• An Erratum to this article was published on 29 June 2017

This article has been updated

Abstract

Cellular transformation and cancer progression is accompanied by changes in the metabolic landscape. Master co-regulators of metabolism orchestrate the modulation of multiple metabolic pathways through transcriptional programs, and hence constitute a probabilistically parsimonious mechanism for general metabolic rewiring. Here we show that the transcriptional co-activator peroxisome proliferator-activated receptor gamma co-activator 1α (PGC1α) suppresses prostate cancer progression and metastasis. A metabolic co-regulator data mining analysis unveiled that PGC1α is downregulated in prostate cancer and associated with disease progression. Using genetically engineered mouse models and xenografts, we demonstrated that PGC1α opposes prostate cancer progression and metastasis. Mechanistically, the use of integrative metabolomics and transcriptomics revealed that PGC1α activates an oestrogen-related receptor alpha (ERRα)-dependent transcriptional program to elicit a catabolic state and metastasis suppression. Importantly, a signature based on the PGC1α–ERRα pathway exhibited prognostic potential in prostate cancer, thus uncovering the relevance of monitoring and manipulating this pathway for prostate cancer stratification and treatment.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Change history

  • 22 May 2017

    In the original version of this Article, the name of author James David Sutherland was coded wrongly, resulting in it being incorrect when exported to citation databases. This has now been corrected, though no visible changes will be apparent.

Accessions

Primary accessions

Gene Expression Omnibus

Referenced accessions

References

  1. 1.

    et al. Extracellular metabolic energetics can promote cancer progression. Cell 160, 393–406 (2015).

  2. 2.

    , & Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).

  3. 3.

    et al. Evidence for an alternative glycolytic pathway in rapidly proliferating cells. Science 329, 1492–1499 (2010).

  4. 4.

    , & Cancer metabolism: fatty acid oxidation in the limelight. Nat. Rev. Cancer 13, 227–232 (2013).

  5. 5.

    , & Oncometabolites: linking altered metabolism with cancer. J. Clin. Invest. 123, 3652–3658 (2013).

  6. 6.

    et al. Pten positively regulates brown adipose function, energy expenditure, and longevity. Cell Metab. 15, 382–394 (2012).

  7. 7.

    et al. Systemic elevation of PTEN induces a tumor-suppressive metabolic state. Cell 149, 49–62 (2012).

  8. 8.

    , & Tenets of PTEN tumor suppression. Cell 133, 403–414 (2008).

  9. 9.

    , & The functions and regulation of the PTEN tumour suppressor. Nat. Rev. Mol. Cell Biol. 13, 283–296 (2012).

  10. 10.

    , , & Pten is essential for embryonic development and tumour suppression. Nat. Genet. 19, 348–355 (1998).

  11. 11.

    et al. Crucial role of p53-dependent cellular senescence in suppression of Pten-deficient tumorigenesis. Nature 436, 725–730 (2005).

  12. 12.

    et al. Prostate intraepithelial neoplasia induced by prostate restricted Akt activation: the MPAKT model. Proc. Natl Acad. Sci. USA 100, 7841–7846 (2003).

  13. 13.

    et al. Autonomic nerve development contributes to prostate cancer progression. Science 341, 1236361 (2013).

  14. 14.

    et al. SMAD4-dependent barrier constrains prostate cancer growth and metastatic progression. Nature 470, 269–273 (2011).

  15. 15.

    & Prostate cancer progression and metastasis: potential regulatory pathways for therapeutic targeting. Am. J. Clin. Exp. Urol. 2, 92–101 (2014).

  16. 16.

    et al. RapidCaP, a novel GEM model for metastatic prostate cancer analysis and therapy, reveals myc as a driver of Pten-mutant metastasis. Cancer Discov. 4, 318–333 (2014).

  17. 17.

    , , & Transcriptional coregulators: fine-tuning metabolism. Cell Metab. 20, 26–40 (2014).

  18. 18.

    et al. Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc. Natl Acad. Sci. USA 101, 811–816 (2004).

