PRMT5 methylome profiling uncovers a direct link to splicing regulation in acute myeloid leukemia

Article metrics

Abstract

Protein arginine methyltransferase 5 (PRMT5) has emerged as a promising cancer drug target, and three PRMT5 inhibitors are currently in clinical trials for multiple malignancies. In this study, we investigated the role of PRMT5 in human acute myeloid leukemia (AML). Using an enzymatic dead version of PRMT5 and a PRMT5-specific inhibitor, we demonstrated the requirement of the catalytic activity of PRMT5 for the survival of AML cells. We then identified PRMT5 substrates using multiplexed quantitative proteomics and investigated their role in the survival of AML cells. We found that the function of the splicing regulator SRSF1 relies on its methylation by PRMT5 and that loss of PRMT5 leads to changes in alternative splicing of multiple essential genes. Our study proposes a mechanism for the requirement of PRMT5 for leukemia cell survival and provides potential biomarkers for the treatment response to PRMT5 inhibitors.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: The catalytic activity of PRMT5 is required for proliferation of mouse and human MLL-AF9-rearranged AML cells.
Fig. 2: Proteome and methylome profiling identify new PRMT5 substrates in human AML cells.
Fig. 3: Validation of the essential PRMT5 substrates.
Fig. 4: PRMT5 depletion leads to changes in alternative splicing in human AML cells.
Fig. 5: PRMT5 loss induces alternative splicing and reduction in protein level of multiple essential genes.
Fig. 6: Arginine methylation of SRSF1 is functionally important for cell survival.
Fig. 7: PRMT5 depletion impacts SRSF1 binding to mRNAs and proteins.

Data availability

Next-generation sequencing has been submitted to the Gene Expression Omnibus (accession number GSE129652). Proteomics data has been submitted to ProteomeXchange (accession number PXD013611). Source data for all the main Figures and Extended Data Figs. 1, 2, 4, 6, 7 are available with the paper online either as Source Data or in Supplementary Tables. All other data will be made available on request.

Code availability

GitHub project with the RNA-sequencing analysis code is available at: https://github.com/VGrinev/transcriptome-analysis/blob/master/TranscriptsFeatures. Any additional code will be provided upon request from the authors.

References

  1. 1.

    Larsen, S. C. et al. Proteome-wide analysis of arginine monomethylation reveals widespread occurrence in human cells. Sci. Signal 9, rs9 (2016).

  2. 2.

    Blanc, R. S. & Richard, S. Arginine methylation: the coming of age. Mol. Cell 65, 8–24 (2017).

  3. 3.

    Gayatri, S. & Bedford, M. T. Readers of histone methylarginine marks. Biochim. Biophys. Acta 1839, 702–710 (2014).

  4. 4.

    Li, X., Wang, C., Jiang, H. & Luo, C. A patent review of arginine methyltransferase inhibitors (2010–2018). Expert Opin. Ther. Pat. 29, 97–114 (2019).

  5. 5.

    Stopa, N., Krebs, J. E. & Shechter, D. The PRMT5 arginine methyltransferase: many roles in development, cancer and beyond. Cell. Mol. Life Sci. 72, 2041–2059 (2015).

  6. 6.

    Shailesh, H., Zakaria, Z. Z., Baiocchi, R. & Sif, S. Protein arginine methyltransferase 5 (PRMT5) dysregulation in cancer. Oncotarget 9, 36705–36718 (2018).

  7. 7.

    Kryukov, G. V. et al. MTAP deletion confers enhanced dependency on the PRMT5 arginine methyltransferase in cancer cells. Science 351, 1214–1218 (2016).

  8. 8.

    Marjon, K. et al. MTAP deletions in cancer create vulnerability to targeting of the MAT2A/PRMT5/RIOK1 axis. Cell Rep. 15, 574–587 (2016).

  9. 9.

    Mavrakis, K. J. et al. Disordered methionine metabolism in MTAP/CDKN2A-deleted cancers leads to dependence on PRMT5. Science 351, 1208–1213 (2016).

