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Targeting PUS7 suppresses tRNA pseudouridylation and glioblastoma tumorigenesis

Nature Cancer volume 2, pages 932–949 (2021)Cite this article

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Abstract

Pseudouridine is the most frequent epitranscriptomic modification. However, its cellular functions remain largely unknown. Here, we show that pseudouridine synthase 7 (PUS7) is highly expressed in glioblastoma versus normal brain tissues, and high PUS7 expression levels are associated with worse survival in patients with glioblastoma. PUS7 expression and catalytic activity are required for glioblastoma stem cell (GSC) tumorigenesis. Mechanistically, we identify PUS7 targets in GSCs through small RNA pseudouridine sequencing and show that pseudouridylation of PUS7-regulated transfer RNA is critical for codon-specific translational control of key regulators of GSCs. Moreover, we identify chemical inhibitors for PUS7 and show that these compounds prevent PUS7-mediated pseudouridine modification, suppress tumorigenesis and extend the life span of tumor-bearing mice. Overall, we identify an epitranscriptomic regulatory mechanism in glioblastoma and provide preclinical evidence of a potential therapeutic strategy for glioblastoma.

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Fig. 1: PUS7 is highly expressed in patients with GBM.
Fig. 2: PUS7 regulates GSC growth and self-renewal.
Fig. 3: Reduction of PUS7 expression suppresses tumor progression.
Fig. 4: PUS7 inhibitors suppress GSC growth.
Fig. 5: The PUS7 inhibitor suppresses tumor progression.
Fig. 6: Pseudouridine modification profile in small RNAs.
Fig. 7: PUS7 regulates the IFN pathway in GSCs.
Fig. 8: PUS7 regulates GSC growth by controlling the TYK2-mediated IFN pathway.

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Data availability

The data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession codes GSE147382 (for RNA-seq data) and GSE147342 (for pseudouridine-seq data). Human data were derived from the CGGA, REMBRANDT, TCGA and Gravendeel datasets. Data derived from these resources are available from the GlioVis portal (http://gliovis.bioinfo.cnio.es/). Transfer RNA sequences from the Genomic tRNA Database and human genome sequences from GCF_000001405.25 at GRCh37.p13 were used for this study. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding authors upon reasonable request.

References

  1. Machnicka, M. A. et al. MODOMICS: a database of RNA modification pathways—2013 update. Nucleic Acids Res. 41, D262–D267 (2013).

    Article  CAS  PubMed  Google Scholar 

  2. Nachtergaele, S. & He, C. Chemical modifications in the life of an mRNA transcript. Annu. Rev. Genet. 52, 349–372 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wu, G., Yu, A. T., Kantartzis, A. & Yu, Y. T. Functions and mechanisms of spliceosomal small nuclear RNA pseudouridylation. Wiley Interdiscip. Rev. RNA 2, 571–581 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Charette, M. & Gray, M. W. Pseudouridine in RNA: what, where, how, and why. IUBMB Life 49, 341–351 (2000).

    Article  CAS  PubMed  Google Scholar 

  5. Davis, F. F. & Allen, F. W. Ribonucleic acids from yeast which contain a fifth nucleotide. J. Biol. Chem. 227, 907–915 (1957).

    Article  CAS  PubMed  Google Scholar 

  6. Karijolich, J., Yi, C. & Yu, Y. T. Transcriptome-wide dynamics of RNA pseudouridylation. Nat. Rev. Mol. Cell Biol. 16, 581–585 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Hamma, T. & Ferre-D’Amare, A. R. Pseudouridine synthases. Chem. Biol. 13, 1125–1135 (2006).

    Article  CAS  PubMed  Google Scholar 

  8. Spenkuch, F., Motorin, Y. & Helm, M. Pseudouridine: still mysterious, but never a fake (uridine)! RNA Biol. 11, 1540–1554 (2014).

    Article  PubMed  Google Scholar 

  9. Rintala-Dempsey, A. C. & Kothe, U. Eukaryotic stand-alone pseudouridine synthases—RNA modifying enzymes and emerging regulators of gene expression? RNA Biol. 14, 1185–1196 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Carlile, T. M. et al. Pseudouridine profiling reveals regulated mRNA pseudouridylation in yeast and human cells. Nature 515, 143–146 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Li, X. et al. Chemical pulldown reveals dynamic pseudouridylation of the mammalian transcriptome. Nat. Chem. Biol. 11, 592–597 (2015).

