Small-molecule inhibitors of human mitochondrial DNA transcription

Abstract

Altered expression of mitochondrial DNA (mtDNA) occurs in ageing and a range of human pathologies (for example, inborn errors of metabolism, neurodegeneration and cancer). Here we describe first-in-class specific inhibitors of mitochondrial transcription (IMTs) that target the human mitochondrial RNA polymerase (POLRMT), which is essential for biogenesis of the oxidative phosphorylation (OXPHOS) system1,2,3,4,5,6. The IMTs efficiently impair mtDNA transcription in a reconstituted recombinant system and cause a dose-dependent inhibition of mtDNA expression and OXPHOS in cell lines. To verify the cellular target, we performed exome sequencing of mutagenized cells and identified a cluster of amino acid substitutions in POLRMT that cause resistance to IMTs. We obtained a cryo-electron microscopy (cryo-EM) structure of POLRMT bound to an IMT, which further defined the allosteric binding site near the active centre cleft of POLRMT. The growth of cancer cells and the persistence of therapy-resistant cancer stem cells has previously been reported to depend on OXPHOS7,8,9,10,11,12,13,14,15,16,17, and we therefore investigated whether IMTs have anti-tumour effects. Four weeks of oral treatment with an IMT is well-tolerated in mice and does not cause OXPHOS dysfunction or toxicity in normal tissues, despite inducing a strong anti-tumour response in xenografts of human cancer cells. In summary, IMTs provide a potent and specific chemical biology tool to study the role of mtDNA expression in physiology and disease.

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Fig. 1: IMTs affect stability, substrate binding and enzyme activity of POLRMT and inhibit mitochondrial gene expression.
Fig. 2: POLRMT is the in vivo target of IMTs.
Fig. 3: IMTs specifically inhibit proliferation of cancer cells.
Fig. 4: IMT treatment inhibits tumour growth in vivo and mitochondrial gene expression in a dose-dependent manner.

Data availability

All relevant data generated and analysed in this study are included in the Article. Uncropped gels are provided in Supplementary Fig. 1. Publicly available datasets used in this study are the human proteome database (UP000005640, UniProt) and POLRMT structures (PBD codes 5OLA and 4BOC). Further information and requests for unique reagents should be directed to the corresponding authors. Proteomics data and R scripts for analysis have been deposited in PRIDE with the dataset identifier PXD018426. The electron microscopy maps have been deposited with the Electron Microscopy Data Bank (accession code EMD-11679) and the structural model of POLRMT bound to IMT1B has been deposited in the PDB (accession code 7A8P). Source data are provided with this paper.

References

  1. 1.

    Kühl, I. et al. POLRMT does not transcribe nuclear genes. Nature 514, E7–E11 (2014).

    PubMed  Google Scholar 

  2. 2.

    Kühl, I. et al. Transcriptomic and proteomic landscape of mitochondrial dysfunction reveals secondary coenzyme Q deficiency in mammals. eLife 6, e30952 (2017).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Kühl, I. et al. POLRMT regulates the switch between replication primer formation and gene expression of mammalian mtDNA. Sci. Adv. 2, e1600963 (2016).

    ADS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Posse, V. et al. The amino terminal extension of mammalian mitochondrial RNA polymerase ensures promoter specific transcription initiation. Nucleic Acids Res. 42, 3638–3647 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Ringel, R. et al. Structure of human mitochondrial RNA polymerase. Nature 478, 269–273 (2011).

    ADS  CAS  PubMed  Google Scholar 

  6. 6.

    Schwinghammer, K. et al. Structure of human mitochondrial RNA polymerase elongation complex. Nat. Struct. Mol. Biol. 20, 1298–1303 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Birsoy, K. et al. Metabolic determinants of cancer cell sensitivity to glucose limitation and biguanides. Nature 508, 108–112 (2014).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Bosc, C., Selak, M. A. & Sarry, J. E. Resistance is futile: targeting mitochondrial energetics and metabolism to overcome drug resistance in cancer treatment. Cell Metab. 26, 705–707 (2017).

    CAS  PubMed  Google Scholar 

  9. 9.

    Funes, J. M. et al. Transformation of human mesenchymal stem cells increases their dependency on oxidative phosphorylation for energy production. Proc. Natl Acad. Sci. USA 104, 6223–6228 (2007).

