Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Velcrin-induced selective cleavage of tRNALeu(TAA) by SLFN12 causes cancer cell death

Abstract

Velcrin compounds kill cancer cells expressing high levels of phosphodiesterase 3A (PDE3A) and Schlafen family member 12 (SLFN12) by inducing complex formation between these two proteins, but the mechanism of cancer cell killing by the PDE3A–SLFN12 complex is not fully understood. Here, we report that the physiological substrate of SLFN12 RNase is tRNALeu(TAA). SLFN12 selectively digests tRNALeu(TAA), and velcrin treatment promotes the cleavage of tRNALeu(TAA) by inducing PDE3A–SLFN12 complex formation in vitro. We found that distinct sequences in the variable loop and acceptor stem of tRNALeu(TAA) are required for substrate digestion. Velcrin treatment of sensitive cells results in downregulation of tRNALeu(TAA), ribosome pausing at Leu-TTA codons and global inhibition of protein synthesis. Velcrin-induced cleavage of tRNALeu(TAA) by SLFN12 and the concomitant global inhibition of protein synthesis thus define a new mechanism of apoptosis initiation.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Downregulation of tRNALeu(TAA) following DNMDP treatment.
Fig. 2: SLFN12 cleaves the 3′ acceptor stem of tRNALeu(TAA) and SLFN12–PDE3A complex formation upregulates SLFN12 RNase activity.
Fig. 3: SLFN12 preferentially digests tRNALeu(TAA) in vitro and this selective cleavage requires distinct sequences in the variable loop and the acceptor stem regions.
Fig. 4: SLFN12 RNase activation by DNMDP causes ribosomal pausing at TTA codons.
Fig. 5: DNMDP treatment or ectopic expression of SLFN12 inhibits global translation in sensitive cancer cell lines.
Fig. 6: Expression of leucine tRNA resistant to SLFN12 RNase rescues cells from DNMDP-mediated apoptosis.

Similar content being viewed by others

Data availability

Ribosome profiling, RNA-seq and tRNA-seq datasets were deposited to the GEO database (GSE185828). Analyzed ribosome profiling data can be found in Supplementary Data 13. Raw data, original images and PDB files are provided in Source data and Supplementary Data 4. The reads from tRNA sequencing were mapped using GtRNAdb (http://gtrnadb.ucsc.edu/). Source data are provided with this paper.

References

  1. de Waal, L. et al. Identification of cancer-cytotoxic modulators of PDE3A by predictive chemogenomics. Nat. Chem. Biol. 12, 102–108 (2016).

    Article  PubMed  Google Scholar 

  2. Garvie, C. W. et al. Structure of PDE3A–SLFN12 complex reveals requirements for activation of SLFN12 RNase. Nat. Commun. 12, 4375 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wu, X. et al. Mechanistic insights into cancer cell killing through interaction of phosphodiesterase 3A and Schlafen family member 12. J. Biol. Chem. 295, 3431–3446 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ai, Y. et al. An alkaloid initiates phosphodiesterase 3A-Schlafen 12 dependent apoptosis without affecting the phosphodiesterase activity. Nat. Commun. 11, 3236 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. An, R. et al. PDE3A inhibitor anagrelide activates death signaling pathway genes and synergizes with cell death-inducing cytokines to selectively inhibit cancer cell growth. Am. J. Cancer Res. 9, 1905–1921 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Lewis, T. A. et al. Optimization of PDE3A modulators for SLFN12-dependent cancer cell killing. ACS Med. Chem. Lett. 10, 1537–1542 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Nazir, M. et al. Targeting tumor cells based on phosphodiesterase 3A expression. Exp. Cell. Res. 361, 308–315 (2017).

    Article  CAS  PubMed  Google Scholar 

  8. Corsello, S. M. et al. Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling. Nat. Cancer 1, 235–248 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Li, D. et al. Estrogen-related hormones induce apoptosis by stabilizing Schlafen-12 protein turnover. Mol. Cell 75, 1103–1116 (2019).

    Article  CAS  PubMed  Google Scholar 

  10. Chen, J. et al. Structure of PDE3A–SLFN12 complex and structure-based design for a potent apoptosis inducer of tumor cells. Nat. Commun. 12, 6204 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  11. de la Casa-Esperon, E. From mammals to viruses: the Schlafen genes in developmental, proliferative and immune processes. Biomol. Concepts 2, 159–169 (2011).

