Mitochondrial metabolism promotes adaptation to proteotoxic stress

An Author Correction to this article was published on 04 June 2019

Abstract

The mechanisms by which cells adapt to proteotoxic stress are largely unknown, but are key to understanding how tumor cells, particularly in vivo, are largely resistant to proteasome inhibitors. Analysis of cancer cell lines, mouse xenografts and patient-derived tumor samples all showed an association between mitochondrial metabolism and proteasome inhibitor sensitivity. When cells were forced to use oxidative phosphorylation rather than glycolysis, they became proteasome-inhibitor resistant. This mitochondrial state, however, creates a unique vulnerability: sensitivity to the small molecule compound elesclomol. Genome-wide CRISPR–Cas9 screening showed that a single gene, encoding the mitochondrial reductase FDX1, could rescue elesclomol-induced cell death. Enzymatic function and nuclear-magnetic-resonance-based analyses further showed that FDX1 is the direct target of elesclomol, which promotes a unique form of copper-dependent cell death. These studies explain a fundamental mechanism by which cells adapt to proteotoxic stress and suggest strategies to mitigate proteasome inhibitor resistance.

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Fig. 1: Mitochondrial energy metabolism is associated with the proteasome inhibitor-resistant Lo19S state.
Fig. 2: Increased mitochondrial energy metabolism (Hi-Mito) is sufficient to promote proteotoxic stress tolerance.
Fig. 3: The proteasome inhibitor-resistant Lo19S state exhibits increased sensitivity to elesclomol.
Fig. 4: FDX1 is the primary mediator of elesclomol-induced toxicity.
Fig. 5: Elesclomol inhibits the natural function of FDX1 in the Fe–S cluster biosynthesis, serving as a neo-substrate when bound to copper.
Fig. 6: Elesclomol-mediated copper-dependent cell death is not inhibited by known apoptosis and ferroptosis inhibitors.

Data availability

The data that support the findings of this study are available in the paper (and its Supplementary Information files) or on a public server (GEO: GSE123639). Additional information and reasonable requests for data, resources, sequences and reagents should be directed to and will be fulfilled by the corresponding author.

References

  1. 1.

    Luo, J. et al. A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the ras oncogene. Cell 137, 835–848 (2009).

    CAS  Article  Google Scholar 

  2. 2.

    Petrocca, F. et al. A genome-wide siRNA screen identifies proteasome addiction as a vulnerability of basal-like triple-negative breast cancer cells. Cancer Cell 24, 182–196 (2013).

    CAS  Article  Google Scholar 

  3. 3.

    Adams, J. et al. Proteasome inhibitors: a novel class of potent and effective antitumor agents. Cancer Res. 59, 2615–2622 (1999).

    CAS  PubMed  Google Scholar 

  4. 4.

    Deshaies, R. J. Proteotoxic crisis, the ubiquitin-proteasome system, and cancer therapy. BMC Biol. 12, 94 (2014).

    Article  Google Scholar 

  5. 5.

    Holbeck, S. L., Collins, J. M. & Doroshow, J. H. Analysis of food and drug administration-approved anticancer agents in the NCI60 panel of human tumor cell lines. Mol. Cancer Ther. 9, 1451–1460 (2010).

    CAS  Article  Google Scholar 

  6. 6.

    Orlowski, R. Z. & Kuhn, D. J. Proteasome inhibitors in cancer therapy: lessons from the first decade. Clin. Cancer Res. 14, 1649–1657 (2008).

    CAS  Article  Google Scholar 

  7. 7.

    Markovina, S. et al. Bortezomib-resistant nuclear factor-kappaB activity in multiple myeloma cells. Mol. Cancer Res. 6, 1356–1364 (2008).

    CAS  Article  Google Scholar 

  8. 8.

    Li, B. et al. The nuclear factor (erythroid-derived 2)-like 2 and proteasome maturation protein axis mediate bortezomib resistance in multiple myeloma. J. Biol. Chem. 290, 29854–29868 (2015).

    CAS  Article  Google Scholar 

  9. 9.

    Zhang, X. D. et al. Tight junction protein 1 modulates proteasome capacity and proteasome inhibitor sensitivity in multiple myeloma via EGFR/JAK1/STAT3 signaling. Cancer Cell 29, 639–652 (2016).