  10. 10.

    Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010).

  11. 11.

    Branscombe, T. L. et al. PRMT5 (Janus kinase-binding protein 1) catalyzes the formation of symmetric dimethylarginine residues in proteins. J. Biol. Chem. 276, 32971–32976 (2001).

  12. 12.

    Rho, J. et al. Prmt5, which forms distinct homo-oligomers, is a member of the protein-arginine methyltransferase family. J. Biol. Chem. 276, 11393–11401 (2001).

  13. 13.

    Friesen, W. J. et al. A novel WD repeat protein component of the methylosome binds Sm proteins. J. Biol. Chem. 277, 8243–8247 (2002).

  14. 14.

    Antonysamy, S. et al. Crystal structure of the human PRMT5:MEP50 complex. Proc. Natl Acad. Sci. USA 109, 17960–17965 (2012).

  15. 15.

    Burgos, E. S. et al. Histone H2A and H4 N-terminal tails are positioned by the MEP50 WD repeat protein for efficient methylation by the PRMT5 arginine methyltransferase. J. Biol. Chem. 290, 9674–9689 (2015).

  16. 16.

    Gilbert, L. A. et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154, 442–451 (2013).

  17. 17.

    Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

  18. 18.

    Thompson, A. et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75, 1895–1904 (2003).

  19. 19.

    McAlister, G. C. et al. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal. Chem. 86, 7150–7158 (2014).

  20. 20.

    Wang, T. et al. Gene essentiality profiling reveals gene networks and synthetic lethal interactions with oncogenic ras. Cell 168, 890–903.e15 (2017).

  21. 21.

    Braun, C. J. et al. Coordinated splicing of regulatory detained introns within oncogenic transcripts creates an exploitable vulnerability in malignant glioma. Cancer Cell 32, 411–426.e11 (2017).

  22. 22.

    Hamard, P.-J. et al. PRMT5 regulates DNA repair by controlling the alternative splicing of histone-modifying enzymes. Cell Rep. 24, 2643–2657 (2018).

  23. 23.

    Rengasamy, M. et al. The PRMT5/WDR77 complex regulates alternative splicing through ZNF326 in breast cancer. Nucleic Acids Res. 45, 11106–11120 (2017).

  24. 24.

    Koh, C. M. et al. MYC regulates the core pre-mRNA splicing machinery as an essential step in lymphomagenesis. Nature 523, 96–100 (2015).

  25. 25.

    Bezzi, M. et al. Regulation of constitutive and alternative splicing by PRMT5 reveals a role for Mdm4 pre-mRNA in sensing defects in the spliceosomal machinery. Genes Dev. 27, 1903–1916 (2013).

  26. 26.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  27. 27.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

  28. 28.

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

  29. 29.

    Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

  30. 30.

    Anders, S., Reyes, A. & Huber, W. Detecting differential usage of exons from RNA-seq data. Genome Res. 22, 2008–2017 (2012).

  31. 31.

    Hartley, S. W. & Mullikin, J. C. Detection and visualization of differential splicing in RNA-Seq data with JunctionSeq. Nucleic Acids Res. 44, e127 (2016).

  32. 32.

    Chandler, S. D., Mayeda, A., Yeakley, J. M., Krainer, A. R. & Fu, X. D. RNA splicing specificity determined by the coordinated action of RNA recognition motifs in SR proteins. Proc. Natl Acad. Sci. USA 94, 3596–3601 (1997).

  33. 33.

    Hall-Pogar, T., Liang, S., Hague, L. K. & Lutz, C. S. Specific trans-acting proteins interact with auxiliary RNA polyadenylation elements in the COX-2 3′-UTR. RNA 13, 1103–1115 (2007).

  34. 34.

    Melton, A. A., Jackson, J., Wang, J. & Lynch, K. W. Combinatorial control of signal-induced exon repression by hnRNP L and PSF. Mol. Cell. Biol. 27, 6972–6984 (2007).

  35. 35.