    Article  CAS  PubMed  Google Scholar 

  12. Schwartz, S. et al. Transcriptome-wide mapping reveals widespread dynamic-regulated pseudouridylation of ncRNA and mRNA. Cell 159, 148–162 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lovejoy, A. F., Riordan, D. P. & Brown, P. O. Transcriptome-wide mapping of pseudouridines: pseudouridine synthases modify specific mRNAs in S. cerevisiae. PLoS ONE 9, e110799 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Song, J. et al. Differential roles of human PUS10 in miRNA processing and tRNA pseudouridylation. Nat. Chem. Biol. 16, 160–169 (2020).

    Article  CAS  PubMed  Google Scholar 

  15. Lei, Z. & Yi, C. A radiolabeling-free, qPCR-based method for locus-specific pseudouridine detection. Angew. Chem. Int. Ed. Engl. 56, 14878–14882 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Guzzi, N. et al. Pseudouridylation of tRNA-derived fragments steers translational control in stem cells. Cell 173, 1204–1216.e26 (2018).

    Article  CAS  PubMed  Google Scholar 

  17. Penzo, M., Guerrieri, A. N., Zacchini, F., Trere, D. & Montanaro, L.RNA pseudouridylation in physiology and medicine: for better and for worse. Genes 8, 301 (2017).

    Article  PubMed Central  CAS  Google Scholar 

  18. Johnson, D. R. & O’Neill, B. P. Glioblastoma survival in the United States before and during the temozolomide era. J. Neurooncol. 107, 359–364 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. Stupp, R. et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol. 10, 459–466 (2009).

    Article  CAS  PubMed  Google Scholar 

  20. Cui, Q. et al. Downregulation of TLX induces TET3 expression and inhibits glioblastoma stem cell self-renewal and tumorigenesis. Nat. Commun. 7, 10637 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Shi, Y. et al. Ibrutinib inactivates BMX-STAT3 in glioma stem cells to impair malignant growth and radioresistance. Sci. Transl. Med. 10, eaah6816 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Man, J. et al. Hypoxic induction of vasorin regulates Notch1 turnover to maintain glioma stem-like cells. Cell Stem Cell 22, 104–118.e6 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Bao, S. et al. Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature 444, 756–760 (2006).

    Article  CAS  PubMed  Google Scholar 

  24. Duan, S. et al. PTEN deficiency reprogrammes human neural stem cells towards a glioblastoma stem cell-like phenotype. Nat. Commun. 6, 10068 (2015).

    Article  CAS  PubMed  Google Scholar 

  25. Sancho-Martinez, I. et al. Establishment of human iPSC-based models for the study and targeting of glioma initiating cells. Nat. Commun. 7, 10743 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Cui, Q. et al. m6A RNA methylation regulates the self-renewal and tumorigenesis of glioblastoma stem cells. Cell Rep. 18, 2622–2634 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zhang, S. et al. m6A demethylase ALKBH5 maintains tumorigenicity of glioblastoma stem-like cells by sustaining FOXM1 expression and cell proliferation program. Cancer Cell 31, 591–606.e6 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Dixit, D. et al. The RNA m6A reader YTHDF2 maintains oncogene expression and is a targetable dependency in glioblastoma stem cells. Cancer Discov. 11, 480–499 (2021).

    Article  CAS  PubMed  Google Scholar 

  29. Fang, R. et al. EGFR/SRC/ERK-stabilized YTHDF2 promotes cholesterol dysregulation and invasive growth of glioblastoma. Nat. Commun. 12, 177 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Waalkes, T. P., Dinsmore, S. R. & Mrochek, J. E. Urinary excretion by cancer patients of the nucleosides N-dimethylguanosine, 1-methylinosine, and pseudouridine. J. Natl Cancer Inst. 51, 271–274 (1973).

    Article  CAS  PubMed  Google Scholar 

  31. Stockert, J. A. et al. Predictive value of pseudouridine in prostate cancer. Am. J. Clin. Exp. Urol. 7, 262–272 (2019).

