

Extended Data Fig. 7 Glycogen availability does not alter cell migration.

Glycogen availability does not alter cell migration. a, Representative field image of crystal violet-stained UMRC2 cells under the specified conditions and genetic alterations. Scale bar=200 μm. b, Quantification of cell migration calculated as average cell number per field; n = 3 biologically independent cell populations (average count of 4 center-oriented fields per sample). Data presented as mean ± SEM. sgC: UMRC2 control sgLacZ; sgPYGB/L: UMRC2 PYGB/L double knockout. Sidak’s multiple comparison test was used to determine significance (ns, P > 0.05; *, 0.05 < P < 0.005; **, 0.005 < P < 0.0005; ***, P < 0.0005).

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Xie, H., Song, J., Godfrey, J. et al. Glycogen metabolism is dispensable for tumour progression in clear cell renal cell carcinoma. Nat Metab 3, 327–336 (2021). https://doi.org/10.1038/s42255-021-00367-x

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