PRMT5 methylome profiling uncovers a direct link to splicing regulation in acute myeloid leukemia - Nature Structural & Molecular Biology
- ️Helin, Kristian
- ️Mon Oct 14 2019
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.
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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).
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Authors and Affiliations
Biotech Research and Innovation Centre, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
Aliaksandra Radzisheuskaya, Eugenia Lorenzini, Daria Shlyueva & Kristian Helin
The Danish Stem Cell Center, University of Copenhagen, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
Aliaksandra Radzisheuskaya, Eugenia Lorenzini, Daria Shlyueva & Kristian Helin
Cell Biology Program and Center for Epigenetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Aliaksandra Radzisheuskaya & Kristian Helin
Department of Biochemistry and Molecular Biology, VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense, Denmark
Pavel V. Shliaha, Sergey Kovalchuk, Vladimir Gorshkov & Ole N. Jensen
Microchemistry and Proteomics Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Pavel V. Shliaha & Ronald C. Hendrickson
Department of Genetics, Faculty of Biology, Belarusian State University, Minsk, Belarus
Vasily Grinev
Laboratory of Bioinformatic Methods for Combinatorial Chemistry and Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
Sergey Kovalchuk
Authors
- Aliaksandra Radzisheuskaya
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- Pavel V. Shliaha
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- Vasily Grinev
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- Eugenia Lorenzini
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- Sergey Kovalchuk
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- Daria Shlyueva
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Contributions
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.
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Correspondence to Kristian Helin.
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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.
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.
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.
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.
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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). https://doi.org/10.1038/s41594-019-0313-z
Received: 11 April 2019
Accepted: 03 September 2019
Published: 14 October 2019
Issue Date: November 2019
DOI: https://doi.org/10.1038/s41594-019-0313-z