    PubMed  PubMed Central  Google Scholar 

  32. Zhao, Z. et al. Comprehensive RNA-seq transcriptomic profiling in the malignant progression of gliomas. Sci. Data 4, 170024 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Gusev, Y. et al. The REMBRANDT study, a large collection of genomic data from brain cancer patients. Sci. Data 5, 180158 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Brennan, C. W. et al. The somatic genomic landscape of glioblastoma. Cell 155, 462–477 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Gravendeel, L. A. et al. Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology. Cancer Res. 69, 9065–9072 (2009).

    Article  CAS  PubMed  Google Scholar 

  36. Cohen, A. L., Holmen, S. L. & Colman, H. IDH1 and IDH2 mutations in gliomas. Curr. Neurol. Neurosci. Rep. 13, 345 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Labussiere, M. et al. All the 1p19q codeleted gliomas are mutated on IDH1 or IDH2. Neurology 74, 1886–1890 (2010).

    Article  CAS  PubMed  Google Scholar 

  38. Suva, M. L. et al. Reconstructing and reprogramming the tumor-propagating potential of glioblastoma stem-like cells. Cell 157, 580–594 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Ligon, K. L. et al. Olig2-regulated lineage-restricted pathway controls replication competence in neural stem cells and malignant glioma. Neuron 53, 503–517 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Veselska, R. et al. Nestin expression in the cell lines derived from glioblastoma multiforme. BMC Cancer 6, 32 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Anido, J. et al. TGF-β receptor inhibitors target the CD44high/Id1high glioma-initiating cell population in human glioblastoma. Cancer Cell 18, 655–668 (2010).

    Article  CAS  PubMed  Google Scholar 

  42. Liu, G. et al. Analysis of gene expression and chemoresistance of CD133+ cancer stem cells in glioblastoma. Mol. Cancer 5, 67 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Son, M. J., Woolard, K., Nam, D. H., Lee, J. & Fine, H. A. SSEA-1 is an enrichment marker for tumor-initiating cells in human glioblastoma. Cell Stem Cell 4, 440–452 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Bao, S. et al. Targeting cancer stem cells through L1CAM suppresses glioma growth. Cancer Res. 68, 6043–6048 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ogden, A. T. et al. Identification of A2B5+CD133− tumor-initiating cells in adult human gliomas. Neurosurgery 62, 505–514 (2008).

    Article  PubMed  Google Scholar 

  46. Qiang, L. et al. Isolation and characterization of cancer stem like cells in human glioblastoma cell lines. Cancer Lett. 279, 13–21 (2009).

    Article  CAS  PubMed  Google Scholar 

  47. Behm-Ansmant, I. et al. The Saccharomyces cerevisiae U2 snRNA:pseudouridine-synthase Pus7p is a novel multisite-multisubstrate RNA:Psi-synthase also acting on tRNAs. RNA 9, 1371–1382 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Taoka, M. et al. Landscape of the complete RNA chemical modifications in the human 80S ribosome. Nucleic Acids Res. 46, 9289–9298 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Seidel, A., Brunner, S., Seidel, P., Fritz, G. I. & Herbarth, O. Modified nucleosides: an accurate tumour marker for clinical diagnosis of cancer, early detection and therapy control. Br. J. Cancer 94, 1726–1733 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Frye, M., Harada, B. T., Behm, M. & He, C.RNA modifications modulate gene expression during development. Science 361, 1346–1349 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Zheng, L. L. et al. tRF2Cancer: a web server to detect tRNA-derived small RNA fragments (tRFs) and their expression in multiple cancers. Nucleic Acids Res. 44, W185–W193 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Pickerill, E. S. et al. Pseudouridine synthase 7 impacts Candida albicans rRNA processing and morphological plasticity. Yeast 36, 669–677 (2019).

    Article  CAS  PubMed  Google Scholar 

  53. Darvish, H. et al. A novel PUS7 mutation causes intellectual disability with autistic and aggressive behaviors. Neurol. Genet. 5, e356 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. De Brouwer, A. P. M. et al. Variants in PUS7 cause intellectual disability with speech delay, microcephaly, short stature, and aggressive behavior. Am. J. Hum. Genet. 103, 1045–1052 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Silginer, M. et al. Autocrine activation of the IFN signaling pathway may promote immune escape in glioblastoma. Neuro Oncol. 19, 1338–1349 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Zhan, X. et al. Glioma stem-like cells evade interferon suppression through MBD3/NuRD complex-mediated STAT1 downregulation. J. Exp. Med. 217, e20191340 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Alvarado, A. G. et al. Glioblastoma cancer stem cells evade innate immune suppression of self-renewal through reduced TLR4 expression. Cell Stem Cell 20, 450–461.e4 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Pencheva, N. et al. Identification of a druggable pathway controlling glioblastoma invasiveness. Cell Rep. 20, 48–60 (2017).