  19. 19.

    et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

  20. 20.

    et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal 6, pl1 (2013).

  21. 21.

    et al. The mutational landscape of lethal castration-resistant prostate cancer. Nature 487, 239–243 (2012).

  22. 22.

    et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 18, 11–22 (2010).

  23. 23.

    et al. Integrative molecular concept modeling of prostate cancer progression. Nat. Genet. 39, 41–51 (2007).

  24. 24.

    et al. Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression. Cancer Cell 8, 393–406 (2005).

  25. 25.

    et al. Integrative clinical genomics of advanced prostate cancer. Cell 161, 1215–1228 (2015).

  26. 26.

    , & Metabolic control through the PGC-1 family of transcription coactivators. Cell Metab. 1, 361–370 (2005).

  27. 27.

    et al. Oncogenic BRAF regulates oxidative metabolism via PGC1α and MITF. Cancer Cell 23, 302–315 (2013).

  28. 28.

    et al. PGC1α expression defines a subset of human melanoma tumors with increased mitochondrial capacity and resistance to oxidative stress. Cancer Cell 23, 287–301 (2013).

  29. 29.

    et al. PGC-1α mediates mitochondrial biogenesis and oxidative phosphorylation in cancer cells to promote metastasis. Nat. Cell Biol. 16, 992–1003 (2014).

  30. 30.

    et al. Suppression of PGC-1α is critical for reprogramming oxidative metabolism in renal cell carcinoma. Cell Rep. 12, 116–127 (2015).

  31. 31.

    et al. Peroxisome proliferator-activated receptor-gamma coactivator 1-α (PGC1α) is a metabolic regulator of intestinal epithelial cell fate. Proc. Natl Acad. Sci. USA 108, 6603–6608 (2011).

  32. 32.

    et al. MYC/PGC-1α balance determines the metabolic phenotype and plasticity of pancreatic cancer stem cells. Cell Metab. 22, 590–605 (2015).

  33. 33.

    et al. The PGC-1α/ERRα axis represses one-carbon metabolism and promotes sensitivity to anti-folate therapy in breast cancer. Cell Rep. 14, 920–931 (2016).

  34. 34.

    et al. Defects in adaptive energy metabolism with CNS-linked hyperactivity in PGC-1α null mice. Cell 119, 121–135 (2004).

  35. 35.

    & Functional crosstalk of PGC-1 coactivators and inflammation in skeletal muscle pathophysiology. Semin. Immunopathol. 36, 27–53 (2014).

  36. 36.

    et al. PGC-1α regulates normal and pathological angiogenesis in the retina. Am. J. Pathol. 182, 255–265 (2013).

  37. 37.

    , , & Faithfull modeling of PTEN loss driven diseases in the mouse. Curr. Top. Microbiol. Immunol. 347, 135–168 (2011).

  38. 38.

    et al. Aberrant Rheb-mediated mTORC1 activation and Pten haploinsufficiency are cooperative oncogenic events. Genes Dev. 22, 2172–2177 (2008).

  39. 39.

    et al. Genome-wide coactivation analysis of PGC-1α identifies BAF60a as a regulator of hepatic lipid metabolism. Cell Metab. 8, 105–117 (2008).

  40. 40.

    et al. Androgens regulate prostate cancer cell growth via an AMPK-PGC-1α-mediated metabolic switch. Oncogene 33, 5251–5261 (2014).

  41. 41.

    et al. Peroxisome proliferator-activated receptor gamma coactivator-1α interacts with the androgen receptor (AR) and promotes prostate cancer cell growth by activating the AR. Mol. Endocrinol. 24, 114–127 (2010).

  42. 42.

    & Osteotropic cancers: from primary tumor to bone. Cancer Lett. 273, 177–193 (2009).

  43. 43.

    et al. Cyclooxygenase-2 inhibitor suppresses tumour progression of prostate cancer bone metastases in nude mice. BJU Int. 113, E164–E177 (2014).

  44. 44.

