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.

Alternative RNA splicing modulates ribosomal composition and determines the spatial phenotype of glioblastoma cells

Abstract

Glioblastoma (GBM) is characterized by exceptionally high intratumoral heterogeneity. However, the molecular mechanisms underlying the origin of different GBM cell populations remain unclear. Here, we found that the compositions of ribosomes of GBM cells in the tumour core and edge differ due to alternative RNA splicing. The acidic pH in the core switches before messenger RNA splicing of the ribosomal gene RPL22L1 towards the RPL22L1b isoform. This allows cells to survive acidosis, increases stemness and correlates with worse patient outcome. Mechanistically, RPL22L1b promotes RNA splicing by interacting with lncMALAT1 in the nucleus and inducing its degradation. Contrarily, in the tumour edge region, RPL22L1a interacts with ribosomes in the cytoplasm and upregulates the translation of multiple messenger RNAs including TP53. We found that the RPL22L1 isoform switch is regulated by SRSF4 and identified a compound that inhibits this process and decreases tumour growth. These findings demonstrate how distinct GBM cell populations arise during tumour growth. Targeting this mechanism may decrease GBM heterogeneity and facilitate therapy.

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

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: GBM cells from the edge and core of the tumour have ribosomes with different protein compositions.
Fig. 2: Alternative splicing generates two different isoforms of RPL22L1.
Fig. 3: The RPL22L1 isoform ratio is regulated by extracellular pH.
Fig. 4: Interactome of RPL22L1 isoforms.
Fig. 5: Molecular functions of RPL22L1b.
Fig. 6: Molecular functions of RPL22L1a.
Fig. 7: SRSF4 regulates the splicing of RPL22L1.
Fig. 8: FG1059 impairs the splicing of RPL22L1.

Data availability

All of the proteomics data that were obtained during this study are presented in Supplementary Tables 1, 2, 4, 5, 7 and 9. The raw mass spectrometry data have been deposited in ProteomeXchange with the primary accession codes PXD035849, PXD035767 and PXD035855. All of the RNA-seq data that were obtained during this study are presented in Supplementary Tables 3, 6 and 8. The raw RNA-seq data of GBM neurospheres overexpressing different isoforms of RPL22L1 protein or an empty vector as a control have been deposited in the Gene Expression Omnibus (GEO) database under accession code GSE180465. The RNA-IP sequencing data for RNA that interacts with Fc-tagged RPL22L1a, RPL22L1b or a control protein have been deposited in the GEO database under accession code GSE180464. Previously published RNA-seq data for GBM neurospheres and GBM tissue isolated from the different regions of the tumours that were re-analysed here are available from the GEO database under accession codes PRJNA344648 and GSE153746. iCLIP data for the SRSF4 protein that were re-analysed here are available from the Array Express database under accession code E-MTAB-747. Gene expression data from the Ivy Glioblastoma Atlas Project database (https://glioblastoma.alleninstitute.org/), gene expression and survival data from the Repository for Molecular Brain Neoplasia Data (GSE108474 and GSE68848) and gene expression, DNA methylation, survival and phenotype data from The Cancer Genome Atlas database (https://tcga-data.nci.nih.gov/tcga/) were used in this study. Source data are provided with this paper.

References

  1. Van Linde, M. E. et al. Treatment outcome of patients with recurrent glioblastoma multiforme: a retrospective multicenter analysis. J. Neurooncol. 135, 183–192 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Puchalski, R. B. et al. An anatomic transcriptional atlas of human glioblastoma. Science 360, 660–663 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Bastola, S. et al. Glioma-initiating cells at tumor edge gain signals from tumor core cells to promote their malignancy. Nat. Commun. 11, 4660 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Hjelmeland, A. B. et al. Acidic stress promotes a glioma stem cell phenotype. Cell Death Differ. 18, 829–840 (2011).

    Article  CAS  PubMed  Google Scholar 

  5. Vaupel, P., Kallinowski, F. & Okunieff, P. Blood flow, oxygen and nutrient supply, and metabolic microenvironment of human tumors: a review. Cancer Res. 49, 6449–6465 (1989).

    CAS  PubMed  Google Scholar 

  6. Ozawa, T. et al. Most human non-GCIMP glioblastoma subtypes evolve from a common proneural-like precursor glioma. Cancer Cell 26, 288–300 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Bastola, S. et al. Tumor edge architecture in glioblastoma is constructed by inter-cellular signals from vascular endothelial cells. Preprint at bioRxiv https://doi.org/10.1101/2020.10.12.335091 (2020).

  8. Phillips, H. S. et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 9, 157–173 (2006).

    Article  CAS  PubMed  Google Scholar 

  9. De Aquino, P. F. et al. A time-based and intratumoral proteomic assessment of a recurrent glioblastoma multiforme. Front. Oncol. 6, 183 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Yanovich-Arad, G. et al. Proteogenomics of glioblastoma associates molecular patterns with survival. Preprint at medRxiv https://doi.org/10.1101/2020.04.28.20083501 (2020).