    Article  CAS  PubMed  Google Scholar 

  59. Zhu, Z. et al. Zika virus targets glioblastoma stem cells through a SOX2–integrin αvβ5 axis. Cell Stem Cell 26, 187–204.e10 (2020).

    Article  CAS  PubMed  Google Scholar 

  60. Shi, Y. et al. Expression and function of orphan nuclear receptor TLX in adult neural stem cells. Nature 427, 78–83 (2004).

    Article  CAS  PubMed  Google Scholar 

  61. Bowman, R. L., Wang, Q., Carro, A., Verhaak, R. G. & Squatrito, M. GlioVis data portal for visualization and analysis of brain tumor expression datasets. Neuro Oncol. 19, 139–141 (2017).

    Article  CAS  PubMed  Google Scholar 

  62. Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Su, R. et al. Targeting FTO suppresses cancer stem cell maintenance and immune evasion. Cancer Cell 38, 79–96.e11 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Van Nostrand, E. L. et al. Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods 13, 508–514 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Li, X. et al. Base-resolution mapping reveals distinct m1A methylome in nuclear- and mitochondrial-encoded transcripts. Mol. Cell 68, 993–1005.e9 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Bailey, T. L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    Article  CAS  PubMed  Google Scholar 

  70. Wang, X. et al. N6-methyladenosine modulates messenger RNA translation efficiency. Cell 161, 1388–1399 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Li, L. et al. GFAP mutations in astrocytes impair oligodendrocyte progenitor proliferation and myelination in an hiPSC model of Alexander disease. Cell Stem Cell 23, 239–251 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Wang, C. et al. ApoE-isoform-dependent SARS-CoV-2 neurotropism and cellular response. Cell Stem Cell 28, 331–342.e5 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank L. Horvitz and H. Horvitz for generosity and forethought, Y. Lin and X. Li for technical assistance and X. Xiong for bioinformatics analysis suggestions. This work was supported by the Louise and Herbert Horvitz Charitable Foundation, Sidell Kagan Foundation, National Institute on Aging of the National Institutes of Health (R01 AG056305, RF1 AG061794 and R56 AG061171) to Y.S., National Key R&D Program (2019YFA0110900 and 2019YFA0802200 to C. Y.), National Natural Science Foundation of China (21825701 and 91940304 to C.Y.) and National Center for Protein Sciences at Peking University (to C. Y.). Research reported in this publication includes work performed in the Small Animal Imaging, Synthetic and Biopolymer Chemistry, Integrative Genomics and Analytical Cytometry cores at City of Hope, and was also supported by the National Cancer Institute of the National Institutes of Health under award number P30CA33572. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Part of the analysis was performed on the high-performance computing platform of the Center for Life Sciences (Peking University). The results of the TCGA database analysis published here are in whole or in part based on data generated by the TCGA Research Network: https://www.cancer.gov/tcga.

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Author notes

  1. These authors contributed equally: Qi Cui, Kailin Yin, Xiaoting Zhang.

Authors and Affiliations

  1. Division of Stem Cell Biology Research, Department of Developmental and Stem Cell Biology, Beckman Research Institute of City of Hope, Duarte, CA, USA

    Qi Cui, Peng Ye, Xianwei Chen, Jianfei Chao, Li Li, Yue Qin, Mingzi Zhang, Jeremy Klein, Marvin Huynhle, Cheng Wang & Yanhong Shi