    CAS  Article  Google Scholar 

  10. 10.

    Kisselev, A. F., van der Linden, W. A. & Overkleeft, H. S. Proteasome inhibitors: an expanding army attacking a unique target. Chem. Biol. 19, 99–115 (2012).

    CAS  Article  Google Scholar 

  11. 11.

    Tsvetkov, P. et al. Suppression of 19S proteasome subunits marks emergence of an altered cell state in diverse cancers. Proc. Natl Acad. Sci. USA 114, 382–387 (2017).

    CAS  Article  Google Scholar 

  12. 12.

    Tsvetkov, P. et al. Compromising the 19S proteasome complex protects cells from reduced flux through the proteasome. eLife 4, https://doi.org/10.7554/eLife.08467 (2015).

  13. 13.

    Shi, C. X. et al. CRISPR genome-wide screening identifies dependence on the proteasome subunit PSMC6 for bortezomib sensitivity in multiple myeloma. Mol. Cancer. Ther. 16, 2862–2870 (2017).

    CAS  Article  Google Scholar 

  14. 14.

    Acosta-Alvear, D. et al. Paradoxical resistance of multiple myeloma to proteasome inhibitors by decreased levels of 19S proteasomal subunits. eLife 4, e08153 (2015).

    Article  Google Scholar 

  15. 15.

    Gohil, V. M. et al. Nutrient-sensitized screening for drugs that shift energy metabolism from mitochondrial respiration to glycolysis. Nat. Biotechnol. 28, 249–255 (2010).

    CAS  Article  Google Scholar 

  16. 16.

    Yu, C. et al. High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines. Nat. Biotechnol. 34, 419–423 (2016).

    CAS  Article  Google Scholar 

  17. 17.

    Ackler, S. et al. The Bcl-2 inhibitor ABT-263 enhances the response of multiple chemotherapeutic regimens in hematologic tumors in vivo. Cancer Chemo. Pharmacol. 66, 869–880 (2010).

    CAS  Article  Google Scholar 

  18. 18.

    O’Day, S. et al. Phase II, randomized, controlled, double-blinded trial of weekly elesclomol plus paclitaxel versus paclitaxel alone for stage IV metastatic melanoma. J. Clin. Oncol. 27, 5452–5458 (2009).

    Article  Google Scholar 

  19. 19.

    Hasinoff, B. B., Yadav, A. A., Patel, D. & Wu, X. The cytotoxicity of the anticancer drug elesclomol is due to oxidative stress indirectly mediated through its complex with Cu(II). J. Inorg. Biochem. 137, 22–30 (2014).

    CAS  Article  Google Scholar 

  20. 20.

    Nagai, M. et al. The oncology drug elesclomol selectively transports copper to the mitochondria to induce oxidative stress in cancer cells. Free Radic. Biol. Med. 52, 2142–2150 (2012).

    CAS  Article  Google Scholar 

  21. 21.

    Soma, S. et al. Elesclomol restores mitochondrial function in genetic models of copper deficiency. Proc. Natl Acad. Sci. USA 115, 8161–8166 (2018).

    CAS  Article  Google Scholar 

  22. 22.

    Yadav, A. A., Patel, D., Wu, X. & Hasinoff, B. B. Molecular mechanisms of the biological activity of the anticancer drug elesclomol and its complexes with Cu(II), Ni(II) and Pt(II). J. Inorg. Biochem. 126, 1–6 (2013).

    CAS  Article  Google Scholar 

  23. 23.

    Barbi de Moura, M. et al. Mitochondrial respiration–an important therapeutic target in melanoma. PLoS ONE 7, e40690 (2012).

    Article  Google Scholar 

  24. 24.

    Blackman, R. K. et al. Mitochondrial electron transport is the cellular target of the oncology drug elesclomol. PLoS ONE 7, e29798 (2012).

    CAS  Article  Google Scholar 

  25. 25.

    Kirshner, J. R. et al. Elesclomol induces cancer cell apoptosis through oxidative stress. Mol Cancer Ther. 7, 2319–2327 (2008).

    CAS  Article  Google Scholar 

  26. 26.