  11. Gularyan, S. K. et al. Investigation of inter- and intratumoral heterogeneity of glioblastoma using TOF-SIMS. Mol. Cell. Proteom. 19, 960–970 (2020).

    Article  Google Scholar 

  12. Heiland, D. H. et al. The integrative metabolomic–transcriptomic landscape of glioblastome multiforme. Oncotarget 8, 49178–49190 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Lemée, J. M. et al. Integration of transcriptome and proteome profiles in glioblastoma: looking for the missing link. BMC Mol. Biol. 19, 13 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ruggero, D. Translational control in cancer etiology. Cold Spring Harb. Perspect. Biol. 5, a012336 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Fujii, K., Shi, Z., Zhulyn, O., Denans, N. & Barna, M. Pervasive translational regulation of the cell signalling circuitry underlies mammalian development. Nat. Commun. 8, 14443 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kondrashov, N. et al. Ribosome-mediated specificity in Hox mRNA translation and vertebrate tissue patterning. Cell 145, 383–397 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Guimaraes, J. C. & Zavolan, M. Patterns of ribosomal protein expression specify normal and malignant human cells. Genome Biol. 17, 236 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Ebright, R. Y. et al. Deregulation of ribosomal protein expression and translation promotes breast cancer metastasis. Science 367, 1468–1473 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Panda, A. et al. Tissue- and development-stage-specific mRNA and heterogeneous CNV signatures of human ribosomal proteins in normal and cancer samples. Nucleic Acids Res. 48, 7079–7098 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Yong, W. H. et al. Ribosomal proteins RPS11 and RPS20, two stress-response markers of glioblastoma stem cells, are novel predictors of poor prognosis in glioblastoma patients. PLoS ONE 10, e0141334 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Shi, Z. et al. Heterogeneous ribosomes preferentially translate distinct subpools of mRNAs genome-wide. Mol. Cell 67, 71–83 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zhang, Y. et al. Control of hematopoietic stem cell emergence by antagonistic functions of ribosomal protein paralogs. Dev. Cell 24, 411–425 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zhang, Y. et al. Ribosomal proteins Rpl22 and Rpl22l1 control morphogenesis by regulating pre-mRNA splicing. Cell Rep. 18, 545–556 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Liang, Z. et al. Identification of candidate diagnostic and prognostic biomarkers for human prostate cancer: RPL22L1 and RPS21. Med. Oncol. 36, 56 (2019).

    Article  PubMed  Google Scholar 

  25. Ma, J., Jing, X., Chen, Z., Duan, Z. & Zhang, Y. MiR-361-5p decreases the tumorigenicity of epithelial ovarian cancer cells by targeting at RPL22L1 and c-Met signaling. Int. J. Clin. Exp. Pathol. 11, 2588–2596 (2018).

    PubMed  PubMed Central  Google Scholar 

  26. Rao, S. et al. RPL22L1 induction in colorectal cancer is associated with poor prognosis and 5-FU resistance. PLoS ONE 14, e0222392 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Bell, J. L. et al. Identification of RNA-binding proteins as targetable putative oncogenes in neuroblastoma. Int. J. Mol. Sci. 21, 5098 (2020).

    Article  CAS  PubMed Central  Google Scholar 

  28. O’Leary, M. N. et al. The ribosomal protein Rpl22 controls ribosome composition by directly repressing expression of its own paralog, Rpl22l1. PLoS Genet. 9, e1003708 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Minata, M. et al. Phenotypic plasticity of invasive edge glioma stem-like cells in response to ionizing radiation. Cell Rep. 26, 1893–1905 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kurosaki, T. & Maquat, L. E. Nonsense-mediated mRNA decay in humans at a glance. J. Cell Sci. 129, 461–467 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Sato, H. & Singer, R. H. Cellular variability of nonsense-mediated mRNA decay. Nat. Commun. 12, 7203 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Hoek, T. A. et al. Single-molecule imaging uncovers rules governing nonsense-mediated mRNA decay. Mol. Cell. 75, 324–339 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lindeboom, R. G., Supek, F. & Lehner, B. The rules and impact of nonsense-mediated mRNA decay in human cancers. Nat. Genet. 48, 1112–1118 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Martin, L. et al. Identification and characterization of small molecules that inhibit nonsense-mediated RNA decay and suppress nonsense p53 mutations. Cancer Res. 74, 3104–3113 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Longman, D. et al. Identification of a localized nonsense-mediated decay pathway at the endoplasmic reticulum. Genes Dev. 34, 1075–1088 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Michel, A. M. et al. GWIPS-viz: development of a ribo-seq genome browser. Nucleic Acids Res. 42, D859–D864 (2014).