    ADS  CAS  PubMed  Google Scholar 

  10. 10.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Lee, J. et al. Effective breast cancer combination therapy targeting BACH1 and mitochondrial metabolism. Nature 568, 254–258 (2019).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Shi, Y. et al. Gboxin is an oxidative phosphorylation inhibitor that targets glioblastoma. Nature 567, 341–346 (2019).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Škrtić, M. et al. Inhibition of mitochondrial translation as a therapeutic strategy for human acute myeloid leukemia. Cancer Cell 20, 674–688 (2011).

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Vander Heiden, M. G. & DeBerardinis, R. J. Understanding the intersections between metabolism and cancer biology. Cell 168, 657–669 (2017).

    PubMed Central  Google Scholar 

  15. 15.

    Viale, A. et al. Oncogene ablation-resistant pancreatic cancer cells depend on mitochondrial function. Nature 514, 628–632 (2014).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Martínez-Reyes, I. et al. Mitochondrial ubiquinol oxidation is necessary for tumour growth. Nature 585, 288–292 (2020).

    PubMed  Google Scholar 

  17. 17.

    Vasan, K., Werner, M. & Chandel, N. S. Mitochondrial metabolism as a target for cancer therapy. Cell Metab. 32, 341–352 (2020).

    CAS  PubMed  Google Scholar 

  18. 18.

    Bergbrede, T., Hoberg, E., Larsson, N. G., Falkenberg, M. & Gustafsson, C. M. An adaptable high-throughput technology enabling the identification of specific transcription modulators. SLAS Discov. 22, 378–386 (2017).

    CAS  PubMed  Google Scholar 

  19. 19.

    Horn, M. et al. Unbiased compound-protein interface mapping and prediction of chemoresistance loci through forward genetics in haploid stem cells. Oncotarget 9, 9838–9851 (2018).

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Hillen, H. S. et al. Mechanism of transcription anti-termination in human mitochondria. Cell 171, 1082–1093 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Hillen, H. S., Morozov, Y. I., Sarfallah, A., Temiakov, D. & Cramer, P. Structural basis of mitochondrial transcription initiation. Cell 171, 1072–1081 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Sriskanthadevan, S. et al. AML cells have low spare reserve capacity in their respiratory chain that renders them susceptible to oxidative metabolic stress. Blood 125, 2120–2130 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Weinberg, F. et al. Mitochondrial metabolism and ROS generation are essential for Kras-mediated tumorigenicity. Proc. Natl Acad. Sci. USA 107, 8788–8793 (2010).

    ADS  CAS  PubMed  Google Scholar 

  24. 24.

    Bralha, F. N. et al. Targeting mitochondrial RNA polymerase in acute myeloid leukemia. Oncotarget 6, 37216–37228 (2015).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Wang, J. et al. Dilated cardiomyopathy and atrioventricular conduction blocks induced by heart-specific inactivation of mitochondrial DNA gene expression. Nat. Genet. 21, 133–137 (1999).

    PubMed  Google Scholar 

  26. 26.

    Sörensen, L. et al. Late-onset corticohippocampal neurodepletion attributable to catastrophic failure of oxidative phosphorylation in MILON mice. J. Neurosci. 21, 8082–8090 (2001).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Viader, A. et al. Schwann cell mitochondrial metabolism supports long-term axonal survival and peripheral nerve function. J. Neurosci. 31, 10128–10140 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Wredenberg, A. et al. Increased mitochondrial mass in mitochondrial myopathy mice. Proc. Natl Acad. Sci. USA 99, 15066–15071 (2002).

    ADS  CAS  PubMed  Google Scholar 

  29. 29.

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

    CAS  PubMed  Google Scholar 

  30. 30.

    Hillen, H. S., Temiakov, D. & Cramer, P. Structural basis of mitochondrial transcription. Nat. Struct. Mol. Biol. 25, 754–765 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Peter, B. et al. Defective mitochondrial protease LonP1 can cause classical mitochondrial disease. Hum. Mol. Genet. 27, 1743–1753 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Falkenberg, M. et al. Mitochondrial transcription factors B1 and B2 activate transcription of human mtDNA. Nat. Genet. 31, 289–294 (2002).

    CAS  PubMed  Google Scholar 

  33. 33.