    Article  PubMed  Google Scholar 

  12. Puck, A. et al. Expression and regulation of Schlafen (SLFN) family members in primary human monocytes, monocyte-derived dendritic cells and T cells. Results Immunol. 5, 23–32 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Li, M. et al. DNA damage-induced cell death relies on SLFN11-dependent cleavage of distinct type II tRNAs. Nat. Struct. Mol. Biol. 25, 1047–1058 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Pisareva, V. P., Muslimov, I. A., Tcherepanov, A. & Pisarev, A. V. Characterization of novel ribosome-associated endoribonuclease SLFN14 from rabbit reticulocytes. Biochemistry 54, 3286–3301 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. Yang, J. Y. et al. Structure of Schlafen13 reveals a new class of tRNA/rRNA-targeting RNase engaged in translational control. Nat. Commun. 9, 1165 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Metzner, F. J., Huber, E., Hopfner, K. P. & Lammens, K. Structural and biochemical characterization of human Schlafen 5. Nucleic Acids Res. 50, 1147–1161 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Wilson, D. N. & Doudna Cate, J. H. The structure and function of the eukaryotic ribosome. Cold Spring Harb. Perspect. 4, a011536 (2012).

    Google Scholar 

  18. Gogakos, T. et al. Characterizing expression and processing of precursor and mature human tRNAs by hydro-tRNAseq and PAR-CLIP. Cell Rep. 20, 1463–1475 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Pan, T. Modifications and functional genomics of human transfer RNA. Cell Res. 28, 395–404 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. van Zundert, G. C. P. et al. The HADDOCK2.2 web server: user-friendly integrative modeling of biomolecular complexes. J. Mol. Biol. 428, 720–725 (2016).

    Article  PubMed  Google Scholar 

  21. Hein, C. D., Liu, X. M. & Wang, D. Click chemistry, a powerful tool for pharmaceutical sciences. Pharm. Res. 25, 2216–2230 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Iordanov, M. S. et al. Ribotoxic stress response: activation of the stress-activated protein kinase JNK1 by inhibitors of the peptidyl transferase reaction and by sequence-specific RNA damage to the α-sarcin/ricin loop in the 28S rRNA. Mol. Cell. Biol. 17, 3373–3381 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wu, C. C., Peterson, A., Zinshteyn, B., Regot, S. & Green, R. Ribosome collisions trigger general stress responses to regulate cell fate. Cell 182, 404–416 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ivanov, P., Emara, M. M., Villen, J., Gygi, S. P. & Anderson, P. Angiogenin-induced tRNA fragments inhibit translation initiation. Mol. Cell 43, 613–623 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zoppoli, G. et al. Putative DNA/RNA helicase Schlafen-11 (SLFN11) sensitizes cancer cells to DNA-damaging agents. Proc. Natl Acad. Sci. USA 109, 15030–15035 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Murai, J. et al. SLFN11 blocks stressed replication forks independently of ATR. Mol. Cell 69, 371–384 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Malone, D., Lardelli, R. M., Li, M. & David, M. Dephosphorylation activates the interferon-stimulated Schlafen family member 11 in the DNA damage response. J. Biol. Chem. 294, 14674–14685 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Yan, B. et al. Multiple PDE3A modulators act as molecular glues promoting PDE3A–SLFN12 interaction and induce SLFN12 dephosphorylation and cell death. Cell Chem. Biol. 29, 958–969 (2022).

    Article  CAS  PubMed  Google Scholar 

  30. Katsoulidis, E. et al. Role of interferon α (IFN α)-inducible Schlafen-5 in regulation of anchorage-independent growth and invasion of malignant melanoma cells. J. Biol. Chem. 285, 40333–40341 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kane, M. et al. Identification of interferon-stimulated genes with antiretroviral activity. Cell Host Microbe 20, 392–405 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kim, E. T. et al. Comparative proteomics identifies Schlafen 5 (SLFN5) as a herpes simplex virus restriction factor that suppresses viral transcription. Nat. Microbiol. 6, 234–245 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Li, M. et al. Codon-usage-based inhibition of HIV protein synthesis by human Schlafen 11. Nature 491, 125–128 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Seong, R. K. et al. Schlafen 14 (SLFN14) is a novel antiviral factor involved in the control of viral replication. Immunobiology 222, 979–988 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Chan, P. P. & Lowe, T. M. GtRNAdb 2.0: an expanded database of transfer RNA genes identified in complete and draft genomes. Nucleic Acids Res. 44, D184–D189 (2016).