  2. State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China

    Kailin Yin, Xiaoting Zhang, Haowei Meng & Chengqi Yi

  3. Department of Chemistry, The University of Chicago, Chicago, IL, USA

    Jiangbo Wei & Chuan He

  4. Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA

    Jiangbo Wei & Chuan He

  5. Department of Molecular Imaging and Therapy, Beckman Research Institute of City of Hope, Duarte, CA, USA

    Daniel Roeth & Markus Kalkum

  6. Diabetes and Metabolism Research Institute at City of Hope, Duarte, CA, USA

    Guihua Sun

  7. Department of Surgery, Beckman Research Institute of City of Hope, Duarte, CA, USA

    Leying Zhang & Behnam Badie

  8. Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL, USA

    Chuan He

  9. Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA

    Chuan He

  10. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China

    Chengqi Yi

  11. Irell & Manella Graduate School of Biological Sciences, Beckman Research Institute of City of Hope, Duarte, CA, USA

    Yanhong Shi

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  1. Qi Cui
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Contributions

Y.S., C.Y., Q.C., K.Y. and X.Z. designed the experiments and interpreted the results. Q.C. performed the experiments. Q.C., K.Y., X.Z. and H.M. performed the DM-Ψ-seq, pseudouridine sequencing and TYK2 codon usage analysis. Q.C. performed the luciferase reporter assay using reporter constructs prepared by K.Y. Y.S., C.H., J.W. and Q.C. designed the polysome profiling experiment. J.W. performed the polysome profiling analysis from cells provided by Q.C.. Y.S., M.K., Q.C. and D.R. designed the TMT proteomics experiment. D.R. performed the TMT proteomics experiment from cells provided by Q.C.. P.Y. and Q.C. performed the GSC transplantation experiments and PUS7 inhibitor in vivo treatment experiments. X.C. established and performed the in vitro PUS7 activity assay with substrates designed and prepared by Q.C. J.C. performed mass spectrometry sample injection and helped with mass spectrometry analysis of PUS7 inhibitor-treated samples provided by Q.C. L.L. prepared the sgRNA construct and performed the western blot for TYK2 and STAT1. Q.C. prepared the samples for RNA-seq analysis in the Integrative Genomics Core. X.Z. and Q.C. performed the PUS7 and ISG correlation analysis. L.Z. and B.B. provided tissues of patients with GBM and established GBM cell lines. G.S. designed the PUS7 shRNA constructs and prepared the coding sequences of the PUS7 constructs. Y.Q. performed the OP-puro incorporation assay and cell cycle analysis from cells provided by Q.C. M.Z. performed the tissue microarray IHC analysis and apoptosis analysis. K.Y. performed the nascent protein synthesis assay and protein silver staining. J.K. provided technical help with PBT707 GSC transplantation and tumor imaging. M.H. performed the western blot for PUS7 in the GSC and NSC samples provided by Q.C. M.Z. and C.W. provided astrocytes. Y.S., C.Y., Q.C., K.Y. and X.Z. wrote the manuscript with comments from all other authors.

Corresponding authors

Correspondence to Chengqi Yi or Yanhong Shi.

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Competing interests

C.H. is a scientific founder and a member of the scientific advisory board of Accent Therapeutics. The other authors declare no competing interests.

Additional information

Peer review information Nature Cancer thanks Sandra Blanco, Massimo Squatrito and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 High level of PUS7 expression correlates with poor prognosis in GBM patients.

(a) The expression of PUS7 in all glioma patients stratified by the IDH mutation status and 1p19q chromosome co-deletion status from the CGGA dataset (n = 182 IDH mut 1p19q codel patients, n = 315 IDH mut 1p19q noncodel patients, n = 392 IDH WT patients). (b) The expressions of PUS7 in GBM patients stratified by IDH mutation status or GCIMP status from the CGGA (n = 90 Mut patients and n = 288 WT patients), TCGA (n = 8 Mut patients and n = 142 WT patients), Gravendeel (n = 33 Mut patients and n = 95 WT patients), and Rembrandt (n = 11 GCIMP patients and n = 208 Non-GCIMP patients) datasets. (c-f)Kaplan-Meier survival curves with log-rank analysis to assess the correlation between PUS7 expression and overall survival of IDH WT GBM patients in the CGGA dataset (c), TCGA dataset (e), and Gravendeel dataset (f) or non GCIMP GBM patients in the REMBRANDT dataset (d). (g) The expression of SOX2 and PUS7 in GBM patients and in non-tumor control samples in GBM tissue microarray analyzed by immunohistochemistry (IHC). Scale bar: 10 µm. (h) Quantification of the expression level of PUS7 in GBM patients and in non-tumor control samples analyzed by IHC. n = 34 individuals for GBM patients and 5 individuals for non-tumor control group. Error bars represent SD of the mean for panels a and b. Error bars represent SE of the mean for panel h. Two-tailed Student’s t test for panels a and b (ns: not statistically significant. p = 0.2844 for CGGA, p = 0.1533 for Gravendeel, and p = 0.1095 for Rembrandt). **p < 0.01 (p = 0.002) by one-tailed Student’s t-test for panel h.

