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

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

Aliaksandra Radzisheuskaya et al. Nat Struct Mol Biol. 2019 Nov.

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.

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Conflict of interest statement

Competing interests

The authors declare no competing interests

Figures

Extended Data Fig. 1
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, qRT-PCR 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, qRT-PCR 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, qRT-PCR 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
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
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, qRT-PCR 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
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
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
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
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.

Figure 1.
Figure 1.. The catalytic activity of PRMT5 is required for proliferation of mouse and human MLL-AF9-rearranged AML cells.

a, qRT-PCR analysis of total and endogenous PRMT5 expression in THP-1-cdCas9-KRAB-stuffer or wtPRMT5 cells transduced with either a non-targeting (NegCtrl) sgRNA or two sgRNAs against PRMT5. The values are normalized to RPLP0 and shown as mean ±SD (n=3 technical replicates, * is p < 0.05, **** is p < 0.0001, “ns” is not significant according to Sidak’s multiple comparisons test). b, Western blot analysis of PRMT5 and B-actin expression and symmetrical arginine dimethylation (SDMA) levels in THP-1-cdCas9-KRAB-stuffer or wtPRMT5 cells transduced with either a non-targeting (NegCtrl) sgRNA or two sgRNAs against PRMT5 six days after transduction. Bar chart shows quantification of protein levels relative to a loading control. c, Growth curves of THP-1-cdCas9-KRAB-stuffer or wtPRMT5 cells transduced with either a non-targeting (NegCtrl) sgRNA or two sgRNAs against PRMT5. X-axis indicates number of days after transduction. d, qRT-PCR analysis of total and endogenous PRMT5 expression in THP-1-cdCas9-KRAB-stuffer or cdPRMT5 cells transduced with either a non-targeting (NegCtrl) sgRNA or two sgRNAs against PRMT5. The values are normalized to RPLP0 and shown as mean ±SD (n=3 technical replicates, *** is p < 0.001, **** is p < 0.0001 according to Sidak’s multiple comparisons test). e, Western blot analysis of PRMT5 and GAPDH expression and symmetrical arginine dimethylation (SDMA) levels in THP-1-cdCas9-KRAB-stuffer or cdPRMT5 cells transduced with either a non-targeting (NegCtrl) sgRNA or two sgRNAs against PRMT5 three days after transduction. Bar chart shows quantification of protein levels relative to a loading control. f, Growth curves of THP-1-cdCas9-KRAB-stuffer or cdPRMT5 cells transduced with either a non-targeting (NegCtrl) sgRNA or two sgRNAs against PRMT5. X-axis indicates number of days after transduction. g, Growth curves of THP-1 cultured in DMSO or with the PRMT5 inhibitor EPZ015666 at different concentrations. X-axis indicates number of days after addition of the compound. The experiments in a-c, g were repeated three times independently with similar results. The experiments in d-f were repeated twice with similar results. Source data for a-g are available online. The uncropped westerns blots for b and e are shown in the Source Data.

Figure 2.
Figure 2.. Proteome and methylome profiling identify novel PRMT5 substrates in human AML cells.