    Article  CAS  PubMed  Google Scholar 

  36. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Liu, Q., Shvarts, T., Sliz, P. & Gregory, R. I. RiboToolkit: an integrated platform for analysis and annotation of ribosome profiling data to decode mRNA translation at codon resolution. Nucleic Acids Res. 48, W218–W229 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Kumari, R., Michel, A. M. & Baranov, P. V. PausePred and Rfeet: webtools for inferring ribosome pauses and visualizing footprint density from ribosome profiling data. RNA 24, 1297–1304 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Zuker, M. & Stiegler, P. Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res. 9, 133–148 (1981).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Bailey, T. L., Johnson, J., Grant, C. E. & Noble, W. S. The MEME suite. Nucleic Acids Res. 43, W39–W49 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Kwon, N. H. et al. Transfer-RNA-mediated enhancement of ribosomal proteins S6 kinases signaling for cell proliferation. RNA Biol. 15, 635–648 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Tamaki, S., Tomita, M., Suzuki, H. & Kanai, A. Systematic analysis of the binding surfaces between tRNAs and their respective aminoacyl tRNA synthetase based on structural and evolutionary data. Front. Genet. 8, 227 (2017).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work is supported by Bayer A.G. (S.L., S.H., X.W., C.G., J.M., A.C., M.M., H.G.), National Cancer Institute grant R35 CA197568 (to M.M.) and a Broad Institute NextGen award (to H.G.). We thank S. Goldoni and H. Faus for helpful discussions.

Author information

Authors and Affiliations

Authors

Contributions

The project was conceptualized by S.L., X.W., M.M. and H.G. All experiments were designed and performed by S.L. and J.M. S.H. and A.D.C. analyzed the Ribo-seq data. C.G. prepared the purified SLFN12 proteins. M.T. and X.W. made the SLFN12-knockout cell lines. M.S. performed the computational analysis of the SLFN12–tRNA structure. M.G.R. analyzed the correlation between SLFN11 expression and DNMDP response. M.M. provided key input on several experiments. H.G. assisted with experimental design, supervised the experiments and led the project. The manuscript was written by S.L. and H.G. with input from all authors.

Corresponding author

Correspondence to Heidi Greulich.

Ethics declarations

Competing interests

All authors received research funding from Bayer A.G. M.M. also received research funding from Janssen and Ono; consulting fees from Bayer, Interline and Isabl; royalties from LabCorp; patents relevant to abstract presentation; and additional patents not relevant to research funding. In addition, X.W., M.M. and H.G. receive an inventor’s share of license revenue as part of their employment for certain patent filings, including US-2016-0016913 and US-2018-0235961, which relate to aspects of the work described in this manuscript. The co-owners of those patent filings are The Broad Institute, Inc., Dana-Farber Cancer Institute, Inc. and Bayer Pharma A.G.

Peer review

Peer review information

Nature Chemical Biology thanks Song Gao, Brian Liau and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Down-regulation of tRNA-Leu-TAA upon DNMDP treatment.

(a) The effect of DNMDP on ribosomal RNA integrity. Three velcrin-sensitive cell lines (HeLa, A2058, and SKMEL3) and insensitive SLFN12-depleted HeLa were treated with 3 µM DNMDP for the indicated time. SLFN12-depleted HeLa cells were generated by prolonged incubation with DNMDP2, and serve as a negative control in this experiment. Total RNA purified from these cell lines was analyzed on a 6% urea-polyacrylamide gel with RNA ladders (n = 4 replicates for HeLa and n = 2 for others). (b) Leucine tRNA abundance in a panel of cancer cell lines treated with DMSO or 1 mM DNMDP3. After demethylation of tRNA, cDNA was synthesized and leucine tRNA levels were measured using qPCR. tRNA-Leu-TAA-1 level is plotted relative to tRNA-Leu-TAG-2 as a control (n = 3 replicates per condition). Data are represented as mean ± SEM. P values were calculated by two-tailed unpaired Student’s t test. not significant (ns). Sensitive cell lines are arranged left to right in order of greatest to least DNMDP response3, from HeLa at the top left to CAL51 at the bottom right.

Source data

Extended Data Fig. 2 SLFN12 induces down-regulation of tRNA-Leu-TAA.