Source data

Extended Data Fig. 2 PUS7 regulates GSC growth and self-renewal.

(a) RT-PCR analysis of PUS7 knock down (KD) in GSCs (PBT003, PBT707, PBT726, PBT111, PBT017, and PBT030) transduced with lentivirus expressing control shRNA (shC) or PUS7 shRNA (sh1 and sh2). n = 3 technical replicates. p = 0.0002 for sh1 and p = 0.0002 for sh2 in PBT003; p = 0.0005 for sh1 and p = 0.0004 for sh2 in PBT707; p = 0.0008 for sh1 and p = 0.0066 for sh2 in PBT726; p = 0.0003 for sh1 and p < 0.0001 for sh2 in PBT111; p = 0.0009 for sh1 and p = 0.0004 for sh2 in PBT017; p < 0.0001 for sh1 and p < 0.0001 for sh2 in PBT030. (b) Western blot analysis of PUS7 KD in GSCs (PBT003 and PBT726). The uncropped blot images for the cropped images shown here are in the source data. Repeated twice with similar results. (c) Cell growth of GSCs (PBT017 and PBT030) transduced with lentivirus expressing control shRNA (shC) or PUS7 shRNA (sh1 and sh2). n = 4 cell culture replicates. p < 0.0001 for sh1 and p < 0.0001 for sh2 in PBT017; p < 0.0001 for sh1 and p = 0.0013 for sh2 in PBT030. (d) Sphere formation of GSCs (PBT017 and PBT030) transduced with lentivirus expressing shC or PUS7 shRNA (sh1 and sh2). n = 4 cell culture replicates. p = 0.006 for sh1 and p = 0.006 for sh2 in PBT017; p = 0.0063 for sh1 and p = 0.0063 for sh2 in PBT030. (e) Western blot analysis of PUS7 in PBT003 GSCs transduced with lentivirus expressing control sgRNA or sgRNA for PUS7 (sg1 and sg2). The uncropped blot images for the cropped images shown here are in the source data. Repeated four times with similar results. (f) Cell growth of PBT003 GSCs transduced with lentivirus expressing control sgRNA or sgRNA for PUS7. n = 4 cell culture replicates. p = 0.0043 for sg1 and p = 0.0009 for sg2. (g) Sphere formation of PBT003 GSCs transduced with lentivirus expressing control sgRNA or sgRNA for PUS7. n = 20 sphere-forming culture replicates. (h) Active Caspase 3 (Cas3) analysis of PBT003 GSCs transduced with lentivirus expressing control sgRNA or sgRNA for PUS7. p < 0.0001 for sg1 and p < 0.0001 for sg2. n = 5 cell culture replicates. (i) Cell cycle analysis of PBT003 GSCs transduced with lentivirus expressing control sgRNA or sgRNA for PUS7. (j) Western blot analysis showing overexpression of the WT and the mutant PUS7 in PBT003 and PBT707 GSCs. The uncropped blot images for the cropped images shown here are in the source data. Repeated three times with similar results. Error bars are SE of the mean for this figure. **p < 0.01 and ***p < 0.001 by One-way ANOVA and Dunnett’s multiple comparisons test for panels a, c, d, f, and g. ns: not statistically significant (p = 0.1372) by one-tailed Student’s t-test for panel h.

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Extended Data Fig. 3 PUS7 regulates GSC growth in a catalytic activity dependent manner.