    Sinha, R. et al. Arginine methylation controls the subcellular localization and functions of the oncoprotein splicing factor SF2/ASF. Mol. Cell. Biol. 30, 2762–2774 (2010).

  36. 36.

    Das, S. & Krainer, A. R. Emerging functions of SRSF1, splicing factor and oncoprotein, in RNA metabolism and cancer. Mol. Cancer Res. 12, 1195–1204 (2014).

  37. 37.

    Twyffels, L., Gueydan, C. & Kruys, V. Shuttling SR proteins: more than splicing factors. FEBS J. 278, 3246–3255 (2011).

  38. 38.

    Zhang, Z. & Krainer, A. R. Involvement of SR proteins in mRNA surveillance. Mol. Cell 16, 597–607 (2004).

  39. 39.

    Kaushik, S. et al. Genetic deletion or small-molecule inhibition of the arginine methyltransferase PRMT5 exhibit anti-tumoral activity in mouse models of MLL-rearranged AML. Leukemia 32, 499–509 (2018).

  40. 40.

    Musiani, D. et al. Proteomics profiling of arginine methylation defines PRMT5 substrate specificity. Sci. Signal 12, eaat8388 (2019).

  41. 41.

    Christoforou, A. L. & Lilley, K. S. Isobaric tagging approaches in quantitative proteomics: the ups and downs. Anal. Bioanal. Chem. 404, 1029–1037 (2012).

  42. 42.

    Cáceres, J. F., Screaton, G. R. & Krainer, A. R. A specific subset of SR proteins shuttles continuously between the nucleus and the cytoplasm. Genes Dev. 12, 55–66 (1998).

  43. 43.

    Krainer, A. R., Conway, G. C. & Kozak, D. Purification and characterization of pre-mRNA splicing factor SF2 from HeLa cells. Genes Dev. 4, 1158–1171 (1990).

  44. 44.

    Krainer, A. R., Conway, G. C. & Kozak, D. The essential pre-mRNA splicing factor SF2 influences 5′ splice site selection by activating proximal sites. Cell 62, 35–42 (1990).

  45. 45.

    Ge, H. & Manley, J. L. A protein factor, ASF, controls cell-specific alternative splicing of SV40 early pre-mRNA in vitro. Cell 62, 25–34 (1990).

  46. 46.

    da Silva, M. R. et al. Splicing regulators and their roles in cancer biology and therapy. Biomed. Res. Int. 2015, 150514 (2015).

  47. 47.

    Xiang, S. et al. Phosphorylation drives a dynamic switch in serine/arginine-rich proteins. Structure 21, 2162–2174 (2013).

  48. 48.

    Aubol, B. E. et al. Processive phosphorylation of alternative splicing factor/splicing factor 2. Proc. Natl Acad. Sci. USA 100, 12601–12606 (2003).

  49. 49.

    Boutz, P. L., Bhutkar, A. & Sharp, P. A. Detained introns are a novel, widespread class of post-transcriptionally spliced introns. Genes Dev. 29, 63–80 (2015).

  50. 50.

    Jacob, A. G. & Smith, C. W. J. Intron retention as a component of regulated gene expression programs. Hum. Genet. 136, 1043–1057 (2017).

  51. 51.

    Weatheritt, R. J., Sterne-Weiler, T. & Blencowe, B. J. The ribosome-engaged landscape of alternative splicing. Nat. Struct. Mol. Biol. 23, 1117–1123 (2016).

  52. 52.

    Radzisheuskaya, A., Shlyueva, D., Müller, I. & Helin, K. Optimizing sgRNA position markedly improves the efficiency of CRISPR/dCas9-mediated transcriptional repression. Nucleic Acids Res. 44, e141 (2016).