    & Transcriptional coregulators in the control of energy homeostasis. Trends Cell Biol. 17, 292–301 (2007).

  45. 45.

    , , , & SnapShot: cancer metabolism pathways. Cell Metab. 17, e462–466 (2013).

  46. 46.

    et al. Estrogen-related receptor alpha is critical for the growth of estrogen receptor-negative breast cancer. Cancer Res. 68, 8805–8812 (2008).

  47. 47.

    et al. Receptor-selective coactivators as tools to define the biology of specific receptor-coactivator pairs. Mol. Cell 24, 797–803 (2006).

  48. 48.

    et al. The metabolic regulator ERRα, a downstream target of HER2/IGF-1R, as a therapeutic target in breast cancer. Cancer Cell 20, 500–510 (2011).

  49. 49.

    et al. Oxidative stress inhibits distant metastasis by human melanoma cells. Nature 527, 186–191 (2015).

  50. 50.

    & Aerobic glycolysis: meeting the metabolic requirements of cell proliferation. Annu. Rev. Cell Dev. Biol. 27, 441–464 (2011).

  51. 51.

    et al. A metabolic prosurvival role for PML in breast cancer. J. Clin. Invest. 122, 3088–3100 (2012).

  52. 52.

    et al. Antioxidant and oncogene rescue of metabolic defects caused by loss of matrix attachment. Nature 461, 109–113 (2009).

  53. 53.

    et al. Involvement of ANXA5 and ILKAP in susceptibility to malignant melanoma. PLoS ONE 9, e95522 (2014).

  54. 54.

    et al. Nuclear PTEN regulates the APC-CDH1 tumor-suppressive complex in a phosphatase-independent manner. Cell 144, 187–199 (2011).

  55. 55.

    et al. Differential p53-independent outcomes of p19(Arf) loss in oncogenesis. Sci. Signal 2, ra44 (2009).

  56. 56.

    et al. Differential requirement of mTOR in postmitotic tissues and tumorigenesis. Sci. Signal 2, ra2 (2009).

  57. 57.

    , , , & Mammary cancer stem cells reinitiation assessment at the metastatic niche: the lung and bone. Methods Mol. Biol. 1293, 221–229 (2015).

  58. 58.

    et al. Inhibition of mTORC1 leads to MAPK pathway activation through a PI3K-dependent feedback loop in human cancer. J. Clin. Invest. 118, 3065–3074 (2008).

  59. 59.

    et al. Methodological aspects of the molecular and histological study of prostate cancer: focus on PTEN. Methods 77–78, 25–30 (2015).

  60. 60.

    et al. SIRT3 opposes reprogramming of cancer cell metabolism through HIF1α destabilization. Cancer Cell 19, 416–428 (2011).

  61. 61.

    et al. Metabolic reprogramming is required for antibody production that is suppressed in anergic but exaggerated in chronically BAFF-exposed B cells. J. Immunol. 192, 3626–3636 (2014).

  62. 62.

    et al. Methods to monitor ROS production by fluorescence microscopy and fluorometry. Methods Enzymol. 542, 243–262 (2014).

  63. 63.

    et al. Animal models of human prostate cancer: the consensus report of the New York meeting of the Mouse Models of Human Cancers Consortium Prostate Pathology Committee. Cancer Res. 73, 2718–2736 (2013).

  64. 64.

    et al. High resolution metabolomics with acyl-CoA profiling reveals widespread remodeling in response to diet. Mol. Cell. Proteomics 14, 1489–1500 (2015).

  65. 65.

    , & Development and quantitative evaluation of a high-resolution metabolomics technology. Anal. Chem. 86, 2175–2184 (2014).

  66. 66.

    et al. Quantitative determinants of aerobic glycolysis identify flux through the enzyme GAPDH as a limiting step. eLife 3, e03342 (2014).

  67. 67.

    et al. Regulation of the transcriptional program by DNA methylation during human alphabeta T-cell development. Nucleic Acids Res. 43, 760–774 (2015).