The WT but not the mutant (Mut) PUS7 rescued PUS KD-induced growth inhibition in PBT003 and PBT707 GSCs. n = 4 cell culture replicates. p < 0.0001 for shPUS7 (-) and PUS7 (-) vs shPUS7 (+) and PUS7 (-), p < 0.0001 for shPUS7 (+) and PUS7 (-) vs shPUS7 (+) and WT PUS7 ( + ), ns: p = 0.7179 for shPUS7 (+) and PUS7 (-) vs shPUS7 (+) and Mut PUS7 ( + ) in PBT003; p < 0.0001 for shPUS7 (-) and PUS7(-) vs shPUS7 (+) and PUS7(-), p < 0.0001 for shPUS7 (+) and PUS7 (-) vs shPUS7 (+) and WT PUS7 ( + ), ns: p = 0.9976 for shPUS7 (+) and PUS7 (-) vs shPUS7 (+) and Mut PUS7 ( + ) in PBT707. Error bars are SE of the mean for this figure. ***p < 0.001 and ns: not statistically significant (p > 0.05, defined above) by One-way ANOVA and Dunnett’s multiple comparisons test for this figure.

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Extended Data Fig. 4 Inhibition of PUS7 suppresses tumor progression.

(a) Bioluminescent images of brain tumors in NSG mice transplanted with PBT003 GSCs that were transduced with control sgRNA (Control-sg) or PUS7 sgRNA (PUS7-sg). (b) Quantification of the bioluminescence intensity of tumors after PBT003 GSC transplantation. n = 5 mice for each group. Error bars represent SE of the mean. *p < 0.05 (p = 0.037) by one-tailed Student’s t-test. (c) The survival curves of NSG mice transplanted with PBT003 GSCs transduced with control sgRNA or PUS7 sgRNA. n = 5 mice for each group. The X axis represents days after GSC transplantation. Log-rank test for mice survival.

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Extended Data Fig. 5 PUS7 inhibitors suppress GSC growth.

(a) Cell growth of PBT003 GSCs treated with the C4 PUS7 inhibitor. n = 4 cell culture replicates. p = 0.0009 for 10 µM and p < 0.0001 for 50 µM condition. (b) Cell growth of PBT003 GSCs or NSC009 NSCs treated with the C17 PUS7 inhibitor. n = 4 cell culture replicates. p < 0.0001 for PBT003 and ns: p = 0.2831 for NSC009. (c) IC50 test for C17 compound in GSCs (PBT003, PBT707, PBT726, and PBT111). For each GSC, n = 4 cell culture replicates for each treatment condition. (d) Cell growth of GSC (PBT707, PBT726, and PBT111) treated with the C17 analog compound. n = 4 cell culture replicates. p = 0.0002, <0.0001, <0.0001, <0.0001 for 0.4, 2, 10, 50 µM conditions respectively in PBT707; p < 0.0001 for 2, 10, 50 µM conditions in PBT726; p < 0.0001 for 2, 10, 50 µM conditions in PBT111. Error bars are SE of the mean. ***p < 0.001 by One-way ANOVA and Dunnett’s multiple comparisons test for panels a and d. ***p < 0.001 and ns: not statistically significant (p > 0.05, defined above) by one-tailed Student’s test for panel b.

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Extended Data Fig. 6 The pseudouridine modification profile in GSCs.

(a) A representative PUS7-dependent pseudouridine site identified by small RNA DM-Ψ-seq in PBT003 GSCs. (b) Validation of the PUS7-dependent pseudouridine site in tRNA-Arg-CCG-2-1 in PUS7 KO PBT003 GSCs by primer extension assay. The uncropped blot images for the cropped images shown here are in the source data. Repeated twice with similar results. (c) A representative PUS7-dependent pseudouridine site in tRNA-Glu-TTC-4-1 in control or C17-treated PBT003 GSCs. (d) Pearson correlation analysis for global tRNA abundance in control and PUS7 KO PBT003 GSCs. (e) Expression of tRNA-Arg-CCG in control and PUS7 KO PBT003 GSCs examined by Northern blot analysis. U6 was used as a loading control. The uncropped blot images for the cropped images shown here are in the source data. Repeated twice with similar results. (f) Analysis of tRF abundance in control and PUS7 KO PBT003 GSCs. Red dots: tRFs derived from tRNAs with PUS7-dependent pseudouridine sites. The q value was calculated by Cochran Mantel Haenszel test and adjusted by BH methods. (g) The OP-puro incorporation analysis of control and PUS7 KO PBT707 GSCs. (h) Nascent protein synthesis and total protein level analysis of control and PUS7 KO 293T cells. (i) A luciferase reporter assay to test tRNA translation efficiency in control and PUS7 KO PBT707 cells. n = 3 cell culture replicates. Error bars are SE of the mean. *p < 0.05 (p = 0.0251), and ns: not statistically significant [p > 0.05, p = 0.0921 for control, p = 0.0251 for 6x(CGG)Arg, and p = 0.1238 for 6x(CGA)Arg] by one-tailed Student’s t-test.