Download references

Acknowledgements

We thank members of the Helin laboratory for discussions, S. Teed and H. Damhofer for technical assistance, I. Comet for advice on nuclear-cytoplasm fractionation and S. Fujisawa and the rest of the Molecular Cytology Core at the MSKCC for microscopy assistance. A.R. and D.S. were funded by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant (agreement nos. 659171 and 749362, respectively). The work in the Helin laboratory was supported by the Danish Cancer Society (grant no. R167-A10877), through a center grant from the NNF to the NNF Center for Stem Cell Biology (no. NNF17CC0027852), and through the Memorial Sloan Kettering Cancer Center Support Grant (no. NIH P30 CA008748). Experimental and computational proteomics work at SDU (P.S., V. Gorshkov, S.K. and O.N.J) was supported by the research infrastructure provided by the Danish National Mass Spectrometry Platform for Functional Proteomics (grant nos. PRO-MS and 5072-00007B) and the VILLUM Center for Bioanalytical Sciences (grant no. 7292). P.S. was supported by a postdoctoral fellowship from the Lundbeck Foundation (no. R231-2016-3093). S.K. was supported by a research grant from Independent Research Fund Denmark (grant no. 4181-00172B to O.N.J.). Research in the V. Grinev laboratory was supported in part by the Ministry of Education of the Republic of Belarus (grant no. 3.08.3 469/54).

Author information

A.R. and K.H. came up with the concept. A.R., P.V.S. and V.Grinev designed the methodology. A.R., P.V.S., V.Grinev, E.L., S.K., D.S. and V.Gorshkov carried out the investigation. The original draft was written by A.R. and K.H. Review and editing of the manuscript was done by all authors. The visualization was done by A.R., P.V.S. and V.Grinev. A.R., P.V.S, O.N.J. and K.H. acquired the funding. R.C.H., O.N.J and K.H. supervised the study.

Correspondence to Kristian Helin.

Ethics declarations

Competing interests

The authors declare no competing interests

Additional information

Peer review information Anke Sparmann was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 PRMT5 and WDR77 are required for the survival of mouse and human AML cells.

a, Overview of the CRISPR interference and knockout approaches. b, RT-qPCR analysis of PRMT5 expression in THP-1-cdCas9-KRAB cells transduced with a non-targeting (NegCtrl) sgRNA or two sgRNAs targeting PRMT5 (3 and 6 days post-transduction). The values are normalized to RPLP0 and shown as mean ± SD (n = 3, **** is p-value < 0.0001 using Sidak’s multiple comparisons test). c-d, Western blot analysis of PRMT5 and GAPDH (c) and symmetrical arginine dimethylation (SDMA) (d) levels in THP-1-cdCas9-KRAB cells transduced with a non-targeting sgRNA or two sgRNAs targeting PRMT5 (3 and 6 days post-transduction). Bar charts show quantification of protein levels relative to a loading control. e, Growth curves of THP-1-cdCas9-KRAB cells transduced with a non-targeting sgRNA or two sgRNAs targeting PRMT5. Here and below, X-axis indicates number of days after transduction. f, RT-qPCR analysis of PRMT5 expression in MOLM-13-cdCas9-KRAB cells transduced with a non-targeting sgRNA or two sgRNAs targeting PRMT5 (3 and 6 days post-transduction). The values are normalized to RPLP0 and shown as mean ± SD (n = 3, **** is p-value < 0.0001 using Sidak’s multiple comparisons test). g, Growth curves of MOLM-13-cdCas9-KRAB cells transduced with a non-targeting sgRNA or two sgRNAs targeting PRMT5. h, RT-qPCR analysis of PRMT5 expression in THP-1-cdCas9-KRAB cells transduced with a non-targeting sgRNA or two sgRNAs targeting WDR77 (3 and 6 days post-transduction). The values are normalized to RPLP0 and shown as mean ± SD (n = 3, **** is p-value < 0.0001 using Sidak’s multiple comparisons test). i) Growth curves of THP-1-cdCas9-KRAB cells transduced with a non-targeting sgRNA or two sgRNAs targeting WDR77. j, Competition assays of THP-1-wtCas9, MOLM-13-wtCas9, MONOMAC-6-wtCas9 and mouse Mll-Af9-wtCas9 cells transduced with a non-targeting sgRNA or sgRNAs targeting MCM2 (Mcm2) (positive control) or PRMT5 (Prmt5). The experiments were repeated at least twice with similar results. The uncropped western blots are presented in the Source Data. Source Data