    Morozov, Y. I. et al. A novel intermediate in transcription initiation by human mitochondrial RNA polymerase. Nucleic Acids Res. 42, 3884–3893 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Vos, S. M., Farnung, L., Urlaub, H. & Cramer, P. Structure of paused transcription complex Pol II–DSIF–NELF. Nature 560, 601–606 (2018).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Vos, S. M., Farnung, L., Linden, A., Urlaub, H. & Cramer, P. Structure of complete Pol II–DSIF–PAF–SPT6 transcription complex reveals RTF1 allosteric activation. Nat. Struct. Mol. Biol. 27, 668–677 (2020).

    CAS  PubMed  Google Scholar 

  36. 36.

    Macao, B. et al. The exonuclease activity of DNA polymerase γ is required for ligation during mitochondrial DNA replication. Nat. Commun. 6, 7303 (2015).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Tegunov, D. & Cramer, P. Real-time cryo-electron microscopy data preprocessing with Warp. Nat. Methods 16, 1146–1152 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).

    CAS  PubMed  Google Scholar 

  39. 39.

    Scheres, S. H. RELION: implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180, 519–530 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Tan, Y. Z. et al. Addressing preferred specimen orientation in single-particle cryo-EM through tilting. Nat. Methods 14, 793–796 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).

    CAS  PubMed  Google Scholar 

  42. 42.

    Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).

    CAS  PubMed  Google Scholar 

  43. 43.

    Ruzzenente, B. et al. LRPPRC is necessary for polyadenylation and coordination of translation of mitochondrial mRNAs. EMBO J. 31, 443–456 (2012).

    CAS  PubMed  Google Scholar 

  44. 44.

    Kauppila, J. H. K. et al. Base-excision repair deficiency alone or combined with increased oxidative stress does not increase mtDNA point mutations in mice. Nucleic Acids Res. 46, 6642–6669 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Busch, J. D. et al. MitoRibo-tag mice provide a tool for in vivo studies of mitoribosome composition. Cell Rep. 29, 1728–1738 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Rappsilber, J., Ishihama, Y. & Mann, M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal. Chem. 75, 663–670 (2003).

    CAS  PubMed  Google Scholar 

  47. 47.

    Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    CAS  Google Scholar 

  48. 48.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2010).

  50. 50.

    Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    CAS  PubMed  Google Scholar 

  51. 51.

    Wong, J. M. et al. Benzoyl chloride derivatization with liquid chromatography-mass spectrometry for targeted metabolomics of neurochemicals in biological samples. J. Chromatogr. A 1446, 78–90 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Schwaiger, M. et al. Anion-exchange chromatography coupled to high-resolution mass spectrometry: a powerful tool for merging targeted and non-targeted metabolomics. Anal. Chem. 89, 7667–7674 (2017).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the Max Planck Society, the Swedish Research Council (2015-00418 to N.-G.L., 2013-3621 to M.F., 2012-2583 to C.M.G.), Swedish Cancer Foundation (to M.F., C.M.G. and N.-G.L.), the Knut and Alice Wallenberg foundation (to M.F., C.M.G. and N.-G.L.), the Deutsche Forschungsgemeinschaft (SFB1218/A06 to N.-G.L.), European Research Council (Advanced Grant 2016-741366 to N.-G.L.) and grants from the Swedish state under the agreement between the Swedish government and the county councils, the ALF agreement to M.F. (ALFGBG-727491), C.M.G. (ALFGBG-728151) and N.-G.L. (SLL2018.0471). P.C. was supported by the Deutsche Forschungsgemeinschaft (SFB860, SPP1935), the European Research Council (advanced investigator grant TRANSREGULON, grant agreement no. 693023), the Volkswagen Foundation and Germany’s Excellence Strategy (EXC 2067/1- 390729940). H.S.H. was supported by the Deutsche Forschungsgemeinschaft (FOR2848). We thank N. Hochhard and S. Miethe for technical assistance; M. del Pilar Miranda for assistance; T. Colby, A. Taha, Y. Hinze, S. Perin and C. Edlich-Muth for support; I. Ivanov at Oncolead for providing cell panel data; C. Dienemann for assistance with cryo-EM data collection and processing; and Y. Chen for providing purified RNA polymerase II.