    Cai, K., Tonelli, M., Frederick, R. O. & Markley, J. L. Human mitochondrial ferredoxin 1 (FDX1) and ferredoxin 2 (FDX2) both bind cysteine desulfurase and donate electrons for iron–sulfur cluster biosynthesis. Biochemistry 56, 487–499 (2017).

    CAS  Article  Google Scholar 

  27. 27.

    Sheftel, A. D. et al. Humans possess two mitochondrial ferredoxins, Fdx1 and Fdx2, with distinct roles in steroidogenesis, heme, and Fe/S cluster biosynthesis. Proc. Natl Acad. Sci. USA 107, 11775–11780 (2010).

    CAS  Article  Google Scholar 

  28. 28.

    Shi, Y., Ghosh, M., Kovtunovych, G., Crooks, D. R. & Rouault, T. A. Both human ferredoxins 1 and 2 and ferredoxin reductase are important for iron-sulfur cluster biogenesis. Biochim Biophys. Acta 1823, 484–492 (2012).

    CAS  Article  Google Scholar 

  29. 29.

    Arroyo, J. D. et al. A genome-wide CRISPR death screen identifies genes essential for oxidative phosphorylation. Cell Metab. 24, 875–885 (2016).

    CAS  Article  Google Scholar 

  30. 30.

    Yang, W. S. et al. Regulation of ferroptotic cancer cell death by GPX4. Cell 156, 317–331 (2014).

    CAS  Article  Google Scholar 

  31. 31.

    Shimada, K. et al. Copper-binding small molecule induces oxidative stress and cell-cycle arrest in glioblastoma-patient-derived cells. Cell Chem. Biol. 25, 585–594 e587 (2018).

    CAS  Article  Google Scholar 

  32. 32.

    Tardito, S. et al. Copper binding agents acting as copper ionophores lead to caspase inhibition and paraptotic cell death in human cancer cells. J. Am. Chem. Soc. 133, 6235–6242 (2011).

    CAS  Article  Google Scholar 

  33. 33.

    Cen, D., Brayton, D., Shahandeh, B., Meyskens, F. L. Jr. & Farmer, P. J. Disulfiram facilitates intracellular Cu uptake and induces apoptosis in human melanoma cells. J. Med. Chem. 47, 6914–6920 (2004).

    CAS  Article  Google Scholar 

  34. 34.

    Kuntz, E. M. et al. Targeting mitochondrial oxidative phosphorylation eradicates therapy-resistant chronic myeloid leukemia stem cells. Nat. Med. 23, 1234–1240 (2017).

    CAS  Article  Google Scholar 

  35. 35.

    Lee, K. M. et al. MYC and MCL1 cooperatively promote chemotherapy-resistant breast cancer stem cells via regulation of mitochondrial oxidative phosphorylation. Cell Metab. 26, 633–647 e637 (2017).

    CAS  Article  Google Scholar 

  36. 36.

    Matassa, D. S. et al. Oxidative metabolism drives inflammation-induced platinum resistance in human ovarian cancer. Cell Death Differ. 23, 1542–1554 (2016).

    CAS  Article  Google Scholar 

  37. 37.

    Vazquez, F. et al. PGC1alpha expression defines a subset of human melanoma tumors with increased mitochondrial capacity and resistance to oxidative stress. Cancer Cell 23, 287–301 (2013).

    CAS  Article  Google Scholar 

  38. 38.

    Vellinga, T. T. et al. SIRT1/PGC1alpha-dependent increase in oxidative phosphorylation supports chemotherapy resistance of colon cancer. Clin. Cancer Res. 21, 2870–2879 (2015).

    CAS  Article  Google Scholar 

  39. 39.

    Soriano, G. P. et al. Proteasome inhibitor-adapted myeloma cells are largely independent from proteasome activity and show complex proteomic changes, in particular in redox and energy metabolism. Leukemia 30, 2198–2207 (2016).

    CAS  Article  Google Scholar 

  40. 40.

    Zaal, E. A. et al. Bortezomib resistance in multiple myeloma is associated with increased serine synthesis. Cancer Metab. 5, 7 (2017).

    Article  Google Scholar 

  41. 41.

    Frumkin, I. et al. Gene architectures that minimize cost of gene expression. Mol. Cell 65, 142–153 (2017).