  68. 68.

    , & Statistical computing and graphics: different outcomes of the Wilcoxon—Mann—Whitney test from different statistics packages. Am. Statistician 54, 72–77 (2000).

  69. 69.

    & Experimental Design and Data Analysis for Biologists (Cambridge Univ. Press, 2002).

Download references

Acknowledgements

Apologies to those whose related publications were not cited owing to space limitations. We would like to thank the following researchers: B. Spiegelman (Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA; Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA) for providing the Pgc1aLoxP mice; D. Santamaría and M. Barbacid (Experimental Oncology, Molecular Oncology Programme, Centro Nacional de Investigaciones Oncológicas (CNIO), Madrid, Spain) for technical help and advice with doxycycline-enriched diets in xenograft experiments; P. Puigserver (Department of Cell Biology, Harvard Medical School, and in the Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA) for providing Pgc1α-expressing constructs; B. Carver (Department of Surgery, Division of Urology, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, USA) for help and advice with data set analysis, D. McDonnell (Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, North Carolina, USA) for providing mutant Pgc1αL2L3M-expressing constructs and M. D. Boyano (Department of Cell Biology and Histology, School of Medicine and Dentistry, University of the Basque Country (UPV/EHU), Leioa, Bizkaia, Spain) and A. Buqué (Medical Oncology Research Laboratory, Cruces Universtity Hospital, Bizkaia, Spain) for providing melanoma cell lines. The work of A.C. is supported by the Ramón y Cajal award, the Basque Department of Industry, Tourism and Trade (Etortek), health (2012111086) and education (PI2012-03), Marie Curie (277043), Movember, ISCIII (PI10/01484, PI13/00031), FERO VIII Fellowship and the European Research Council Starting Grant (336343). N.M.-M. is supported by the Spanish Association Against Cancer (AECC). A.C.-M. is supported by the MINECO postdoctoral program and the CIG program from the European commission (660191). A.A.-A. and L.V.-J. are supported by the Basque Government of Education. P.Pinton is grateful to C. degli Scrovegni for continuous support and the work in his laboratory was supported by the Italian Association for Cancer Research (AIRC: IG-14442), the Italian Ministry of Education, University and Research (COFIN no. 20129JLHSY_002, FIRB no. RBAP11FXBC_002, and Futuro in Ricerca no. RBFR10EGVP_001) and the Italian Ministry of Health. R.B. is supported by MINECO (BFU2014-52282-P, BFU2011-25986) and the Basque Government (PI2012/42). The work of V.S.-M. was supported by Cancer Research UK C33043/A12065; Royal Society RG110591. P.Pandya was supported by King’s Overseas Scholarship. Work by the group of G.V. was supported by grants from the Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III (MINECO/ISCIII) together with the European Regional Development Fund (ERDF/FEDER): PS09/01401; PI12/02248 and PI15/00339, Fundación Mutua Madrileña and Fundació la Marató de TV3. C.C.-C. and M.C.-M. were financially supported by NIH P01CA087497. J.W.L. is supported by R00CA168997, R01CA193256 and R21CA201963 from the National Institutes of Health. Work in the M.Graupera laboratory was supported by SAF2014-59950-P from MINECO (Spain), 2014-SGR-725 from the Catalan Government, from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ (REA grant agreement 317250), and the Institute of Health Carlos III (ISC III) and the European Regional Development Fund (ERDF) under the integrated Project of Excellence no. PIE13/00022 (ONCOPROFILE). J.U. is a Juan de la Cierva Researcher (MINECO). A.Bellmunt is a FPI-Severo Ochoa fellowship grantee (MINECO). R.R.G. research support was provided by the Spanish Government (MINECO) and FEDER grant SAF2013-46196, as well as the Generalitat de Catalunya AGAUR 2014-SGR grant 535.

Author information

Author notes

    • Veronica Torrano
    •  & Lorea Valcarcel-Jimenez

    These authors contributed equally to this work.

    • Jason W. Locasale
    •  & Roger R. Gomis

    These authors jointly supervised this work.