a, Outline of the proteome and methylome profiling strategies in THP-1-cdCas9-KRAB cells transduced with a non-targeting sgRNA (NegCtrl) or two independent sgRNAs against PRMT5 (see methods for details). MMA = monomethylated arginine, SDMA = symmetrically dimethylated arginine, high pH RP = high pH reverse phase chromatography, PSMs = peptide spectrum matches. b, Heatmap of 2962 differentially expressed proteins (q-value ≤ 0.05 in both sgRNAs, limma test, with p-values adjusted by Storey method). c, Boxplot representing relative protein abundance of PRMT5 and its co-factor WDR77 in THP-1-cdCas9-KRAB cells transduced with non-targeting sgRNA (NegCtrl) or two independent sgRNAs against PRMT5. Boxplot summary: outliers (points), 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). For PRMT5-depleted cells, n=3 independently transduced samples. For wild-type cells, n=4 independently transduced samples. The difference between the negative control and each of the knockdown sgRNA is statistically significant (q-value < 0.05, limma test, with p-values adjusted by Storey method). d, Gene Ontology-based functional classification of 2962 up- and downregulated proteins in THP-1 cells following PRMT5 knockdown (two-sided Fisher’s exact test, FDR-adjusted p-value < 0.05). The nodes represent significantly enriched protein sets, node size is proportional to the number of members in a protein set, and color intensity reflects the q-value. Edges indicate the protein overlap between the nodes with thicker edges indicating higher degree of overlap. Orange edges illustrate upregulated categories, green – downregulated. Functionally related protein sets are clustered, numbered and named. Blue color in half circles indicates no enriched categories. e, Venn diagram representing the number of methylated peptides identified using the strategies outlined in Fig.2a. f, An example of normalization of the arginine methylation site quantified against the protein level. 681R and 696R methylation sites of SUPT5H were quantified as decreasing upon PRMT5 knockdown, while the SUPT5H protein itself was, conversely, slightly upregulated. Hence, normalization results in the increased fold change for methylation site quantification. Rme = arginine methylation. Boxplot summary as in c. For PRMT5-depleted cells, n=3 independently transduced samples. For wild-type cells, n=4 independently transduced samples. g, Heatmap of 420 differentially methylated sites. (q-value ≤ 0.1 in both sgRNAs, limma test, with p-values adjusted by Storey method). h, Enrichment map of the Gene Ontology-enriched protein sets across 61 identified potential PRMT5 targets. Representation and statistics as in panel d. The proteomics experiments were performed using three independently transduced samples of PRMT5-depleted cells (two independent sgRNAs) and four independently transduced samples of wild-type cells. Source data are available in Supplementary Table 1.

Figure 3.
Figure 3.. Validation of the essential PRMT5 substrates.

a, Distributions of abundances of unmethylated and methylated peptide forms after the incubation with or without recombinant PRMT5-WDR77 complex. Only the peptides belonging to the confirmed PRMT5 substrates are shown here. b, PRMT5 methylation motif predicted using an iceLogo tool. Y axis represents the difference between the frequency of an amino acid in a sample set and the reference set (human proteome). c, CRISPRi competition assays to confirm essentiality of CCT4, CCT7, PNN, SNRPB, SRSF1, TAF15, SUPT5H, SFPQ, RPS10 and CPSF6. THP-1-cdCas9-KRAB cells were transduced with the sgRNAs against the genes of interest and the percentage of sgRNA-transduced (BFP-positive) cells was measured over time. An sgRNA targeting POLR1D was used as a positive control and a non-targeting sgRNA (NegCtrl) was used as a negative control. d, CRISPRko competition assays to confirm essentiality of ALYREF and WDR33. THP-1-wtCas9 cells were transduced with lentiviruses expressing the sgRNAs against the genes of interest and the percentage of sgRNA-transduced (BFP-positive) cells was measured over time. An sgRNA targeting MCM2 was used as a positive control and a non-targeting sgRNA (NegCtrl) was used as a negative control. e, Enrichment map of the Gene Ontology-enriched protein sets across 11 validated essential substrates of PRMT5 (two-sided Fisher’s exact test, FDR-adjusted p-value < 0.05). Nodes represent significantly enriched protein sets, node size is proportional to the number of members in a protein set, and color intensity depends on the q-value. Edges indicate the protein overlap between the nodes with thicker edges indicating higher overlap between the nodes. Functionally related protein sets are clustered, numbered and named. The experiments in a, c, d were repeated twice with similar results. Source data for a are available in Supplementary Table 1. Source data for c,d are available online.