Author information

Affiliations

Authors

Contributions

M.F., C.M.G. and N.-G.L. conceptualized the original IMT idea. M.F., C.M.G., N.-G.L., B.K., N.A.B. and B.P. conceptualized research goals and experiments. N.A.B. and B.P. planned, performed and analysed the majority of the experiments. N.A.B. conducted and analysed qPCR and western blotting experiments in tissue culture (IMT1, IMT1B and con IMT) and xenografts, performed and analysed siRNA transfection experiments, cell proliferation assays, ROS measurement, metabolomics, quantitative proteomics and in organello translation assays. B.P. performed and analysed differential scanning fluorimetry, microscale thermophoresis analysis and in vitro transcription assays (IMT1, IMT1B and con IMT) and expressed mutant POLRMT constructs. H.S.H. designed and carried out cryo-EM experiments and RNA Pol II primer extension assays. A.F. planned, performed and analysed qPCR, western blotting and respiration measurements using IMT1. T.B. designed the HTRF-based POLRMT detection method and analysed experiments in the course of IMT development. A.C. and P.H. planned, performed and analysed cell proliferation assays. M.H. planned, performed and analysed the forward genetic screens and generated A2780 mutant cell lines. A.U. designed and analysed toxicology, pharmacology and efficacy data. R.L. designed IMT compounds and synthesis routes, analysed data and devised strategies to elaborate the structure–activity relationship of inhibitors. I.A. and X.L. performed, analysed and interpreted quantitative proteomics data. P.G. performed, analysed and interpreted metabolomics data. U.K. carried out structural modelling of human POLRMT, docking of compounds and aided in cluster analysis of inhibitors. S.M. and J.B. designed and carried out qPCR and western blot experiments in the course of IMT development. M.S.D. planned and interpreted the forward genetic screens. P.N. aided in compound and synthesis design. P.C. designed and supervised cryo-EM experiments and RNA Pol II primer extension assays. M.F. planned, performed and analysed in vitro assays on RPO41, T7 RNA Pol, E. coli RNA Pol, RevertAid reverse transcriptase and POLG. N.A.B., B.P., C.M.G. and N.-G.L. wrote the manuscript, with input from all authors.

Corresponding authors

Correspondence to Claes M. Gustafsson or Nils-Göran Larsson.

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Competing interests

The IMT compound intellectual property is published as WO 2019/057821 and has been licensed by the Max Planck Society and the Lead Discovery Center GmbH.

Additional information

Peer review information Nature thanks Navdeep Chandel, Angela Koehler and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data figures and tables

Extended Data Fig. 1 IMT1 affects POLRMT stability and DNA binding.

a, Overview of the high-throughput screening procedure for IMTs. b, Chemical structure of IMT1 (LDC195943). c, Differential scanning fluorimetry performed in the presence IMT1. The shift in melting temperature (ΔTm) is plotted against the IMT1 concentration (n = 2 independent experiments). d, Template used in the single ribonucleotide incorporation and MST assays. NT2, non-template strand; TS2, template strand; R14, 14-mer RNA. e, Representative gels used for the determination of Michaelis–Menten kinetics in Fig. 1c. The P32-labelled 14-mer scaffold and the extended 15-mer product are indicated. A representative image of n = 3 independent experiments is shown. f, Residuals of the fits to the steady-state POLRMT kinetics data. g, Single nucleotide incorporation at varying substrate concentrations in the presence of 0–10 μM IMT1 (mean values of n = 2 independent experiments). h, In vitro promoter-dependent transcription on linear LSP and supercoiled circular LSP templates in the presence of 0–10 μM IMT1B. A representative image of n = 2 independent experiments is shown. Quantification is shown in Fig. 1d. i, A concentration-dependent decrease in binding affinity (Kd) was observed between POLRMT and a RNA–DNA scaffold at increasing levels of IMT1B (mean ± s.d., n = 3 independent experiments). j, Raw MST traces. Source data

Extended Data Fig. 2 IMT1 affects mitochondrial gene expression.