Affiliations

  1. CIC bioGUNE, Bizkaia Technology Park, Building 801A, 48160 Derio, Bizkaia, Spain

    • Veronica Torrano
    • , Lorea Valcarcel-Jimenez
    • , Ana Rosa Cortazar
    • , Sonia Fernández-Ruiz
    • , Alfredo Caro-Maldonado
    • , Patricia Zúñiga-García
    • , Natalia Martín-Martín
    • , James David Sutherland
    • , Pilar Sanchez-Mosquera
    • , Laura Bozal-Basterra
    • , Amaia Zabala-Letona
    • , Amaia Arruabarrena-Aristorena
    • , Nieves Embade
    • , Ana Maria Aransay
    • , Rosa Barrio
    •  & Arkaitz Carracedo
  2. Department of Pharmacology and Cancer Biology, Duke Cancer Institute, Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, North Carolina 27710, USA

    • Xiaojing Liu
    •  & Jason W. Locasale
  3. Oncology Programme, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona 08028, Catalonia, Spain

    • Jelena Urosevic
    • , Marc Guiu
    • , Anna Bellmunt
    •  & Roger R. Gomis
  4. Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA

    • Mireia Castillo-Martin
    •  & Carlos Cordon-Cardo
  5. Department of Pathology, Fundação Champalimaud, 1400-038 Lisboa, Portugal

    • Mireia Castillo-Martin
  6. Department of Morphology, Surgery and Experimental Medicine, Section of Pathology, Oncology and Experimental Biology, University of Ferrara, 44100, Italy

    • Giampaolo Morciano
    •  & Paolo Pinton
  7. Vascular Signalling Laboratory, Institut d’Investigació Biomèdica de Bellvitge (IDIBELL), Gran Via de l’Hospitalet 199-203, 08907 L’Hospitalet de Llobregat, Barcelona, Spain

    • Mariona Graupera
  8. Tumour Plasticity Team, Randall Division of Cell and Molecular Biophysics, King’s College London, New Hunt’s House, Guy’s Campus, London SE1 1UL, UK

    • Pahini Pandya
    •  & Victoria Sanz-Moreno
  9. Department of Biochemistry and Molecular Biology I, School of Biology, Complutense University and Instituto de Investigaciones Sanitarias San Carlos (IdISSC), 28040 Madrid, Spain

    • Mar Lorente
    •  & Guillermo Velasco
  10. Biostatistics/Bioinformatics Uni, IRB Barcelona, Parc Científic de Barcelona, 08028 Barcelona, Spain

    • Antonio Berenguer
  11. Department of Pathology, Basurto University Hospital, 48013 Bilbao, Spain

    • Aitziber Ugalde-Olano
  12. Department of Urology, Basurto University Hospital, 48013 Bilbao, Spain

    • Isabel Lacasa-Viscasillas
    • , Ana Loizaga-Iriarte
    •  & Miguel Unda-Urzaiz
  13. Computational Biology, Memorial Sloan-Kettering Cancer Center, New York 10065, USA

    • Nikolaus Schultz
  14. Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd)

    • Ana Maria Aransay
  15. Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain

    • Roger R. Gomis
  16. Ikerbasque, Basque Foundation for Science, 48011 Bilbao, Spain

    • Arkaitz Carracedo
  17. Biochemistry and Molecular Biology Department, University of the Basque Country (UPV/EHU), PO Box 644, E-48080 Bilbao, Spain

