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Mitochondrial metabolism promotes adaptation to proteotoxic stress

Nature Chemical Biology volume 15, pages 681–689 (2019)Cite this article

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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.

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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.

Author information

Author notes

  1. Luke Whitesell

    Present address: Molecular Genetics Department, University of Toronto, Toronto, ON, Canada

  2. Deceased: Susan Lindquist.

Authors and Affiliations

  1. Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Peter Tsvetkov, Guillaume Kugener, Jordan Rossen, Mustafa Kocak, Aviad Tsherniak & Todd R. Golub

  2. Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    Alexandre Detappe, Mairead Reidy & Irene M. Ghobrial

  3. Biochemistry Department, University of Wisconsin-Madison, Madison, WI, USA

    Kai Cai & John L. Markley

  4. Whitehead Institute for Biomedical Research, Cambridge, MA, USA

    Heather R. Keys, Zarina Brune, Prathapan Thiru, Nora Kory, Luke Whitesell & Susan Lindquist

  5. OnTarget Pharmaceutical Consulting LLC, Lexington, MA, USA

    Weiwen Ying

  6. Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Sandro Santagata

  7. Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA

    Sandro Santagata

  8. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Susan Lindquist & Todd R. Golub

  9. Harvard Medical School, Boston, MA, USA

    Todd R. Golub

  10. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Todd R. Golub

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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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