    Article  CAS  PubMed  Google Scholar 

  38. Choudhary, S. et al. Genomic analyses of early responses to radiation inglioblastoma reveal new alterations at transcription, splicing, and translation levels. Sci. Rep. 10, 8979 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Pearson, H. et al. MHC class I-associated peptides derive from selective regions of the human genome. J. Clin. Invest. 126, 4690–4701 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ellingson, B. M. et al. pH-weighted molecular MRI in human traumatic brain injury (TBI) using amine proton chemical exchange saturation transfer echoplanar imaging (CEST EPI). Neuroimage Clin. 22, 101736 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Corbet, C. et al. TGFβ2-induced formation of lipid droplets supports acidosis-driven EMT and the metastatic spreading of cancer cells. Nat. Commun. 11, 454 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Sonabend, A. M. et al. The transcriptional regulatory network of proneural glioma determines the genetic alterations selected during tumor progression. Cancer Res. 74, 1440–1451 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Saito, N. et al. A high Notch pathway activation predicts response to γ secretase inhibitors in proneural subtype of glioma tumor-initiating cells. Stem Cells 32, 301–312 (2014).

    Article  CAS  PubMed  Google Scholar 

  45. Pavlyukov, M. S. et al. Apoptotic cell-derived extracellular vesicles promote malignancy of glioblastoma via intercellular transfer of splicing factors. Cancer Cell 34, 119–135 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wang, Z., Zhang, H., Xu, S., Liu, Z. & Cheng, Q. The adaptive transition of glioblastoma stem cells and its implications on treatments. Signal Transduct. Target. Ther. 6, 124 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Moreb, J. S. Aldehyde dehydrogenase as a marker for stem cells. Curr. Stem Cell Res. Ther. 3, 237–246 (2008).

    Article  CAS  PubMed  Google Scholar 

  48. Julian, L. M. & Stanford, W. L. Organelle cooperation in stem cell fate: lysosomes as emerging regulators of cell identity. Front. Cell Dev. Biol. 8, 591 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Wu, G. et al. Inhibition of SF3B1 by molecules targeting the spliceosome results in massive aberrant exon skipping. RNA 24, 1056–1066 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Dewaele, M. et al. Antisense oligonucleotide-mediated MDM4 exon 6 skipping impairs tumor growth. J. Clin. Invest. 126, 68–84 (2016).

    Article  PubMed  Google Scholar 

  51. McCown, P. J., Wang, M. C., Jaeger, L. & Brown, J. A. Secondary structural model of human MALAT1 reveals multiple structure–function relationships. Int. J. Mol. Sci. 20, 5610 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  52. Tripathi, V. et al. The nuclear-retained noncoding RNA MALAT1 regulates alternative splicing by modulating SR splicing factor phosphorylation. Mol. Cell 39, 925–938 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Baspinar, Y., Elmaci, I., Ozpinar, A. & Altinoz, M. A. Long non-coding RNA MALAT1 as a key target in pathogenesis of glioblastoma. Janus faces or Achilles’ heal? Gene 739, 144518 (2020).

    Article  CAS  PubMed  Google Scholar 

  54. Latorre, E. et al. The ribonucleic complex HuR–MALAT1 represses CD133 expression and suppresses epithelial–mesenchymal transition in breast cancer. Cancer Res. 76, 2626–2636 (2016).

    Article  CAS  PubMed  Google Scholar 

  55. Mao, P. et al. Mesenchymal glioma stem cells are maintained by activated glycolytic metabolism involving aldehyde dehydrogenase 1A3. Proc. Natl Acad. Sci. USA 110, 8644–8649 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Singh, K. et al. c-MYC regulates mRNA translation efficiency and start-site selection in lymphoma. J. Exp. Med. 216, 1509–1524 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Pospísek, M. & Valásek, L. Polysome profile analysis—yeast. Methods Enzymol. 530, 173–181 (2013).

    Article  PubMed  Google Scholar 

  58. Lee, J. H., Kim, H. S., Lee, S. J. & Kim, K. T. Stabilization and activation of p53 induced by Cdk5 contributes to neuronal cell death. J. Cell Sci. 120, 2259–2271 (2007).

    Article  CAS  PubMed  Google Scholar 

  59. Chang, P. M. et al. Transcriptome analysis and prognosis of ALDH isoforms in human cancer. Sci. Rep. 8, 2713 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Ono, M. et al. The expression and clinical significance of ribophorin II (RPN2) in human breast cancer. Pathol. Int. 65, 301–308 (2015).

    Article  CAS  PubMed  Google Scholar 

  61. Yin, Z. et al. Identification of ALDH3A2 as a novel prognostic biomarker in gastric adenocarcinoma using integrated bioinformatics analysis. BMC Cancer 20, 1062 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Duan, J. J., Cai, J., Guo, Y. F., Bian, X. W. & Yu, S. C. ALDH1A3, a metabolic target for cancer diagnosis and therapy. Int J. Cancer 139, 965–975 (2016).