a, Mitochondrial transcript levels of ND1 after 6 h and 4 d of IMT1 treatment (mean ± s.e.m., n = 3–4 biological replicates, two-way ANOVA, Sidak’s multiple comparisons test, DMSO–IMT1). b, mtDNA levels after IMT1 treatment (mean ± s.e.m., n = 3 (144 h, 192 h), 4 (4 h, 6 h, 96 h), 6 (24 h, 48 h, 72 h) or 8 (DMSO) biological replicates). c, Immunoblot analysis of OXPHOS protein levels after IMT1 treatment (30 μg per lane). A representative image of n = 3 independent experiments is shown. d, Quantitative proteomics of IMT1 treatment over time (n = 5 biological replicates). Within each time point, IMT-treated samples were compared to control samples (DMSO). Changes in the protein levels of OXPHOS complexes, mitoribosomal subunits and cytosolic ribosomes are plotted as log2-transformed change in abundance relative to control over time. The exact values for median, upper and lower box hinge and upper and lower whiskers are shown in Supplementary Table 6 (compound IMT1). e, Intact cell respiration in HeLa cells after IMT1 exposure. Basal respiration, leak respiration and maximum respiration was determined (mean ± s.e.m.; n = 4 independent measurements, two-way ANOVA, Sidak’s multiple comparisons test, respective DMSO control–IMT1). f, Comparison of ND1 transcript levels after IMT1 (Extended Data Fig. 2a) and IMT1B (Fig. 1f) treatment in HeLa and A2780 cells. Dashed lines indicate ND1 transcript levels in DMSO-treated controls. Source data

Extended Data Fig. 3 IMTs are highly specific.

ac, Single ribonucleotide incorporation assay using the template displayed in Extended Data Fig. 1d. IMT1B inhibits POLRMT, but does not affect RPO41 (a), T7 RNA polymerase (b) or E. coli RNA polymerase (c). The first lane in each panel is template only, without added enzyme. Representative images from n = 3 independent experiments are shown. d, IMT1B does not inhibit cDNA synthesis by RevertAid reverse transcriptase. Reverse transcriptase was omitted in the second lane. A representative image from n = 3 independent experiments is shown. e, Primer extension assay of POLRMT and RNA polymerase II performed in the presence of increasing concentrations of IMT1B. A representative image from n = 3 independent experiments is shown. f, IMT treatment does not affect the expression of nuclear RNA polymerase targets. HeLa and A2780 cells were treated with either DMSO or IMT1, IMT1B and a commercially available IMT (con IMT) used as a control. mRNA levels of polymerase targets (RNA Pol I: 18S, 28S; RNA Pol II: POLRMT, β-actin; RNA Pol III: 5S; POLRMT: ND1, CYTB, COX1) are given as mean ± s.e.m. (n = 4 (con IMT) or 5 (IMT1, IMT1B) biological replicates, two-way ANOVA, Sidak’s multiple comparisons test, DMSO–respective IMT treatment). g, IMT1B does not inhibit POLγ primer elongation. The input template for POLγ is 3,800-nt single-stranded DNA that becomes a 3,800-bp nicked circular template product when synthesized. A representative image from n = 3 independent experiments is shown. First lane, ddCTP control. h, In organello translation of freshly isolated mitochondria in the presence or absence of IMT1B. A representative image from n = 2 biological replicates is shown. i, Chemical structure of con IMT (SC-6238532), a structurally related analogue of IMTs. j, Differential scanning fluorimetry performed in the presence or absence of con IMT (n = 2 independent experiments). k, In vitro promoter-dependent transcription on linear LSP and supercoiled circular LSP templates was performed in the presence of 0–10 μM con IMT. A representative image from n = 2 independent experiments is shown. R/O, run-off transcript. l, Quantification of k. Mean values of n = 2 independent experiments are shown. m, Mitochondrial transcript levels of ND1 and CYTB determined after treatment of HeLa cells with different concentrations of con IMT for 6 h (mean ± s.e.m., n = 3, 4 (0.1 μM) or 5 (DMSO, 10 μM, 5 μM) biological replicates). n, mtDNA levels in HeLa cells treated with con IMT (mean ± s.e.m., n = 3, 4 (72 h) or 5 (DMSO) biological replicates). o, Immunoblot analysis of OXPHOS protein levels in HeLa cells treated with Con IMT for 4 d (20 μg per lane). A representative image from n = 2 independent experiments is shown. p, Quantitative proteomics of con IMT treatment over time (n = 5 biological replicates). Within each time point, treated samples were compared to control samples (DMSO). Changes in the protein levels of OXPHOS complexes, mitoribosomal subunits and cytosolic ribosomes are plotted as log2-transformed change in abundance relative to control over time. The exact values for median, upper and lower box hinge and upper and lower whiskers are shown in Supplementary Table 6 (compound con IMT). Source data