Figure 4.
Figure 4.. PRMT5 depletion leads to changes in alternative splicing in human AML cells.

a, DEXSeq and limma-diffSplice algorithms show the differential usage of 415 RIs in the transcriptome of THP-1 cells following PRMT5 knockdown (more than 2-fold changes, p-value < 0.002, q-value < 0.05). b, Volcano plot demonstrating the differential usage of retained introns in the transcriptome of THP-1 cells upon PRMT5 knockdown (total 109530 events). RIs were included in differential analysis as expressed exons, and 336 diffRIs are shown using dark orange squares. The vertical dashed lines represent two-fold differences between the PRMT5 knockdown and wild type cells and horizontal dashed line shows the FDR adjusted q-value threshold of 0.05. These results were generated using DEXSeq algorithm, and very similar results were observed with limma-diffSplice approach. c, Volcano plot demonstrating the differential usage of EEJs in the transcriptome of THP-1 cells upon PRMT5 knockdown (total 76434 exon-exon junctions). 430 DiffEEJs are shown by dark orange squares. The vertical dashed lines represent two-fold differences between the PRMT5 knockdown and wild type cells, and horizontal dashed line shows the FDR adjusted q-value threshold of 0.05. These results were generated using limma-diffSplice algorithm. d, The majority of the identified non-diffEEJs or diffEEJs are annotated in the Ensembl database (GRCh38.p7 assembly of human genome, release 85, July 2016), and only small fractions of EEJs are new junctions. The following numbers of EEJs were analyzed: non-diffEEJs – 76004, diffEEJs log2FC < −1 – 184, diffEEJs log2FC > 1 – 246). e, Classification of non-diffEEJs and diffEEJs according to main modes, or types, of alternative splicing. The identified EEJs were divided in three sub-sets: i) non-diffEEJs, ii) diffEEJs with prevalence in the control cells (logFC < –1), and iii) diffEEJs with prevalence in the cells with PRMT5 knockdown (logFC > 1). Classification was carried out using Ensembl-based models of hypothetical non-alternative preRNAs of human genes. Numbers show the percentage of EEJs assigned to a particular mode of alternative splicing. f, Venn diagram demonstrating minimal overlap between the lists of differentially expressed genes (genes diffExpr), genes with diffRIs (genes diffRIs) and genes with diffEEJs (genes diffEEJs). g, Density diagrams of SRSF1 motif frequency at the 5’ and 3’ splice sites of the differential and non-differential EEJs (dynamic thresholding). h-i, Median absolute numbers of SRSF1 motifs in differential and non-differential EEJs (h) and RIs (i) (fixed thresholding, two-sided Mann-Whitney U test). Boxplot summary (h,i): 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). The following numbers of splicing events were compared: non-diffEEJs – 76004, difEEJs downregulated – 184, diffEEJs upregulated – 246, non-diffRIs – 1540, diffRIs – 336. j, Density diagrams of SFPQ motif frequency at the 5’ and 3’ splice sites of the differential and non-differential EEJs (dynamic thresholding). k-l, Venn diagrams of the overlapping lists of the differentially used EEJs (k) and RIs (l) identified in THP-1 and U-87 MG cells upon PRMT5 KD. The splicing analysis experiments were performed using three independently transduced samples of each sgRNA. Source data are available in Supplementary Table 2.

Figure 5.
Figure 5.. PRMT5 loss induces alternative splicing and reduction in protein level of multiple essential genes.