    Article  CAS  PubMed  Google Scholar 

  63. Änkö, M. L. et al. The RNA-binding landscapes of two SR proteins reveal unique functions and binding to diverse RNA classes. Genome Biol. 13, R17 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Iwai, K. et al. Anti-tumor efficacy of a novel CLK inhibitor via targeting RNA splicing and MYC-dependent vulnerability. EMBO Mol. Med. 10, e8289 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Naro, C. et al. The centrosomal kinase NEK2 is a novel splicing factor kinase involved in cell survival. Nucleic Acids Res. 42, 3218–3227 (2014).

    Article  CAS  PubMed  Google Scholar 

  66. Esvan, Y. J. et al. Discovery of pyrido[3,4-g]quinazoline derivatives as CMGC family protein kinase inhibitors: design, synthesis, inhibitory potency and X-ray co-crystal structure. Eur. J. Med. Chem. 118, 170–177 (2016).

    Article  CAS  PubMed  Google Scholar 

  67. Tazarki, H. et al. New pyrido[3,4-g]quinazoline derivatives as CLK1 and DYRK1A inhibitors: synthesis, biological evaluation and binding mode analysis. Eur. J. Med. Chem. 166, 304–317 (2019).

    Article  CAS  PubMed  Google Scholar 

  68. Wang, J. et al. Spatiotemporal dynamics of intra-tumoral dependence on NEK2–EZH2 signaling in glioblastoma cancer progression. Preprint at bioRxiv https://doi.org/10.1101/2020.12.01.405696 (2020).

  69. Anufrieva, K. S. et al. Therapy-induced stress response is associated with downregulation of pre-mRNA splicing in cancer cells. Genome Med. 10, 49 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Etienne-Manneville, S. & Hall, A. Rho GTPases in cell biology. Nature 420, 629–635 (2002).

    Article  CAS  PubMed  Google Scholar 

  71. Zhou, Z. & Fu, X. D. Regulation of splicing by SR proteins and SR protein-specific kinases. Chromosoma 122, 191–207 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Lavergne, J. P., Conquet, F., Reboud, J. P. & Reboud, A. M. Role of acidic phosphoproteins in the partial reconstitution of the active 60 S ribosomal subunit. FEBS Lett. 216, 83–88 (1987).

    Article  CAS  PubMed  Google Scholar 

  73. Kim, S. J. & Strich, R. Rpl22 is required for IME1 mRNA translation and meiotic induction in S. cerevisiae. Cell Div. 11, 10 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  74. O’Leary, M. N. et al. The ribosomal protein Rpl22 controls ribosome composition by directly repressing expression of its own paralog, Rpl22l1. PLoS Genet. 9, e1003708 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Goudarzi, K. M. & Lindström, M. S. Role of ribosomal protein mutations in tumor development (Review). Int. J. Oncol. 48, 1313–1324 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Bastide, A. & David, A. The ribosome, (slow) beating heart of cancer (stem) cell. Oncogenesis 7, 34 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Brumwell, A., Fell, L., Obress, L. & Uniacke, J. Hypoxia influences polysome distribution of human ribosomal protein S12 and alternative splicing of ribosomal protein mRNAs. RNA 26, 361–371 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Gutschner, T., Hämmerle, M. & Diederichs, S. MALAT1—a paradigm for long noncoding RNA function in cancer. J. Mol. Med. (Berl.) 91, 791–801 (2013).

    Article  CAS  Google Scholar 

  79. Han, Y. et al. Tumor-suppressive function of long noncoding RNA MALAT1 in glioma cells by downregulation of MMP2 and inactivation of ERK/MAPK signaling. Cell Death Dis. 7, e2123 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Li, Z. et al. Long non-coding RNA MALAT1 promotes proliferation and suppresses apoptosis of glioma cells through derepressing Rap1B by sponging miR-101. J. Neurooncol. 134, 19–28 (2017).

    Article  CAS  PubMed  Google Scholar 

  81. Damaghi, M. et al. Chronic acidosis in the tumour microenvironment selects for overexpression of LAMP2 in the plasma membrane. Nat. Commun. 6, 8752 (2015).

    Article  CAS  PubMed  Google Scholar 

  82. Corbet, C. et al. Acidosis drives the reprogramming of fatty acid metabolism in cancer cells through changes in mitochondrial and histone acetylation. Cell Metab. 24, 311–323 (2016).

    Article  CAS  PubMed  Google Scholar 

  83. Andreucci, E. et al. The acidic tumor microenvironment drives a stem-like phenotype in melanoma cells. J. Mol. Med. (Berl.) 98, 1431–1446 (2020).

    Article  CAS  Google Scholar 

  84. Hu, P. et al. Acidosis enhances the self-renewal and mitochondrial respiration of stem cell-like glioma cells through CYP24A1-mediated reduction of vitamin D. Cell Death Dis. 10, 25 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Tan, D. Q. et al. PRMT5 modulates splicing for genome integrity and preserves proteostasis of hematopoietic stem cells. Cell Rep. 26, 2316–2328 (2019).