Extended Data Fig. 4 Identification of the IMT-binding pocket.

a, Schematic of the experimental workflow of the IMT-resistance screen using chemical mutagenesis. b, In vitro promoter-dependent transcription using the IMT-resistant L816Q and A821V POLRMT mutants in the presence of 0–10 μM IMT1B. A representative image from n = 2 independent experiments is shown. c, Quantification of b. Mean values of n = 2 independent experiments are shown. d, Inhibition of transcription initiation on IMT-resistant POLRMT mutants. In vitro transcription initiation was performed on linear LSP templates using wild-type and IMT-resistant POLRMT protein in the presence or absence of IMT1. A representative image from n = 2 independent experiments is shown. e, Increasing amounts of POLRMT do not rescue wild-type activity in the presence of IMT1B. In vitro promoter-dependent transcription on supercoiled circular LSP templates was performed in the presence of 10 μM IMT1B using increasing concentrations of wild-type or mutant POLRMT (L816Q or A821V). A representative image from n = 2 independent experiments is shown. f, Quantification of e. Mean values of n = 2 independent experiments are shown. g, Sanger sequencing of CRISPR–Cas9-engineered POLRMT mutations introduced into A2780 cells. h, Mitochondrial gene expression in POLRMT mutant cell lines (L796Q and L816Q, # denotes two independent clones). Data are expressed as fold of untreated wild-type control (DMSO) and given as mean ± s.e.m. (n = 7–9 biological replicates; two-way ANOVA, Sidak’s multiple comparisons test, DMSO–IMT1). Source data

Extended Data Fig. 5 Structure determination of POLRMT with IMT1B by cryo-EM.

a, Representative micrograph from 40° tilted data collection (n = 2,077 micrographs). b, Most populated classes from 2D classification using cryoSPARC 6. c, Cryo-EM processing workflow. d, Binding pocket of IMT1B between the POLRMT thumb and palm domains. POLRMT is shown as a translucent cartoon, and residues within 4 Å of IMT1B are shown as sticks. IMT1B was modelled on the basis of the cryo-EM density and chemical considerations (Methods). e, Fourier shell correlation plot. f, Angular distribution of particle views. g, Local resolution estimation calculated using RELION 3. The position of IMT1B is indicated with a red dashed box. h, Cryo-EM density around IMT1B. IMT1B is shown as sticks, and the cryo-EM density is shown as blue mesh with a carve of 2 Å.

Extended Data Fig. 6 Loss of POLRMT leads to a decrease in cell viability.

a, POLRMT silencing in HeLa cells affects cell viability. Cell viability was assessed using a ViCell Cell counter. Mean of n = 2 independent experiments is shown. NT, non-transfected. b, Immunoblot analysis of POLRMT levels 4 d after transfection (20 μg per lane). A representative image of n = 2 independent experiments is shown. c, Analysis of cancer cell proliferation using an SRB proliferation assay indicates that IMT1 treatment specifically inhibits cancer cell proliferation in a variety of cancer cell lines, but not in control cells. IC50 values of IMT1 on a panel of 89 cancer cell lines (grey) and primary cells (black, IMR90 lung fibroblasts and human PBMCs) are plotted. d, Cell growth in HeLa cells (IC50 = 0.838 μM), lung carcinoma cells (A594) (IC50 = 0.643 μM) and IMR90 lung fibroblast cells (IC50 > 30 μM) after IMT1 treatment was assessed using an SRB assay. e, Cytotoxicity against human (h)PBMCs (two different donors, left) or primary human hepatocytes (right) was assessed using the CellTiter-Glo assay at the indicated inhibitor concentrations (n = 2 independent experiments). Source data

Extended Data Fig. 7 Basis of IMT-induced cellular toxicity.