Extended Data Fig. 2 Chemical inhibition of PRMT5 leads to growth defects in AML cells.

a, Western blot analysis of symmetrical arginine dimethylation (SDMA) levels and Vinculin in THP-1 cells treated with DMSO or different doses of PRMT5 inhibitor EPZ015666 at 6 days after the addition of a compound. Bar chart shows quantification of protein levels relative to a loading control. b, Growth curves of THP-1-cdCas9-KRAB-stuffer cells treated with DMSO or different doses of PRMT5 inhibitor EPZ015666. X-axis indicates number of days after addition of the compound. c, Growth curves of THP-1-cdCas9-KRAB-wtPRMT5 cells treated with DMSO or different doses of PRMT5 inhibitor EPZ015666. X-axis indicates number of days after addition of the compound. The experiments were repeated twice with similar results. The uncropped western blots are available in the Source Data. Source Data

Extended Data Fig. 3 Validation of the essential PRMT5 substrates.

a, 17 out of 62 potential PRMT5 substrates were chosen as potentially essential according to a previously published CRISPRko screen in THP-1 cells. Y axis represents log2FC of the relative abundance of sgRNA in the screen and -1.5 was chosen as a cut-off. b, Distributions of relative abundances of unmethylated and methylated peptide forms after the incubation with or without recombinant PRMT5-WDR77 complex. Only the peptides belonging to the unconfirmed PRMT5 substrates are shown here. c, RT-qPCR analysis of CCT4, CC7, PNN, SFPQ, SNRPB, SRSF1, SUPT5H, TAF15, CPSF6 and RPS10 expression demonstrates efficient knockdown of the genes upon CRISPRi sgRNA transduction (n = 3, * is p-value < 0.033, *** is p-value < 0.001, **** is p-value < 0.0001 according to the unpaired t test). The experiments were repeated twice with similar results.

Extended Data Fig. 4 Knockdown of PRMT5 leads to differential splicing in the transcriptome of THP-1 AML cells.

a, Two independent algorithms (DESeq2 and edgeR-limma) identified 2974 RIs in the transcriptome of THP-1 cells. b, In total 2923 of 45450 Cufflinks-assembled transcripts of the THP-1 cells contain DESeq2- or edgeR-limma-detected RIs. Of these, 2668 transcripts are common between the two algorithms. c, Density plot of the transcript abundance demonstrating that the transcripts with RIs (+ RIs) are highly expressed in the transcriptome of THP-1 cells comparing to RI-free (–RIs) ones. d, The knockdown of PRMT5 leads to differential usage of a subset of EEJs in the transcriptome of THP-1 cells. The differentially used EEJs were determined using two independent algorithms (limma-diffSplice and JunctionSeq) with moderate overlap between the results. e-g, SRSF1 (e), SRSF2 (f) and SRSF3 (g) motifs are significantly enriched both at the 5′ and 3′ splice sites of the differential EEJs (dynamic thresholding). h, SFPQ motif is not significantly enriched at the 5′ or 3′ splice sites of the differential EEJs (dynamic thresholding). i-j, Density diagrams of SRSF1 motif frequency at the 5’ and 3’ splice sites of the differential and non-differential EEJs in U-87 MG cells. Stars indicate statistically significant differences (p < 0.01) (dynamic thresholding). k-l, Median absolute numbers of SRSF1 motifs in differential and non-differential splicing events in U-87 MG cells (fixed thresholding). Boxplot summary (e-h, k, l): outliers (diamonds), minimum (lower whisker), first quartile (lower bound of box), median (horizontal line inside box), third quartile (upper bound of box), interquartile range (box), and maximum (upper whisker).