    Article  CAS  PubMed  Google Scholar 

  86. Tam, B. Y. et al. The CLK inhibitor SM08502 induces anti-tumor activity and reduces Wnt pathway gene expression in gastrointestinal cancer models. Cancer Lett. 473, 186–197 (2020).

    Article  CAS  PubMed  Google Scholar 

  87. Eskens, F. A. et al. Phase I pharmacokinetic and pharmacodynamic study of the first-in-class spliceosome inhibitor E7107 in patients with advanced solid tumors. Clin. Cancer Res. 19, 6296–6304 (2013).

    Article  CAS  PubMed  Google Scholar 

  88. Harris, R. J. et al. Simultaneous pH–sensitive and oxygen-sensitive MRI of human gliomas at 3 T using multi-echo amine proton chemical exchange saturation transfer spin-and-gradient echo echo-planar imaging (CEST-SAGE-EPI). Magn. Reson. Med. 80, 1962–1978 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Ellingson, B. M. et al. Consensus recommendations for a standardized brain tumor imaging protocol in clinical trials. Neuro Oncol. 17, 1188–1198 (2015).

    PubMed  PubMed Central  Google Scholar 

  90. Yao, J. et al. Improving B0 correction for pH–weighted amine proton chemical exchange saturation transfer (CEST) imaging by use of k-means clustering and Lorentzian estimation. Tomography 4, 123–137 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Harris, R. J. et al. Simulation, phantom validation, and clinical evaluation of fast pH–weighted molecular imaging using amine chemical exchange saturation transfer echo planar imaging (CEST-EPI) in glioma at 3 T. NMR Biomed. 29, 1563–1576 (2016).

    Article  CAS  PubMed  Google Scholar 

  92. Pavlyukov, M. S. et al. Survivin monomer plays an essential role in apoptosis regulation. J. Biol. Chem. 286, 23296–23307 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Hu, Y. & Smyth, G. K. ELDA: extreme limiting dilution analysis for comparing depleted and enriched populations in stem cell and other assays. J. Immunol. Methods 347, 70–78 (2009).

    Article  CAS  PubMed  Google Scholar 

  94. Esser, C., Göttlinger, C., Kremer, J., Hundeiker, C. & Radbruch, A. Isolation of full-size mRNA from ethanol-fixed cells after cellular immunofluorescence staining and fluorescence-activated cell sorting (FACS). Cytometry 21, 382–386 (1995).

    Article  CAS  PubMed  Google Scholar 

  95. Krishan, A. Rapid flow cytofluorometric analysis of mammalian cell cycle by propidium iodide staining. Cell Biol. 66, 188–193 (1975).

    Article  CAS  Google Scholar 

  96. Belin, S. et al. Purification of ribosomes from human cell lines. Curr. Protoc. Cell Biol. https://doi.org/10.1002/0471143030.cb0340s49 (2010).

  97. Saei, A. A. et al. ProTargetMiner as a proteome signature library of anticancer molecules for functional discovery. Nat. Commun. 10, 5715 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Blanchette, M. et al. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res. 14, 708–715 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank all of our respective laboratory colleagues for helpful discussion. We are grateful to M. A. Nakano and Y. D. Nakano for input on manuscript writing and editing. This work was supported by grants from the Ministry of Science and Higher Education of the Russian Federation (for cell culture, 075-15-2020-773 to M.S.P. and M.I.S. and for LC-MS/MS analyses, 075-15-2019-1669 to V.O.S., G.P.A., K.S.A. and P.V.S.), Russian Science Foundation (for bioinformatics analyses, 22-15-00462 to V.O.S., K.S.A., G.P.A., P.V.S. and A.N.K., for ribosome fractionation, 21-64-00006 to O.A.D. and for cell culture work, 22-14-00234 to M.I.S.), Russian Foundation for Basic Research (19-34-90193 to K.S.A., 20-04-00804 to M.S.P. and 19-34-90102 to T.D.L.), Russian Federation (MD-4501.2021.1.4 to M.S.P.), Scholarship of the Russian Federation (SP-3815.2021.4 to V.O.S.), National Natural Science Foundation of China (81802502 to J.W.), National Cancer Institute (P50CA211015 and R01CA241927 to H.I.K., R01CA270027 and R21CA223757 to B.M.E. and R01CA201402 to I.N.), National Institute of Neurological Disorders and Stroke (R01NS121617 to H.I.K., R01NS107071 to I.N. and R01NS113631 to I.N.), Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (to H.I.K.), Adelson Medical Research Foundation (to H.I.K.), American Cancer Society RSG-15-003-01-CCE (to B.M.E.) and Department of Defense (CA200290 to B.M.E.). We also thank the Center for Precision Genome Editing and Genetic Technologies for Biomedicine of Federal Research and Clinical Center of Physical-Chemical Medicine for RNA-seq. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

M.S.P., M.I.S., H.I.K. and I.N. designed the experiments. M.S.P., T.D.L., T.E.A., S.B., V.O.S., J.W., D.E.A., G.P.A., C.L., V.V.T., P.V.S., Y.W., M.P.R., M.C. and T.F.K. performed the experiments. M.S.P., K.S.A., A.N.K. and G.P.A. analysed the data. Y.A.L., B.M.E. and I.N. collected the clinical samples. A.A.S., F.G. and P.M. established the FG1059 inhibitor. M.S.P., A.A.S., H.I.K., O.A.D. and I.N. wrote the manuscript.