a, ROS levels after IMT1 treatment in A2780 cells were determined using the Cellular ROS assay kit Orange (mean ± s.e.m., n = 5 biological replicates). b, Immunoblot analysis of the apoptosis marker cleaved PARP. A2780 cells were treated with IMT1 for the indicated time points and collected by trypsin treatment (attached). For later time points, cell culture supernatants were collected and detached or dead cells were isolated by centrifugation (supernatant) (20 μg per lane). A representative image of n = 3 independent experiments is shown. c, Heat maps illustrating the fold-change in protein levels in apoptosis (top, left), one-carbon pathway (bottom, left), degradation and stress response (right). d, Overview of changes in central carbon metabolism after IMT1 treatment. Changes in metabolite levels are given as fold of control at the indicated time points. Dark blue, minimum (0); dark red, maximum (2). e, Immunoblot analysis of the proliferation marker PCNA. A2780 cells were treated with IMT1B (+) or DMSO (−) for the indicated time points (20 μg per lane). A representative image of n = 2 independent experiments is shown. Source data

Extended Data Fig. 8 IMT treatment in vivo.

a, b, Pharmacokinetic (PK) parameters of IMT1B used in the in vivo studies: intravenous dosing (a) or oral dosing (b) (n = 3 biological replicates). t1/2, elimination half-life; Tmax, time to reach peak plasma concentration following drug administration (that is, Cmax); C0, concentration extrapolated to the origin; Cmax, peak plasma drug concentration; AUC 0–inf, area under the plasma concentration time curve from time zero to infinity; Vz, volume of distribution; CL, total plasma clearance; F, bioavailability. c, Mouse POLRMT is stabilized by IMT1B. Differential scanning fluorimetry performed in the presence or absence of IMT1B using human (left) or mouse (right) POLRMT (n = 2 independent experiments). The determined melting temperatures are given. d, IMT1B inhibits promotor-dependent transcription using mouse POLRMT in a concentration-dependent manner in vitro. In vitro promoter-dependent transcription on linear LSP and supercoiled circular LSP templates was performed in the presence of 0–10 μM IMT1B. A representative image of n = 2 independent experiments is shown. e, A2780 xenograft tumour growth in vivo (study 2). Mice were treated once per day with either vehicle (veh) or IMT1B (100 mg kg−1). Mean ± s.e.m.; n = 8 biological replicates, Mann–Whitney U test, exact significance, one-tailed, vehicle–IMT1B). f, IMT1B treatment inhibits DLD-1 xenograft tumour growth in vivo. Mice were treated once per day with either vehicle or IMT1B (100 mg kg−1). Mean ± s.e.m., n = 8 biological replicates, Mann–Whitney U test, exact significance, one-tailed, vehicle–IMT1B. g, Mean body weight of IMT1B-treated mice in comparison to vehicle-treated controls in the A2780 xenografts. Mean ± s.e.m., n = 8 biological replicates. h, IMT1B treatment induces no acute liver or kidney toxicity after four weeks of treatment. NMRI mice were treated with either vehicle or IMT1B for four weeks orally once per day. Blood was taken after fasting at the first day (baseline) and last day (4 weeks IMT) of treatment from the tail vein. Blood parameters analysed include alanine transaminase levels (ALT, in U l−1) and aspartate aminotransferase (AST, in U l−1) to determine liver toxicity as well as creatinine (CREA, in mg dl−1) to determine kidney toxicity. Data are presented as mean ± s.e.m., n = 4 biological replicates. i, IMT1B treatment does not affect blood count after IMT treatment. Female NMRI mice were treated with either vehicle or IMT1B for four weeks orally once per day. Blood was taken on the last day of the experiments and analysed for the abovementioned parameters. Data are presented as mean + s.e.m., n = 4 biological replicates. MCHC, mean corpuscular haemoglobin concentration; MCH, mean corpuscular haemoglobin; MCV, mean corpuscular volume. j, Mitochondrial DNA levels in tumour, liver and heart (mean ± s.e.m., n = 8 biological replicates). k, Immunoblot analysis of phosphorylated AMPK and ribosomal S6 protein levels in tumour tissue lysates. For determination of total AMPK levels, samples were run on a separate gel and processed in parallel (20 μg per lane). A representative image of n = 3 independent experiments is shown. Source data

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Supplementary Information

This file contains Supplementary Figure 1: Uncropped gels of Figures 1, 3–4 and Extended Data Figures 1–4, 6-8, Supplementary Methods: Detail description of the synthesis procedure and quality control for IMTs and Supplementary Tables 1-6.

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Bonekamp, N.A., Peter, B., Hillen, H.S. et al. Small-molecule inhibitors of human mitochondrial DNA transcription. Nature 588, 712–716 (2020). https://doi.org/10.1038/s41586-020-03048-z

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