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Extended Data Fig. 7 PUS7 regulates IFN pathway in GSC.

(a) Correlation analysis of PUS7 expression and IFN gene signature (IFN alpha response gene signature and IFN gamma response gene signature) analyzed by ssGSEA in GBM patients from the TCGA dataset. (b) The growth of PBT003 and PBT707 GSCs treated with IFNα. n = 4 cell culture replicates. p = 0.0093 for 20 ng/ml and p = 0.0002 for 100 ng/ml in PBT003; p = 0.0064, <0.0001, <0.0001 for 4, 20, 100 ng/ml conditions, respectively, in PBT707. (c) RT-PCR of ISGs in C17 compound-treated PBT003 GSCs. n = 3 technical replicates. p = 0.0041 for ISG15 and p = 0.0015 for XAF1. (d) RT-PCR of ISGs in C17 compound-treated tumor derived from PBT003 GSCs. n = 3 technical replicates. p = 0.0004 for ISG15 and p = 0.0001 for XAF1. Error bars are SE of the mean. **p < 0.01 and ***p < 0.001 by One-way ANOVA and Dunnett’s multiple comparisons test for panels b, and by one-tailed Student’s t-test for panels c and d.

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Extended Data Fig. 8 PUS7 regulates GSC growth through controlling TYK2-mediated IFN pathway.

(a) RT-PCR analysis of WT or mutant TYK2 in PUS7 KO PBT003 GSCs. n = 3 technical replicates. p = 0.1533 for WT and p = 0.0719 for Mut. (b) RT-PCR analysis of WT or mutant TYK2 in PUS7 KO PBT707 GSCs. n = 3 technical replicates. p = 0.0617 for WT and p = 0.0625 for Mut. (c) Western blot analysis of WT or mutant TYK2 in PUS7 KO PBT707 GSCs. The uncropped blot images for the cropped images shown here are in the source data. Repeated twice with similar results. (d) Western blot analysis of TYK2 in TYK2 KO PBT003 and PBT707 GSCs. The uncropped blot images for the cropped images shown here are in the source data. Repeated twice with similar results. (e) Western blot analysis of STAT1 and phosphorylated STAT1 (pSTAT1) in STAT1 KO PBT003 and PBT707 GSCs. The uncropped blot images for the cropped images shown here are in the source data. Repeated twice with similar results. (f) Western blot of PUS7 and TYK2 in PBT003 GSCs transduced with lentivirus expressing PUS7 sgRNA and/or lentivirus expressing sgRNA for TYK2. The uncropped blot images for the cropped images shown here are in the source data. Repeated twice with similar results. (g) The growth of PBT003 and PBT707 GSCs treated by the STAT1 inhibitor fludarabine with or without lentivirus expressing PUS7 sgRNA. n = 4 cell culture replicates. p = 0.0003 for PUS7sg (-) and STAT1 inhibitor (-) vs PUS7sg ( + ) and STAT1 inhibitor (-), p = 0.0144 for PUS7sg ( + ) and STAT1 inhibitor (+) vs PUS7sg ( + ) and STAT1 inhibitor (-) in PBT003; p = 0.0004 for PUS7sg (-) and STAT1 inhibitor (-) vs PUS7sg ( + ) and STAT1 inhibitor (-), p = 0.0114 for PUS7sg ( + ) and STAT1 inhibitor (+) vs PUS7sg ( + ) and STAT1 inhibitor (-) in PBT707. Error bars are SE of the mean. *p < 0.05, ***p < 0.001, and ns: not statistically significant (p > 0.05) by One-way ANOVA and Dunnett’s multiple comparisons test for panels g, and by one-tailed Student’s t-test for panels a and b.

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Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–12.

Supplementary Information

Supplementary Table 13.

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Cui, Q., Yin, K., Zhang, X. et al. Targeting PUS7 suppresses tRNA pseudouridylation and glioblastoma tumorigenesis. Nat Cancer 2, 932–949 (2021). https://doi.org/10.1038/s43018-021-00238-0

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