Corresponding authors

Correspondence to Ichiro Nakano or Marat S. Pavlyukov.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Cell Biology thanks Justin Lathia and the other, anonymous, reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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 Proteomic and transcriptomic intratumoral heterogeneity of glioblastoma.

a, The principal component analysis of LC-MS/MS (left panel) and RNAseq (right panels) data obtained from GBM sphere lines derived from core (n = 4 different clones) and edge (n = 3 different clones) of the 1051 tumor. b, Correlation analysis of protein‐to‐RNA abundance in GBM sphere lines as in ‘a’. c, Correlation of protein composition of ribosomes with mRNA levels of the corresponding ribosomal genes. d, Correlation of protein composition of ribosomes with the differences in pre-mRNA splicing of the corresponding ribosomal genes. Proteins that are differentially included into ribosomes of GBM sphere lines with edge and core phenotype were determined by SILAC LC-MS/MS. Differences in mRNA levels and in pre-mRNA splicing were determined by RNA sequencing of the corresponding cells. Correlation coefficient, trend line and the most differentially present proteins/mRNAs are indicated. e, RT-PCR analysis RPL22L1 splicing in GBM cells from n = 3 different patients. PCR products were separated using PAAG electrophoresis. f, Sashimi plots demonstrating differences in splicing of RPL22L1 between GBM sphere lines with edge (157, 011, 025) and core (083, 028, 006) phenotype (cells isolated from n = 6 different patients). g, RT-PCR analysis of RPL22L1 splicing in different human cell lines. h, The principal component analysis of RNAseq data obtained from GBM sphere lines used in this study. Red – previously characterized GBM spheres with core phenotype, blue - previously characterized GBM spheres with edge phenotype, gray – sphere lines established in this study (n = 14 different patients). i, RT-PCR analysis of RPL22L1 isoform abundance in 267 cells cultivated for 5 days in normal (pH 7.4) or acidified (pH 6.0) medium. NMD inhibitor (NMDI-14) was added to a final concentration of 5 μM 24 hours before cells were collected for RNA purification.

Source data

Extended Data Fig. 2 Expression of RPL22L1 isoforms in human cancers.

a, Relative expression of RPL22L1 isoforms and SETD4 (previously described NMD target36) in Hela cells depleted for different NMD factors (experiment was performed in n = 3 biological replicates; data are mean ± SD). Raw RNAseq data were obtained from GSE152437. b, Riboseq and RNAseq read densities for RPL22L1 in U251 GBM cells (data were obtained from GSE141013 dataset). c, Polysome profile of 083 GBM spheres (upper panel) and RT-PCR analysis of RPL22L1 splicing (lower panel). d, Mass spectrometry identification of the peptide related to RPL22L1b isoform (data were obtained from PXD004023 dataset). e, Western blotting analysis of GBM spheres with edge (157, 025) and core (022, 083) phenotypes with different antibodies against RPL22L1.

Source data

Extended Data Fig. 3

Representative immunofluorescent staining of GBM cells predominantly expressing RPL22L1a (001, 157, 025) or RPL22L1b (006, 022, 083) isoform with antibodies against N-terminal part of RPL22L1. The red dotted line indicates areas of DAPI staining (borders of the nucleus).

Extended Data Fig. 4 Prognostic value of RPL22L1 isoforms.

a, Kaplan-Meier curve showing the overall survival of Kidney Renal Clear Cell Carcinoma (n = 532 different patients), Adrenocortical carcinoma (n = 79 different patients) and Uveal Melanoma (n = 80 different patients) patients subdivided based on the splicing of RPL22L1 (log-rank test). RNAseq data were obtained from TCGA database. b, Splicing of RPL22L1 in GBM patients with wild type and mutated IDH1 (left panel); in patients younger and older than 50 years (middle panel) and in patients with high and low level of MGMT promoter methylation (right panel). 14 different probes were used to assess methylation status, none of them showed statistically significant differences in RPL22L1 splicing. Date were obtained from TCGA database (n = 154 different patients; the line in the box is the median, the up and low of the box are the first and third quartiles, and the whiskers extend to 10th and 90th percentiles respectively). Higher psi values indicate higher percentage of RPL22L1b isoform. c, FACS analysis of cell cycle distribution of 267 cells stained with propidium iodide. Populations that were collected by cell sorting are indicated. d, qRT-PCR analysis of Ki67 expression in cell populations collected as in ‘c’. e, RT-PCR analysis of RPL22L1 splicing in cell populations collected as in ‘c’ (experiment was performed in n = 3 biological replicates). All quantitative data are mean ± SD, n.s. – non significant.