a, Venn diagram showing that among the 826 differentially spliced genes in the PRMT5 KD cells, 88 also exhibited change in their total protein levels, of which 74 proteins were downregulated and 14 proteins upregulated. b, Competition assays of THP-1-wtCas9 cells transduced with the sgRNAs targeting the genes of interest. The percentage of sgRNA-transduced (BFP-positive) cells was measured over time. An sgRNA against MCM2 was used as a positive control and a non-targeting sgRNA (NegCtrl) was used as a negative control. c, Barplot representing changes in protein abundance of the selected candidates upon the knockdown of PRMT5. The values are mean±SD. For PRMT5-depleted cells, n=3 independently transduced samples. For wild-type cells, n=4 independently transduced samples. The values are shown relative to NegCtrl sgRNA (q-value < 0.05, limma test, with p-values adjusted by Storey method). d, qRT-PCR validation of the identified differential retained intron events. The values represent mean±SD of two independent transductions. * is p-value < 0.1, “ns” is not significant according to an unpaired t test. e, qRT-PCR validation of the identified differential EEJ. The values represent mean±SD of two independent transductions. The differential EEJ of the GRIPAP1 gene was added as an example of an exon skipping event, which is a predominant mode of the upregulated alternative splicing events in the PRMT5 KD cells. * is p-value < 0.1, “ns” is not significant according to an unpaired t test. f, Scatter plot demonstrating the correlation between the log2FC of the differential splicing events obtained using RNA-seq (n=3 independent transductions) and qPCR approaches (n=2 independent transductions) (two-sided Student t-test). g-h, Schematic representation of the differential alternative splicing events in PDCD2 (g) and PNKP (h) RNA transcripts. In (g), EEJs are designated by the letter J and numbered. The concomitant statistics for these junctions are as follows: J2 (log2 FC = 0.22, q = 0.846), J3 (log2 FC = 1.23; q = 2.53E-02), J4 (log2 FC = −0.76; q = 0.034) and J5 (log2 FC = −0.31; q = 0.708). The differential splicing events are highlighted in purple. i, qRT-PCR analysis of the levels of PRMT5, constitutive PNKP EEJ (PNKP cEEJ) and PNKP retained intron (PNKP RI) in the cells transduced either with a negative control or a sgRNA targeting PRMT5 and treated with emetine or water for 3 hours. “PNKP RI norm” stands for the expression of the PNKP RI normalized to the levels of PNKP cEEJ. The values are mean ± SD (n=3 technical replicates, * is p-value < 0.01, ** is p-value < 0.005, *** is p-value < 0.001, **** is p-value < 0.0001, “ns” is not significant according to Sidak’s multiple comparisons test). The experiment in b was repeated three times independently with similar results. The experiments in d, e were performed in two independent viral transductions. The experiment in i was performed in three independent viral transductions, which where pooled prior to qRT-PCR analysis. Source data for b, d-f, i are available online. Source data for c, f are available in Supplementary Table 1 and 2, respectively.

Figure 6.
Figure 6.. Arginine methylation of SRSF1 is functionally important for cell survival.

a, Competition assays to assess the functionality of the R-to-K mutants of the essential PRMT5 substrates. Cell lines stably expressing either the wild type or mutant versions of each substrate were transduced with an sgRNA against the substrate of interest. After the transduction, the percentage of BFP positive (sgRNA-expressing) cells was monitored over time. b, qRT-PCR analysis of the endogenous (3’UTR) and exogenous SRSF1 expression in BFP and mCherry double positive cells co-transduced with an sgRNA targeting SRSF1 and either SRSF1 WT or mutant cDNA or a stuffer construct. The values are normalized to RPLP0 and shown as mean ±SD (n=2 technical replicates, ** is p-value < 0.01, *** is p-value < 0.001, **** is p-value < 0.0001, “ns” is non-significant according to Sidak’s multiple comparisons test). The experiments in a and b were repeated twice with similar results. The source data are available online.

Figure 7.
Figure 7.. PRMT5 depletion impacts SRSF1 binding to mRNAs and proteins.

a, Differential binding of SRSF1 to mRNAs upon PRMT5 KD (differentially bound mRNAs are represented by orange color, q-value < 0.05). mRNAs of 13754 genes were identified of which 4459 were differentially bound. b, Overlap of differentially spliced genes with the genes, which mRNAs are differentially bound by SRSF1 upon PRMT5 KD. c, Distribution of differentially spliced genes among the genes with differentially or non-differentially bound mRNAs. d, Differential binding of SRSF1 to proteins upon PRMT5 KD. Dashed lines indicate the chosen thresholds of 2-fold change and q-value of 0.05. Total of 350 binding partners were quantified, and 162 significantly differentially bound proteins are indicated with triangles. The most enriched functional groups of proteins among the differential interactors are indicated with color. e, Proposed model for the essential function of PRMT5. PRMT5 methylates SRSF1 at three arginine sites, which are important for the function of SRSF1 in splicing regulation. Loss of SRSF1 methylation leads to altered binding of SRSF1 to mRNA and proteins, differential alternative splicing of multiple essential genes and, consequently, cell death. RIP-sequencing and SRSF1 IP-MS experiments were performed using 3 independently transduced samples of each sgRNA. The source data for the figure is available in Supplementary Tables 3 and 4.

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