Extended Data Fig. 5 SRSF1 motif number is increased around the differential splicing sites of the selected essential candidate genes.

a, Median absolute numbers of SRSF1 motifs near all the splicing sites that do not change upon PRMT5 depletion and near the splicing sites that change upon PRMT5 KD in the selected essential candidate genes (FDPS, PDCD2, PNISR, PNKP, POLD1, POLD2, PPP1R7) (fixed thresholding). Boxplot summary: outliers (diamonds), minimum (lower whisker), first quartile (lower bound of box), median (horizontal line inside box), third quartile (upper bound of box), interquartile range (box), and maximum (upper whisker). b, Table summary of the identified SRSF1 binding sites in all the splicing events that change upon PRMT5 KD in the FDPS, PDCD2, PNISR, PNKP, POLD1, POLD2, PPP1R7 genes.

Extended Data Fig. 6 PRMT5 depletion doesn’t demonstrate detectable effects on SRSF1 cellular localization.

a, Western blot validation of SRSF1 antibody. Significant decrease in the signal observed after the SRSF1 knockdown, demonstrating antibody specificity. Bar chart shows quantification of protein levels relative to a loading control. b, Western blotting for SRSF1, Lamin B1 and GAPDH after cell transduction with either a negative control or a PRMT5 sgRNA and subsequent nuclear-cytoplasm fractionation. Lamin B1 and GAPDH were used as controls for successful fractionation into nuclear and cytoplasmic (cyto) fractions, respectively. Bar chart shows quantification of protein levels. c, Representative immunofluorescence images of HeLa cells transiently transfected with either triple-FLAG-tagged wild type, triple R-to-K or triple R-to-A mutant SRSF1 cDNAs driven by the CAG promoter. Scale bar = 10 μm. d, Representative immunofluorescence images of HeLa cells transiently transfected with either triple-FLAG-tagged wild type, triple R-to-K or triple R-to-A mutant SRSF1 cDNAs driven by the EF1a promoter. Scale bar = 10 μm. The experiments in the figure were repeated at least twice with similar results. The uncropped western blots are available in the Source Data. Source Data

Extended Data Fig. 7 PRMT5 depletion affects the binding of SRSF1 to mRNA and proteins.

a, Western blotting for SRSF1 and PRMT5 in the input and immunoprecipitation samples (either SRSF1 or IgG). Bar chart shows quantification of protein levels. b, RNA yield after RNA-immunoprecipitation and purification in three biological replicates of each sample. c, Heatmap of the methylated peptides identified for SRSF1 in the negative control and PRMT5 KD SRSF1 IP-MS samples. “aa” stands for amino acid. Each IP was performed in three biological replicates. The uncropped western blots are available in the Source Data. Source Data

Supplementary Information

Supplementary Information

Supplementary methods.

Reporting Summary

Supplementary Table 1

Proteomics analysis tables.

Supplementary Table 2

RNA-sequencing analysis tables.

Supplementary Table 3

Genes with mRNAs differentially bound to SRSF1 on PRMT5 depletion.

Supplementary Table 4

Identified SRSF1 interactors.

Supplementary Table 5

sgRNA sequences used in the study.

Supplementary Table 6

sgRNA sequences used in the study.

Source data

Source Data Fig. 1

Statistical Source Data

Source Data Fig. 1

Uncropped Western blots

Source Data Fig. 3

Statistical Source Data

Source Data Fig. 5

Statistical Source Data

Source Data Fig. 6

Statistical Source Data

Source Data Extended Data Fig. 1

Uncropped Western blots

Source Data Extended Data Fig. 2

Uncropped Western blots

Source Data Extended Data Fig. 6

Uncropped Western blots

Source Data Extended Data Fig. 7

Uncropped Western blots

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Radzisheuskaya, A., Shliaha, P.V., Grinev, V. et al. PRMT5 methylome profiling uncovers a direct link to splicing regulation in acute myeloid leukemia. Nat Struct Mol Biol 26, 999–1012 (2019) doi:10.1038/s41594-019-0313-z

Download citation