Source data

Extended Data Fig. 5 RPL22L1b facilitates GBM growth in low pH conditions.

a, RT-PCR analysis of RPL22L1 splicing in 001, 011 and 022 GBM spheres that were cultivated for 5 days in normal (pH 7.4) or acidified (pH 6.0) medium. b, Immunofluorescent staining of different areas of GBM tumor tissues from patient 1051 for RPL22L1 (green) and DNA (blue). Yellow and red arrows indicate cells with nuclear and cytoplasmic localization of RPL22L1 respectively. c, Colocalization analysis of RPL22L1 and DAPI staining for the same tumor areas as in ‘b’. Pearson’s R value is indicated. d, Representative immunofluorescent staining of 022 GBM cells overexpressing RPL22L1a, RPL22L1b or an empty vector with antibodies against N-terminal part of RPL22L1. e, FACS analysis of caspase 3/7 activity and SYTOX staining of 157 glioma spheres that were transduced with lentiviruses encoding RPL22L1a, RPL22L1b or an empty vector (control) and cultivated in normal (pH 7.4) or acidified medium (pH 6.4) for 8 days.

Source data

Extended Data Fig. 6 Differential splicing of RPL22L1 promotes GBM intratumoral heterogeneity.

a, Forward vs. side scatter plot of 020 GBM spheres overexpressing RPL22L1a or RPL22L1b. Cell size was determined using Countess II Automated Cell Counter (experiment was performed in n = 4 biological replicates). b, Representative confocal images of 157 glioma spheres that were first transduced with lentiviruses encoding RPL22L1a or RPL22L1b and subsequently transduced with lentiviruses encoding GFP or RFP. Next cells overexpressing RPL22L1a + RFP were mixed with cells overexpressing RPL22L1b + GFP and imaged 4 days later. c, Representative images of wound healing assay with 157 cells stably expressing RPL22L1a, RPL22L1b or an empty vector. d, Representative IHC staining for CD109 of mouse brain sections obtained 3 months after intracranial injection of 3·105 luciferase labeled 1051 glioma spheres overexpressing RPL22L1a or RPL22L1b (n = 2 mice per group). e, PAAG electrophoresis of recombinant RPL22L1a (a), or RPL22L1b (b), that were purified from E. coli. f, Enrichment analysis of proteins that were bound to recombinant His-tagged RPL22L1a (upper panel) or RPL22L1b (lower panel) and subsequently identified by LC-MS/MS.

Extended Data Fig. 7 Molecular functions RPL22L1 isoforms in GBM cells.

a, Enrichment analysis of proteins that were co-purified with Fc-tagged RPL22L1a (lower panel) or RPL22L1b (upper panel) and subsequently identified by LC-MS/MS. b, Enrichment analysis of mRNAs that were co-purified with Fc-tagged RPL22L1a (left panel) or RPL22L1b (right panel) and subsequently identified by RNA sequencing. c, KEGG database GSVA analysis of RNA sequencing data obtained from 157 cells overexpressing RPL22L1a, RPL22L1b or an empty vector (experiment was performed in two biological replicates). d, Enrichment analysis of proteins that were differentially present in 157 cells overexpressing RPL22L1a or RPL22L1b isoform as determined by SILAC LC-MS/MS. e, Enrichment analysis of alternative splicing events related to exon skipping (upper panel) or mutually exclusive exon inclusion (lover panel) detected in 157 cells stably expressing RPL22L1b compared to the control cells.

Extended Data Fig. 8 RPL22L1 isoforms regulate pre-mRNA splicing and mRNA translation in GBM cells.

a, Sashimi plots demonstrating differences in splicing of MDM4 between 157 GBM spheres overexpressing RPL22L1a, RPL22L1b or an empty vector. Number of reeds that confirms exon skipping or exon inclusion is indicated. b, qRT-PCR analysis of MALAT1 RNA stability in 1079, 022 and 011 GBM spheres overexpressing RPL22L1a or RPL22L1b isoforms. Cells were cultivated for 6 hours with Actinomycin D at final concentration of 10 μg/ml (experiment was performed in n = 3 biological replicates). c, Kaplan-Meier curve showing the disease-free survival of glioma patients subdivided based on the MALAT1 expression level (n = 338 different patients, log-rank test). Data were obtained from TCGA database. d, Representative fluorescence images demonstrating L-homopropargylglycine (HPG) incorporation into newly synthesized proteins in 157, 022 and 267 GBM sphere lines. HPG was detected by Alexa Fluor488 azide (green). Nucleus were visualized by DAPI (blue). Cells pretreated for 30 min with cycloheximide (100 µg/ml) were used as a control. e, Polysome profiles of 1079 GBM spheres stably expressing RPL22L1a or RPL22L1b (experiment was performed in n = 2 biological replicates). f, RNA-IP enrichment profiles of RPL22L1a, RPL22L1b and a control protein for TP53 gene. 157 cells overexpressing Fc-tagged proteins were used for the experiment. g, Correlation of ALDH3A2 and ALDH1A3 expression levels in glioma. Data were obtained from REMBRANDT (n = 354 different patients; left panel) or TCGA (n = 671 different patients; right panel) databases. All quantitative data are mean ± SD.