    CAS  Article  Google Scholar 

  42. 42.

    Peth, A., Nathan, J. A. & Goldberg, A. L. The ATP costs and time required to degrade ubiquitinated proteins by the 26 S proteasome. J. Biol. Chem. 288, 29215–29222 (2013).

    CAS  Article  Google Scholar 

  43. 43.

    Raynes, R., Pomatto, L. C. & Davies, K. J. Degradation of oxidized proteins by the proteasome: distinguishing between the 20S, 26S, and immunoproteasome proteolytic pathways. Mol. Aspects. Med. 50, 41–55 (2016).

    CAS  Article  Google Scholar 

  44. 44.

    Vabulas, R. M. & Hartl, F. U. Protein synthesis upon acute nutrient restriction relies on proteasome function. Science 310, 1960–1963 (2005).

    CAS  Article  Google Scholar 

  45. 45.

    Suraweera, A., Munch, C., Hanssum, A. & Bertolotti, A. Failure of amino acid homeostasis causes cell death following proteasome inhibition. Mol. Cell 48, 242–253 (2012).

    CAS  Article  Google Scholar 

  46. 46.

    Wang, X., Yen, J., Kaiser, P. & Huang, L. Regulation of the 26S proteasome complex during oxidative stress. Sci. Signal 3, ra88 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Cho-Park, P. F. & Steller, H. Proteasome regulation by ADP-ribosylation. Cell 153, 614–627 (2013).

    CAS  Article  Google Scholar 

  48. 48.

    Tsvetkov, P. et al. NADH binds and stabilizes the 26S proteasomes independent of ATP. J. Biol. Chem. 289, 11272–11281 (2014).

    CAS  Article  Google Scholar 

  49. 49.

    Rousseau, A. & Bertolotti, A. An evolutionarily conserved pathway controls proteasome homeostasis. Nature 536, 184–189 (2016).

    CAS  Article  Google Scholar 

  50. 50.

    Zhao, J., Zhai, B., Gygi, S. P. & Goldberg, A. L. mTOR inhibition activates overall protein degradation by the ubiquitin proteasome system as well as by autophagy. Proc. Natl Acad. Sci. USA 112, 15790–15797 (2015).

    CAS  Article  Google Scholar 

  51. 51.

    Dobin, A. et al. STAR: ultrafast universal RNA-Seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Article  Google Scholar 

  52. 52.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  Article  Google Scholar 

  53. 53.

    Zhu, Y., Qiu, P. & Ji, Y. TCGA-assembler: open-source software for retrieving and processing TCGA data. Nat. Methods 11, 599–600 (2014).

    CAS  Article  Google Scholar 

  54. 54.

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  Article  Google Scholar 

  55. 55.

    Ritz, C., Baty, F., Streibig, J. C. & Gerhard, D. Dose-Response analysis using R. PLoS ONE 10, e0146021 (2015).

    Article  Google Scholar 

  56. 56.

    Safikhani, Z. et al. Revisiting inconsistency in large pharmacogenomic studies. F1000Res. 5, 2333 (2016).

    Article  Google Scholar 

  57. 57.

    Wijeratne, E. M. et al. Structure-activity relationships for withanolides as inducers of the cellular heat-shock response. J. Med. Chem. 57, 2851–2863 (2014).

    CAS  Article  Google Scholar 

  58. 58.

    Wang, T., Lander, E. S. & Sabatini, D. M. Viral Packaging and cell culture for CRISPR-based screens. Cold Spring Harb. Protoc. 2016, pdb.prot090811 (2016).

    Article  Google Scholar 

  59. 59.

    Cai, K., Frederick, R. O., Tonelli, M. & Markley, J. L. ISCU(M108I) and ISCU(D39V) differ from wild-type ISCU in Their failure to form cysteine desulfurase complexes containing both frataxin and ferredoxin. Biochemistry 57, 1491–1500 (2018).

    CAS  Article  Google Scholar 

  60. 60.

    Cai, K., Frederick, R. O., Tonelli, M. & Markley, J. L. Interactions of iron-bound frataxin with ISCU and ferredoxin on the cysteine desulfurase complex leading to Fe–S cluster assembly. J. Inorg. Biochem. 183, 107–116 (2018).