(a-c) Average intact tRNA-Leu-TAA-1 abundance measured by qPCR (n = 3 replicates) was plotted against DNMDP response3, (a) PDE3A protein expression3, and (c) SLFN12 mRNA levels (b) from the DepMap dataset (https://depmap.org/portal/). P-values were calculated by linear regression model (a-c). (c) The A549 cell line lacking expression of SLFN12 and the UACC257 cell line lacking expression of AIP3 were excluded in Extended Data Fig. 1b. (d) Leucine tRNA abundance in SLFN12 knockout cell lines treated with DMSO or 1 µM DNMDP was measured by qPCR. The level of tRNA-Leu-TAA-1 was plotted relative to control tRNA, tRNA-Leu-TAG-2 (n = 3 replicates per condition). Data are represented as mean ± SEM. P values were calculated by two-tailed unpaired Student’s t test. (e) Western blots of SLFN12 and GAPDH in wild-type and SLFN12 knockout cell lines treated with 1 µM DNMDP for 18 hours. The SLFN12 band is marked with an arrow to distinguish it from the upper nonspecific band (n = 2 replicates).

Source data

Extended Data Fig. 3 The effect of SLFN11 expression on DNMDP response.

(a) Correlation between DNMDP response1 and expression of 19217 genes from DepMap dataset (https://depmap.org/portal/) in 674 cell lines. SLFN11, SLFN12 and PDE3A correlation coefficients are indicated with green, red and blue, respectively (center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; points, outliers). (b) In 14 cancer cell lines, SLFN11 mRNA level was plotted against DNMDP response3. (c) Immunoblots of the SLFN11 and GAPDH in the indicated cancer cell lines (n = 2 replicates). (d) The effects of SLFN11 expression on DNMDP response. Untransduced HeLa cells or HeLa cells stably expressing SLFN11 were treated with the indicated DNMDP concentrations. Viable cells were measured by CellTiter-Glo (n = 2 replicates). The protein expression of SLFN11 and GAPDH was measured by immunoblotting in untransduced HeLa cells or HeLa cells stably expressing SLFN11 (n = 2 replicates). (e) HeLa, A2058, and IGR37 cells were transfected with SLFN11 siRNA or control siRNA. Equal numbers of cells were treated with 0.5 µM DNMDP for 2 days after transfection. Cell viability was assessed by CellTiter-Glo (n = 3 replicates). Data are represented as mean ± SEM. P-values were calculated by a two-tailed unpaired Student’s t test (ns, not significant). The protein expression of SLFN11 and GAPDH was measured in IGR37 (data are representative of n = 2 replicates). HeLa and A2058 cells express no SLFN11 as shown in (c) and were therefore not included in the knockdown immunoblot. (f) Leucine tRNA abundance in HeLa cells treated with DMSO, 25 μM cisplatin or 0.1 μM camptothecin (CPT) for 48 hours was measured by qPCR. tRNA-Leu-TAA-1 levels are plotted relative to control tRNA, tRNA-Leu-TAG-2 (n = 3 replicates). Data are represented as mean ± SEM. Statistical significance were calculated by two-tailed unpaired Student’s t test (ns, not significant).

Source data

Extended Data Fig. 4 SLFN12 selectively digests tRNA-Leu-TAA in vitro.

(a) Indicated tRNAs were synthesized using a T7 RNA polymerase-mediated transcription reaction. Synthetic tRNAs were treated with the indicated concentrations of SLFN12 at 37 °C for 40 minutes (n = 2 replicates for Leu-TAA-1, Leu-TAA-3, Ser-CGA and Cys-GCA; n = 3 replicates for others). (b) 0.25 µM PDE3A and SLFN12 proteins pre-treated with 1.25 µM DNMDP were incubated with synthetic tRNAs at 37 °C for 40 minutes (n = 3 replicates for Leu-TAA-1 and Leu-TAA-3; n = 2 replicates for others). (c) 0.5 µM ΔSLFN12 (amino acids 1–347) or PDE3A proteins pre-treated with DMSO, 2.5 µM DNMDP, 2.5 µM trequinsin or 2.5 µM estradiol were incubated with synthetic tRNAs at 37 °C for 40 minutes. After incubation, RNA samples were analyzed on a 15% denaturing polyacrylamide gel (n = 2 replicates). Representative figures are shown here. Intact tRNA is indicated with an arrow. The relative amounts of intact tRNA were quantified using ImageJ software and are shown at the bottom. (d) Leucine tRNA abundance was measured by qPCR in purified total RNA treated with 0.25 μM SLFN12 recombinant protein. tRNA-Leu-TAA-1 levels are plotted relative to control tRNA, tRNA-Leu-TAG-2 (n = 3 replicates). Data are represented as mean ± SEM. Statistical significance was calculated by a two-tailed unpaired Student’s t test.