Source data

Extended Data Fig. 9 SRSF4 regulates RPL22L1 splicing.

a, SRSF4 CLIP-tag enrichment profile in Rpl22l1 RNA sequence. Data were obtained from E-MTAB-747 dataset. b, qRT-PCR analysis of RNAs that were co-purified with Fc-tagged SRSF4 (red), or a control protein (blue) with primers for RPL22L1 exon 3, RPL22L1 3’UTR or RPL22 (experiment was performed in n = 3 biological replicates). c, SRSF4 expression level in different regions of GBM tumor (n = 122 RNA samples obtained from n = 10 different patients, one-way ANOVA test, following Dunnett’s/Tukey’s posttest). Data were obtained from IVY GAP database. The line in the box is the median, the up and low of the box are the first and third quartiles, and the whiskers extend to 10th and 90th percentiles respectively. d, qRT-PCR analysis of SRSF4 expression in 006, 030 and 157 GBM cells transduced with lentiviruses encoding non-target shRNA (shNT) or two different shRNAs against SRSF4 (48shSRSF4 and 49shSRSF4). e, Quantification of RPL22L1 splicing differences in 030 (upper) and 157 (lower) GBM cells transduced with lentiviruses as in ‘d’ (experiment was performed in n = 3 biological replicates). Higher psi values indicate higher percentage of RPL22L1b isoform. f, Kaplan-Meier curve showing the overall survival of glioblastoma patients (n = 179) subdivided based on the SRSF4 expression level (log-rank test). Data were obtained from REMBRANDT database. g, RT-PCR analysis of RPL22L1 splicing in 011 and 1079 glioma spheres that were left untreated (Un) or treated for 24 hours with CMP3a (10 μM), EY404 (10 μM), Fg1059 (10 μM), Cisplatin (10 μM), TMZ (100 μM) or Pladienolide B (1 μM). h, Enrichment analysis of the proteins that were differentially phosphorylated (more than 10 fold differences) in 157 GBM cells after 3 and 12 hours of incubation with 3 μM of FG1059 as opposed to untreated cells (experiment was performed in n = 2 biological replicates). Reactom and InterPro databases were used to calculate enrichment. All quantitative data are mean ± SD.

Source data

Extended Data Fig. 10 FG1059 induce apoptosis of GBM cells.

a, In vitro cell viability assay of 022, 157 and 1079 GBM spheres stably expressing RPL22L1a, RPL22L1b or an empty vector. Cells were treated with different concentrations of FG1059 for 5 days (experiment was performed in n = 6 biological replicates; data are mean ± SD). b, FACS analysis of caspase 3/7 activity and SYTOX staining of 157 cells treated with DMSO; 0.2 μM of FG1059; 20 μM of TMZ or with both compounds simultaneously for 24 hours. c, FACS analysis of caspase 3/7 activity and SYTOX staining of 157 cells treated with DMSO; 50 nM of FG1059; 50 nM of Pladienolide B or with both compounds simultaneously for 24 hours. d, Kaplan-Meier curve showing the overall survival of glioma (n = 509 different patients; left panel) and glioblastoma (n = 152 different patients; right panel) patients subdivided based on the RPL22 expression level (log-rank test). Data were obtained from TCGA database. e, Flow cytometry gating used for apoptosis assay (left panel) and for CD133 or HPG staining (right panel).

Source data

Supplementary information

Source data

Source Data Fig. 2

Unprocessed western blots and/or gels.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 3

Unprocessed western blots and/or gels.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 6

Unprocessed western blots and/or gels.

Source Data Fig. 7

Statistical source data.

Source Data Fig. 7

Unprocessed western blots and/or gels.

Source Data Fig. 8

Statistical source data.

Source Data Fig. 8

Unprocessed western blots and/or gels.

Source Data Extended Data Fig. 1

Unprocessed western blots and/or gels.

Source Data Extended Data Fig. 2

Unprocessed western blots and/or gels.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 4

Unprocessed western blots and/or gels.

Source Data Extended Data Fig. 5

Unprocessed western blots and/or gels.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 9

Unprocessed western blots and/or gels.

Source Data Extended Data Fig. 10

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

Verify currency and authenticity via CrossMark

Cite this article

Larionova, T.D., Bastola, S., Aksinina, T.E. et al. Alternative RNA splicing modulates ribosomal composition and determines the spatial phenotype of glioblastoma cells. Nat Cell Biol 24, 1541–1557 (2022). https://doi.org/10.1038/s41556-022-00994-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41556-022-00994-w

This article is cited by

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