    CAS  Article  Google Scholar 

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Acknowledgements

This work is dedicated to the memory of Susan Lindquist who served as a great inspiration as a scientist, mentor and human being. We thank L. Clayton, C. Kayatekin, B. Bevis, N. Kanarek, N. Dharia, V. Viswanathan, J. Eaton, T. Ast, I. Fung, B. Wang and J. McFarland for constructive discussion and comments. Special thanks to D. Sabatini, D. Pincus, J. Rettenmaier and V. Mootha for providing critical comments and reviewing of the manuscript and G. Fink for his supervision. We thank G. Botta, J. Fonseka, J. Roth and S. Bender for help with the PRISM experiments setup and analysis. We thank C. Lewis and the Whitehead Institue Metabolite Profiling Core Facility for the help with the metabolite profiling. We thank the Koch Institute Swanson Biotechnology Center for technical support, specifically J. Cheah for her support conducting the chemical drug screen. NMR spectroscopy was carried out at the National Magnetic Resonance Facility at Madison, which is supported by National Institutes of Health (NIH) grant no. P41GM103399; other work at the University of Wisconsin-Madison was supported by funds from the Biochemistry Department. S.S was supported by K08NS064168 and R01CA194005. P.T was supported by EMBO Fellowship ALTF 739-2011 and by the Charles A. King Trust Postdoctoral Fellowship Program. A.D was supported by the Multiple Myeloma Research Foundation. T.R.G is an HHMI investigator.

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Authors

Contributions

P.T. designed the concept. P.T., A.D., K.C., H.R.K. and M.R. conducted the investigation. P.T., Pr.T., G.K., J.R., M.K. and H.R.K. carried out the formal analysis. Z.B., W.Y., A.T. and N.K. provided the resources. P.T. wrote the original draft. P.T., H.R.K, S.S., L.W., K.C., J.L.M. and T.R.G. reviewed and edited the paper. S.L., J.L.M., I.M.G. and T.R.G. acquired the funding. Supervision was undertaken by S.L., L.W., J.L.M., I.M.G. and T.R.G.

Corresponding authors

Correspondence to Peter Tsvetkov or Todd R. Golub.

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Competing interests

T.R.G. is a consultant in GlaxoSmithKline, Sherlock Biosciences and Foundation medicine; co-founder of Sherlock Biosciences and Foundation medicine. S.S. has consulted for RareCyte Inc. A.T. is a consultant for Tango Therapeutics. W.Y. is employed by Ranok Therapeutics Co. Ltd. and is also the sole proprietor of OnTarget Pharmaceutical Consulting LLC.

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Supplementary information

Supplementary information

Supplementary Figures 1–7

Reporting Summary

Supplementary Dataset 1

The drug sensitivity of cancer cell lines to bortezomib (n = 294) as downloaded from GDSC (www.cancerrxgene.org).

Supplementary Dataset 2

Gene expression of the inducible Lo19S state in the presence or absence of bortezomib (n = 3 biologically independent samples).

Supplementary Dataset 3

Metabolite profiling of controls and Lo19S state cells in the presence or absence of 20 nM bortezomib treatment for 16 h.

Supplementary Dataset 4

Gene expression of MM.1S orthotopic tumors grown out from control and bortezomib-treated mice.

Supplementary Dataset 5

GSEA of genes upregulated in Lo19S but not control tumors was conducted for breast, prostate, thyroid, skin and kidney cancers from the TCGA. Tumor samples as previously described 11.

Supplementary Dataset 6

Viability results from the PRISM experiment where cells were grown in either glucose or galactose containing media with and without bortezomib.

Supplementary Dataset 7

Drug libraries used in the chemical screens.

Supplementary Dataset 8

The cell viability measurements from the drug screens conducted comparing the control versus Lo19S states and the glucose versus galactose media states.

Supplementary Dataset 9

The viability measurements from the PRISM experiment with elesclomol and the overall gene expression and gene deletion associations used in the study.

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Tsvetkov, P., Detappe, A., Cai, K. et al. Mitochondrial metabolism promotes adaptation to proteotoxic stress. Nat Chem Biol 15, 681–689 (2019). https://doi.org/10.1038/s41589-019-0291-9

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