    • Arkaitz Carracedo

Authors

  1. Search for Veronica Torrano in:

  2. Search for Lorea Valcarcel-Jimenez in:

  3. Search for Ana Rosa Cortazar in:

  4. Search for Xiaojing Liu in:

  5. Search for Jelena Urosevic in:

  6. Search for Mireia Castillo-Martin in:

  7. Search for Sonia Fernández-Ruiz in:

  8. Search for Giampaolo Morciano in:

  9. Search for Alfredo Caro-Maldonado in:

  10. Search for Marc Guiu in:

  11. Search for Patricia Zúñiga-García in:

  12. Search for Mariona Graupera in:

  13. Search for Anna Bellmunt in:

  14. Search for Pahini Pandya in:

  15. Search for Mar Lorente in:

  16. Search for Natalia Martín-Martín in:

  17. Search for James David Sutherland in:

  18. Search for Pilar Sanchez-Mosquera in:

  19. Search for Laura Bozal-Basterra in:

  20. Search for Amaia Zabala-Letona in:

  21. Search for Amaia Arruabarrena-Aristorena in:

  22. Search for Antonio Berenguer in:

  23. Search for Nieves Embade in:

  24. Search for Aitziber Ugalde-Olano in:

  25. Search for Isabel Lacasa-Viscasillas in:

  26. Search for Ana Loizaga-Iriarte in:

  27. Search for Miguel Unda-Urzaiz in:

  28. Search for Nikolaus Schultz in:

  29. Search for Ana Maria Aransay in:

  30. Search for Victoria Sanz-Moreno in:

  31. Search for Rosa Barrio in:

  32. Search for Guillermo Velasco in:

  33. Search for Paolo Pinton in:

  34. Search for Carlos Cordon-Cardo in:

  35. Search for Jason W. Locasale in:

  36. Search for Roger R. Gomis in:

  37. Search for Arkaitz Carracedo in:

Contributions

V.T. and L.V.-J. performed all in vitro and in vivo experiments, unless specified otherwise. A.R.C. carried out the bioinformatic and biostatistical analysis. A.Berenguer and N.S. provided support and advice in data set retrieval and normalization. S.F.-R. performed the histochemical stainings. P.S.-M. and S.F.-R. performed genotyping analyses. X.L. and J.W.L. contributed to the experimental design and executed the metabolomic analyses. G.M. and P.Pinton performed the biochemical ATP measurement in vitro and mitochondria analysis. G.V., P.Z.-G. and M.L. performed or coordinated (G.V.) subcutaneous xenograft experiments. J.U., A.Bellmunt, M.Guiu and R.R.G. performed or coordinated (R.R.G.) the intra-cardiac and intra-tibial metastasis assays. R.R.G. contributed to the design of the patient gene signature analysis. M.Graupera carried out microvessel staining and quantifications. P.Pandya and V.S.-M. provided technical advice and contributed to in vitro analysis. N.M.-M., A.A.-A. and A.Z.-L. contributed to the experimental design and discussion. A.C.-M. and N.E. performed Seahorse assays. J.D.S. and R.B. performed or coordinated (R.B.) the cloning of Pgc1a in lentiviral vectors. C.C.-C. and M.C.-M. carried out the pathological analysis and scoring of the xenografts and GEMMs. A.U.-O., I.L.-V., A.L.-I. and M.U.-U. provided BPH and PCa samples for gene expression analysis from Basurto University Hospital. A.M.A. contributed to the discussion of the results. A.C. directed the project, contributed to data analysis and wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Arkaitz Carracedo.

Integrated supplementary information

Supplementary figures

  1. 1.

    8

  2. 2.

    9

  3. 3.

    10

  4. 4.

    11

  5. 5.

    12

  6. 6.

    13

  7. 7.

    14

  8. 8.

    Unprocessed blots.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    Supplementary Information

Excel files

  1. 1.

    Supplementary Table 1

    Supplementary Information

  2. 2.

    Supplementary Table 2

    Supplementary Information

  3. 3.

    Supplementary Table 3

    Supplementary Information

  4. 4.

    Supplementary Table 4

    Supplementary Information

  5. 5.

    Supplementary Table 5

    Supplementary Information

  6. 6.

    Supplementary Table 6

    Supplementary Information

  7. 7.

    Supplementary Table 7

    Supplementary Information

  8. 8.

    Supplementary Table 8

    Supplementary Information

  9. 9.

    Supplementary Table 9

    Supplementary Information

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/ncb3357

Further reading