Source data

Extended Data Fig. 5 Leucine and serine tRNA sequences are conserved except in the acceptor and variable loop regions.

(a) Sequences of the indicated tRNAs were aligned in R using the msa package. The consensus logo is shown at the top of the figure, with more conserved positions appearing larger. Positions where the sequences do not agree are blank in the consensus. Structural regions of the tRNA are labeled on the bottom of the alignment. (b) The computational model of the structure of SLFN12 bound to tRNA was generated using the HADDOCK software. Both the catalytic SLFN12 monomer (green) and the adaptor SLFN12 monomer (magenta) interact with a single molecule of tRNA-Leu-TAA-3 (orange). The catalytic SLFN12 monomer interacts with the tRNA variable loop region (yellow) and the adaptor SLFN12 monomer interacts with the tRNA acceptor stem (cyan). The active site residues of the catalytic SLFN12 monomer are close to the primary tRNA cleavage site in this model.

Extended Data Fig. 6 Ribo-seq analysis.

(a) Distribution of read lengths of ribosome-protected fragments (RPF) upon treatment with DMSO (gray) or DNMDP (green). (b) RPF read densities near the translation initiation site (left) and translation termination site (right). (c) Scatter plot showing A-site ribosome occupancy at individual codons in DMSO or DNMDP treated cells. The TTA codon is indicated with a red dot; all other codons are grey. (d-e) RNA motifs (d) and RNA secondary structures (e) of the 50 bp upstream and downstream sequences from TTA-pause or TTA-non pause sites identified from Ribo-seq and RNA-seq (n = 2) were examined. RNA motifs at TTA-pause sites were identified compared to TTA non-pause sites using the MEME suite35 in Differential Enrichment mode. The most significant three RNA motifs are shown for upstream and downstream sequences. The minimum free energy (MFE) of the RNA structure was calculated using RNAFold36 and was plotted in a box plot (center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; points, outliers). The p-values were calculated with the two-sided Wilcoxon Rank Sum test.

Source data

Extended Data Fig. 7 Genes containing TTA codons.

(a) Pie chart showing leucine codon usage frequency. (b) Histogram showing the distribution of the number of TTA codons per gene.

Source data

Extended Data Fig. 8 Upregulation of stress response upon DNMDP treatment.

(a) Immunoblots of the indicated proteins from HeLa cells treated with DMSO or DNMDP for the indicated time. Data are representative of n = 2 replicates. (b) Gene set enrichment analyses of up-regulated mRNAs in DNMDP-treated HeLa cells for 18 hours compared to DMSO-treated HeLa cells. The p-value was calculated by Fisher’s exact test.

Source data

Extended Data Fig. 9 Effects of leucine tRNA ectopic expression on DNMDP response.

HeLa cells stably expressing wild-type leucine tRNA-Leu-CAG or hybrid tRNA-Leu-[CAG:TAA] were treated with the indicated DNMDP concentrations for 2 days. Cell viability was assessed by CellTiter-Glo (n = 3 replicates). Data are represented as mean ± SEM.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Supplementary Tables 1–6.

Reporting Summary

Supplementary Data 1

Pause scores from Ribo-seq analysis.

Supplementary Data 2

Genes with TTA pause sites from Ribo-seq analysis.

Supplementary Data 3

Fold change of pause scores for Fig. 4.

Supplementary Data 4

The computational model of the structure of SLFN12 bound to tRNA.

Supplementary Data 5

Statistical source data for Supplementary Fig. 2.

Supplementary Data 6

Statistical source data for Supplementary Fig. 3.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Unprocessed gels.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 3

Unprocessed gels.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 5

Unprocessed blots.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 6

Unprocessed gels.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 1

Unprocessed gels.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 2

Unprocessed blots.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 3

Unprocessed blots.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 4

Unprocessed gels.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 8

Unprocessed blots.

Source Data Extended Data Fig. 9

Statistical source data.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, S., Hoyt, S., Wu, X. et al. Velcrin-induced selective cleavage of tRNALeu(TAA) by SLFN12 causes cancer cell death. Nat Chem Biol 19, 301–310 (2023). https://doi.org/10.1038/s41589-022-01170-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41589-022-01170-9

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer