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

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.

Main

Arginine methylation is an ubiquitous protein posttranslational modification in mammals1, catalyzed by the protein arginine methyltransferase (PRMT) family that transfers a methyl group from S-adenosylmethionine to the guanidine nitrogen atom of arginine. There are three forms of methylated arginines in mammals: ω-NG-monomethylarginine, asymmetric ω-NG,NG-dimethylarginine and symmetric ω-NG,NG-dimethylarginine, and the PRMT enzymes are classified depending on the type of modification they generate2. Functionally, protein arginine methylation is known to affect binding interactions. The bulky methyl groups can prevent access to the potential hydrogen bond donors in arginine groups and, thereby, inhibit protein interactions. At the same time, arginine methylation is known to facilitate the interaction with Tudor domains on proteins3.

PRMT5 has recently emerged as a promising cancer drug target, and three PRMT5 inhibitors are currently in clinical trials for a range of solid and blood cancers4 (https://clinicaltrials.gov). PRMT5 was shown to act as an oncoprotein in multiple malignancies, conferring aggressiveness and promoting cell proliferation5,6. Moreover, several recent reports have demonstrated a selective sensitivity of cancers with 9p21 deletion to the knockdown (KD) of PRMT5, due to synthetic lethality with the deleted MTAP (methylthioadenosine phosphorylase) gene7,8,9. Since 9p21 is a very frequent deletion present in about 14% of all cancers10, PRMT5 inhibition represents an exciting therapeutic strategy for cancers with, in particular, this chromosomal aberration.

PRMT5 belongs to the class II arginine methyltransferases, as it catalyzes monomethylation and symmetrical dimethylation of arginines on proteins11,12. It acts in a complex with WDR77 (also known as MEP50 and WD45)13, responsible for proper orientation of the PRMT5 substrates14,15. Several nuclear and cytoplasmic substrates of PRMT5 have been reported, which are involved in different cellular processes, including transcription, DNA damage response, splicing, translation and cell signaling5,6. However, further studies are required to understand the mechanism by which PRMT5 contributes to tumorigenesis and normal cellular physiology. In this study, we aimed at identifying substrates regulated by PRMT5, which are essential for cancer cell proliferation.

Results

The catalytic activity of PRMT5 is required for proliferation of MLL-AF9-rearranged AML cells

To assess the requirement for PRMT5 expression in AML cells, we used CRISPR interference (CRISPRi) and CRISPR knockout (CRISPRko) (Extended Data Fig. 1a). For CRISPRi, the cells were transduced with a lentivirus constitutively expressing the catalytically dead Cas9 (cdCas9) protein fused to a KRAB repression domain16,17. On the transduction of the THP-1-cdCas9-KRAB cells with two independent single-guide RNAs (sgRNAs) complementary to the PRMT5 transcription start site, efficient PRMT5 gene repression was observed (Extended Data Fig. 1b,c). This led to decreased levels of global symmetrical arginine dimethylation (Extended Data Fig. 1d) as well as substantial cell proliferation defects (Extended Data Fig. 1e). A similar effect was observed using MOLM-13-cdCas9-KRAB (Extended Data Fig. 1f,g). Using a similar setup, we also confirmed the requirement of the PRMT5 co-factor WDR77 for the growth of AML cells (Extended Data Fig. 1h,i). The requirement for PRMT5 for cell proliferation was also validated in human THP-1, MOLM-13, MONOMAC-6 and mouse MLL-AF9-wtCas9 leukemia cells using the CRISPRko system (Extended Data Fig. 1j). Taken together, these data demonstrate that PRMT5 depletion leads to growth inhibition of AML cells.

To investigate whether the enzymatic activity of PRMT5 is important for its function in human AML, we established THP-1-cdCas9-KRAB cell lines stably overexpressing either wild type (wt) or catalytically dead (cd) versions of PRMT5. Next, we transduced them with lentiviruses expressing sgRNAs that bind the PRMT5 promoter and together with the cdCas9-KRAB induce the KD of the endogenous PRMT5 locus. While the exogenously expressed wtPRMT5 complementary DNA induced complete rescue of global symmetrical arginine dimethylation levels and cell growth (Fig. 1a–c), cdPRMT5 conferred a dominant negative phenotype (Fig. 1d–f). In particular, its expression led to further decrease in arginine methylation, when the Stuffer cells demonstrate only a slight decrease (Fig. 1e). Moreover, the effect of knocking down endogenous PRMT5 on cell proliferation was stronger in the cells expressing cdPRMT5 (Fig. 1f). Consistently, we found that treatment of THP-1 cells with the specific PRMT5 inhibitor (EPZ015666) decreases global levels of symmetrical arginine dimethylation (Extended Data Fig. 2a) and negatively impacts cell proliferation (Fig. 1g), further confirming the requirement of the enzymatic activity of PRMT5 for cell growth. Finally, exogenous PRMT5 overexpression increased cell resistance to inhibitor treatment, demonstrating the specificity of PRMT5 inhibition (Extended Data Fig. 2b,c).

Fig. 1: The catalytic activity of PRMT5 is required for proliferation of mouse and human MLL-AF9-rearranged AML cells.
figure1

a, RT–qPCR analysis of total and endogenous PRMT5 expression in THP-1-cdCas9-KRAB-stuffer or wtPRMT5 cells transduced with either a nontargeting sgRNA (sgNegCtrl) or two sgRNAs against PRMT5 (sgPRMT5-1 and sgPRMT5-2). The values are normalized to RPLP0 and shown as mean ± s.d. (n = 3 technical replicates, *P < 0.05, ****P < 0.0001, NS, not significant according to Sidak’s multiple comparisons test). b, Western blot analysis of PRMT5 and B-actin expression and SDMA levels in THP-1-cdCas9-KRAB-stuffer or wtPRMT5 cells transduced with either a nontargeting sgRNA or two sgRNAs against PRMT5 6 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 nontargeting sgRNA or two sgRNAs against PRMT5. The x axis indicates number of days after transduction. d, RT–qPCR analysis of total and endogenous PRMT5 expression in THP-1-cdCas9-KRAB-stuffer or cdPRMT5 cells transduced with either a nontargeting sgRNA or two sgRNAs against PRMT5. The values are normalized to RPLP0 and shown as mean ± s.d. (n = 3 technical replicates, ***P < 0.001, ****P < 0.0001 according to Sidak’s multiple comparisons test). e, Western blot analysis of PRMT5 and GAPDH expression and SDMA levels in THP-1-cdCas9-KRAB-stuffer or cdPRMT5 cells transduced with either a nontargeting sgRNA or two sgRNAs against PRMT5 3 d 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 nontargeting sgRNA or two sgRNAs against PRMT5. The 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. The x axis indicates number of days after addition of the compound. The experiments in ac,g were repeated three times independently with similar results. The experiments in df were repeated twice with similar results. Source data for ag are available online. The uncropped westerns blots for b and e are shown in the Source Data.

Source Data

Source Data

Identification of new PRMT5 substrates in human AML cells

Given the essential nature of the enzymatic activity of PRMT5, we performed systematic identification of its substrates by liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based proteomics with the use of an isobaric mass tag labeling approach for quantitation18, as outlined in Fig. 2a. Ten samples were co-analyzed in the TMT10plex experiment: four biological replicates of cells transduced with a nontargeting sgRNA and three biological replicates of cells transduced with two independent sgRNAs inducing PRMT5 KD. Deep and reliable profiling of proteins and methylated peptides was achieved by two strategies. First, we performed three two-dimensional liquid chromatography separations with a different number of first dimension (high pH reverse phase chromatography) fractions and different lengths of the second dimension (low pH RP) gradient. Second, we enriched for peptides with monomethylated (MMA) and symmetrically dimethylated (SDMA) arginines using immunoaffinity purification.

Fig. 2: Proteome and methylome profiling identify new PRMT5 substrates in human AML cells.
figure2

a, Outline of the proteome and methylome profiling strategies in THP-1-cdCas9-KRAB cells transduced with a nontargeting sgRNA or two independent sgRNAs against PRMT5 (see Methods for details). High pH RP, high pH reverse phase chromatography. b, Heatmap of 2,962 differentially expressed proteins (q value ≤ 0.05 in both sgRNAs, limma test, with P values adjusted by the Storey method). c, Boxplot representing relative protein abundance of PRMT5 and its co-factor WDR77 in THP-1-cdCas9-KRAB cells transduced with nontargeting sgRNA 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 KD sgRNA is statistically significant (q < 0.05, limma test, with P values adjusted by Storey method). d, Gene ontology-based functional classification of 2,962 up- and downregulated proteins in THP-1 cells following PRMT5 KD (two-sided Fisher’s exact test, false discovery rate (FDR)-adjusted P < 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 a. 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 on PRMT5 KD, 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 ≤ 0.1 in both sgRNAs, limma test, with P values adjusted by the Storey method). h, Enrichment map of the gene ontology-enriched protein sets across 61 identified potential PRMT5 targets. Representation and statistics as in 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.

To ensure high accuracy of quantitation we analyzed the samples on an Orbitrap Fusion LUMOS platform using multinotch SPS19, and removed peptide spectrum matches (PSMs) prone to inaccurate quantitation (with low intensity of reporter ions or high degree of contamination with co-selected peptides). Proteins were included, if their quantitation was based on at least two proteotypic peptides. After application of the filtering criteria we identified 384,765 PSMs and 125,801 peptides from 7,426 proteins (Supplementary Table 1). We found that 2,962 proteins were differentially expressed in both PRMT5 sgRNA samples. For all 2,962 proteins, the change in abundance between the two PRMT5 sgRNA conditions and the control sample was in the same direction (Fig. 2b). Furthermore, PRMT5 and its co-factor WDR77 were the two most downregulated proteins (Fig. 2c). PRMT5 sgRNA-2 induced a better PRMT5 KD (Fig. 2c and Extended Data Fig. 1b,c) and, consistently, higher absolute changes of 83% of regulated proteins, supporting the idea that the observed protein changes are specific. Downregulated proteins were significantly enriched in components of DNA replication and repair pathways, while upregulated proteins were enriched in leukocyte granulation, RNA processing and vesicle transport categories (Fig. 2d).

Combining the two approaches of deep two-dimensional LC–MS and immunoenrichment (Fig. 2a), we identified 1,209 peptides with arginine methylation (Fig. 2e). Relative abundances of modified peptides were divided by their corresponding protein abundances (Fig. 2f and Supplementary Table 1), to ensure that the reported changes reflect site occupancy and not just changes in protein expression. We found that 420 peptides from 183 different proteins were differentially methylated (q ≤ 0.1 in both PRMT5 sgRNA conditions). All the observed changes had the same direction in both PRMT5 sgRNA conditions (Fig. 2g). To define a list of potential PRMT5 substrates, we selected proteins with at least one arginine monomethylation or dimethylation site decreased. This resulted in a set of 61 protein, which were enriched in proteins involved in RNA end processing, splicing and binding (Fig. 2h and Supplementary Table 1).

To investigate which of the identified potential targets of PRMT5 could explain its requirement for the leukemic cell growth, we cross-referenced the list of the 61 arginine methylated proteins with a previously published genome-wide CRIPSRko screen in human AML cell lines20. We selected 17 proteins that were shown to be required for AML cell growth (Extended Data Fig. 3a), with the hypothesis that arginine methylation of these would be essential for their function. Next, for these 17 proteins we identified those directly targeted by PRMT5. We synthesized 25 amino acids parts of the protein sequences containing the identified differentially regulated arginine methylation sites and incubated these peptides with a recombinant PRMT5–WDR77 complex followed by mass spectrometry analysis. Peptides from 12 out of the 17 proteins demonstrated characteristic methylation mass shifts (single or multiples of +14 Da) and, hence, confirmed their proteins as PRMT5 substrates (Fig. 3a and Extended Data Fig. 3b). Most of the substrates contained several arginine methylation sites (for example, SNRPB, SFPQ, SRSF1, PNN, CPSF6, CCT7, ALYREF, RPS10). In addition, we observed a tendency for more arginines in the vicinity of the methylated site, but none directly next to each other, resulting in a potential GRGRGR pattern as a PRMT5 consensus motif (Fig. 3b).

Fig. 3: Validation of the essential PRMT5 substrates.
figure3

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. The 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 nontargeting sgRNA 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 nontargeting sgRNA 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 < 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.

Source Data

Next, we confirmed that 11 out of the 12 identified protein substrates indeed are required for the proliferation of human AML cells (Fig. 3c,d and Extended Data Fig. 3c). Most of these substrates are involved in different aspects of RNA metabolism and, particularly, mRNA splicing (Table 1 and Fig. 3e).

Table 1 A list of the validated essential PRMT5 substrates

In summary, by combination of proteomics, in vitro methyltransferase assays and genetic studies we have identified 11 proteins as PRMT5 substrates that are also essential for the proliferation of AML cells. These proteins are highly enriched for regulators of RNA metabolism.

PRMT5 depletion leads to changes in alternative splicing in human AML cells

Several recent studies reported changes in alternative splicing as a result of PRMT5 downregulation in glioma, lymphoma, fetal liver cells and several solid cancer cell lines21,22,23,24,25. However, mechanistic insights into which PRMT5 substrates regulate this process are lacking. Since the majority of the essential PRMT5 substrates we identified were previously implicated in RNA binding, processing (including splicing) and transport, we decided to profile differences in alternative splicing between the wild-type and PRMT5 KD AML cells. As before, THP-1-cdCas9-KRAB cells were transduced with a nontargeting sgRNA or an sgRNA against PRMT5, and the cells were subjected to RNA sequencing.

First, using the DESeq2 (ref. 26) and edgeR-limma27,28 approaches, we identified 2,974 retained introns (RIs) in the transcriptome of THP-1 cells (Extended Data Fig. 4a and Supplementary Table 2). The retention of these introns was confirmed by Cufflinks29 assembly of full-length transcripts (Extended Data Fig. 4b,c) and, for selected events, by quantitative PCR with reverse transcription (RT–qPCR) (see below). Next, we extended the list of the identified RIs with exons expressed in THP-1 cells, and applied DEXSeq30 and limma-diffSplice28 algorithms to detect the differentially used RIs (diffRIs) and exons following the PRMT5 KD. We found that wild-type and PRMT5 KD cells differ in the usage of 415 RIs (Fig. 4a) and 1,079 exons (data not shown) distributed over 321 and 777 individual genes, respectively. The vast majority of the diffRIs exhibit higher levels in the KD cells, indicating the tendency of the PRMT5-depleted cells to retain introns in RNA molecules (Fig. 4b), which is consistent with a previous report in glioma cells21.

Fig. 4: PRMT5 depletion leads to changes in alternative splicing in human AML cells.
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a, DEXSeq and limma-diffSplice algorithms show the differential usage of 415 RIs in the transcriptome of THP-1 cells following PRMT5 KD (more than two fold changes, P < 0.002, q < 0.05). b, Volcano plot demonstrating the differential usage of RIs in the transcriptome of THP-1 cells on PRMT5 KD (total 109,530 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 KD 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 on PRMT5 KD (total 76,434 EEJs). The 430 DiffEEJs are shown by dark orange squares. The vertical dashed lines represent two-fold differences between the PRMT5 KD 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, Most of the identified nondiffEEJs 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: nondiffEEJs, 76,004; diffEEJs, log2(fold change (FC ))< −1–184; diffEEJs, log2(FC) > 1–246). e, Classification of nondiffEEJs and diffEEJs according to main modes or types of alternative splicing. The identified EEJs were divided in three subsets (left to right): (1) nondiffEEJs, (2) diffEEJs with prevalence in the control cells (log(FC) < −1) and (3) diffEEJs with prevalence in the cells with PRMT5 KD (log(FC) > 1). Classification was carried out using Ensembl-based models of hypothetical nonalternative 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 nondifferential EEJs (dynamic thresholding). h,i, Median absolute numbers of SRSF1 motifs in differential and nondifferential 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: nondiffEEJs, 76,004; difEEJs downregulated, 184; diffEEJs upregulated, 246; nondiffRIs, 1,540 and diffRIs, 336. j, Density diagrams of SFPQ motif frequency at the 5′ and 3′ splice sites of the differential and nondifferential 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 on PRMT5 KD. The splicing analysis experiments were performed using three independently transduced samples of each sgRNA. Source data are available in Supplementary Table 2.

Next, using the limma-diffSplice28 and JunctionSeq31 approaches, we analyzed differential usage of the exon–exon junctions (EEJs) in the transcriptome of THP-1 cells on the PRMT5 KD. We found 719 differential EEJs (diffEEJs, Fig. 4c, Extended Data Fig. 4d and Supplementary Table 2) distributed over 572 individual genes. Among the identified diffEEJs, the majority are annotated by Ensembl, while about 13% of the upregulated and 4% of the downregulated EEJs represent novel EEJs (Fig. 4d). This suggests that rather than inducing aberrant splicing events, PRMT5 loss leads to the differential usage of already known isoforms. If we classify all the identified EEJs according to splicing modes, PRMT5 KD mostly causes single or multiple exon skipping and alternative 5′ and or 3′ splice sites selection (Fig. 4e). We observed very small overlap between the differentially spliced and differentially expressed genes (Fig. 4f), suggesting that the detected alternative splicing differences rarely affect total mRNA expression levels.

To understand whether there is a link between the differential splicing and the changes in arginine methylation of the splicing regulators identified as PRMT5 substrates, we focused on two factors that are known to bind RNA during the splicing process (SRSF1 (ref. 32) and SFPQ33,34). We compared the frequency of their binding motifs in 100 nucleotides around the diffEEJs and nondiffEEJs, as well as around the diffRIs and nondiffRIs. The SRSF1 binding motif frequency was significantly higher both at the 5′ and 3′ splice sites of the diffEEJs (Fig. 4g and Extended Data Fig. 4e), but not around the diffRIs (data not shown). The absolute number of SRSF1 motifs per splice site was significantly increased for the differential splicing events of both classes (Fig. 4h,i). Notably, the motifs of two SRSF1 interacting partners, SRSF2 and SRSF3, but not SRSF7, were also significantly enriched at the splice sites of the diffEEJs (Extended Data Fig. 4f,g and data not shown). At the same time, minimal differences in the frequency of the SFPQ motif were observed between the differential and nondifferential splicing events (Fig. 4j and Extended Data Fig. 4h).

To analyze whether changes in alternative splicing induced by the loss of PRMT5 bear similarities in different cell types, we compared the differential splicing events found in AML cells with the ones from a recently published study in U-87 MG glioma cells21. We observed many common differential splicing events using both RI and EEJ analysis (Fig. 4k,l and Supplementary Table 2). Moreover, we found that the SRSF1 motif is also significantly enriched in the differential EEJs in U-87 MG cells (Extended Data Fig. 4i–l). Taken together, these findings suggest a potential common mechanism of alternative splicing regulation by PRMT5 via SRSF1 methylation.

In summary, our results show that loss of PRMT5 leads to numerous changes in alternative splicing events in human AML cells; in particular, increased exon skipping and higher frequency of RIs. Motif analysis demonstrates a strong enrichment for the motif of the PRMT5 substrate SRSF1 among the differential EEJs.

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

Since only a small proportion of the differentially spliced genes demonstrated a change in total mRNA levels, we hypothesized that alternative splicing can affect the protein level of a gene by changing mRNA translation efficiency or protein integrity or stability. Thus, we used the global proteomics data (Fig. 2a) to analyze protein level changes of splicing-affected genes after PRMT5 KD. Among the 825 genes with differential alternative splicing events (both differential EEJ and RIs), 687 had reliably quantified protein levels and 88 of those demonstrated significant changes in protein expression, with the majority, 74, being downregulated on the PRMT5 KD (Fig. 5a). To understand whether differential splicing of some of these genes could be linked to reduced proliferation in KD cells, we selected seven genes with affected splicing: POLD1, POLD2, PPP1R7, PNISR, FDPS, PNKP and PDCD2, which are essential according to published CRISPRko screens20. Using a CRISPRko competition assay, we independently confirmed their essentiality in human AML cells (Fig. 5b). The downregulation on protein level ranged from 25 to 50% (Fig. 5c), but since the tandem mass tag (TMT)-based proteomics experiments might be subject to ratio suppression, the differences could be more pronounced. Using RT–qPCR, we validated all the identified differential splicing events in these genes (Fig. 5d,e), and the results strongly correlated with the RNA-sequencing values (Fig. 5f). In these selected events, we also observed an increased frequency of SRSF1 binding motifs around the differential splicing sites (Extended Data Fig. 5a,b).

Fig. 5: PRMT5 loss induces alternative splicing and reduction in protein level of multiple essential genes.
figure5

a, Venn diagram showing that among the 687 differentially spliced genes quantified at the protein level 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 nontargeting sgRNA was used as a negative control. c, Barplot representing changes in protein abundance of the selected candidates on the KD of PRMT5. The values are mean ± s.d. 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 < 0.05, limma test, with P values adjusted by the Storey method). d, RT–qPCR validation of the identified differential RI events. The values represent mean ± s.d. of two independent transductions. *P < 0.1; NS, not significant according to an unpaired t-test. e, RT–qPCR validation of the identified differential EEJ. The values represent mean ± s.d. 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. *P < 0.1; NS means not significant according to an unpaired t-test. f, Scatter plot demonstrating the correlation between the log2(FC) of the differential splicing events obtained using RNA-seq (n = 3 independent transductions) and RT-qPCR approaches (n = 2 independent transductions; two-sided Student’s 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 = 2.53 × 10-02), 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, RT–qPCR 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 h. ‘PNKP RI norm’ stands for the expression of the PNKP RI normalized to the levels of PNKP cEEJ. The values are mean ± s.d. (n = 3 technical replicates, *P < 0.01, **P < 0.005, ***P < 0.001, ****P < 0.0001; NS, 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 before RT–qPCR analysis. Source data for b,df,i are available online. Source data for c,f are available in Supplementary Tables 1 and 2, respectively.

Source Data

To illustrate the potential functional consequences of the identified differential splicing events in the selected essential genes, we present two examples (Fig. 5g,h). For PDCD2, differential splicing leads to early alternative transcription termination and, thereby, potential loss of a C-terminal domain (Fig. 5g). For PNKP, a differentially RI between exons 5 and 6 of the gene contains a premature translation termination codon, which likely leads to nonsense-mediated mRNA decay (NMD) and or protein truncation (Fig. 5h). To test whether alternative splicing of PNKP leads to NMD of its mRNA, we treated the cells, transduced with either a negative control or a PRMT5 sgRNA, with emetine, a drug inhibiting NMD. This led to stabilization of the PNKP mRNA containing the RI with a more pronounced effect in the case of PRMT5 KD (Fig. 5i). This confirms that differential alternative splicing of PNKP, induced by the PRMT5 KD, leads to the NMD of its mRNA.

In summary, PRMT5 depletion leads to changes in alternative splicing and protein downregulation of several essential genes in human AML cells. This likely contributes to the requirement of PRMT5 for cell survival.

Arginine methylation of SRSF1 is functionally important for cell survival

Next, we tested whether PRMT5-mediated arginine methylation of an identified substrate regulates its function. To do this, we generated arginine-to-lysine mutants (R-to-K) of the identified PRMT5-mediated methylation sites in the essential substrates and assessed their functionality by performing rescue experiments of the corresponding gene KD or knockout. For this, we first established stable cell lines overexpressing either the wild-type or mutant versions of the substrates and then delivered the sgRNAs to deplete the corresponding endogenous gene. As shown in Fig. 6a, the R-K mutant proteins of nine substrates (SNRPB, SFPQ, CCT4, SUPT5H, PNN, CPSF6, ALYREF, CCT7 and RPS10), demonstrated comparable performance to the wild-type version of the gene, suggesting that arginine methylation on those sites is not essential for their function and therefore cell survival (Fig. 6a).

Fig. 6: Arginine methylation of SRSF1 is functionally important for cell survival.
figure6

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, RT–qPCR analysis of the endogenous (3′ untranslated region (UTR)) and exogenous SRSF1 expression in BFP and mCherry double positive cells co-transduced with an sgRNA targeting SRSF1 and either SRSF1 wild-type or mutant cDNA or a stuffer construct. The values are normalized to RPLP0 and shown as mean ± s.d. (n = 2 technical replicates, **P < 0.01, ***P < 0.001, ****P < 0.0001, NS, not 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.

Source Data

Overexpression of SRSF1 on top of the endogenous levels caused cell death in four different human AML cell lines, demonstrating tight regulation of its levels. Thus, we could not use the same strategy for the rescue experiments as above. To address this challenge, we transduced the THP-1-cdCas9-KRAB-2A-mCherry cells with an SRSF1 wild-type or triple R-to-K mutant cDNA simultaneously with the SRSF1 KD sgRNA construct, also driving the blue fluorescent protein (BFP) expression. This approach provides an opportunity for some KD cells to establish wild-type SRSF1 levels and survive. Then, 14 d after transduction we sorted out the BFP and mCherry double positive (dCas9 and sgRNA-positive) cells from each condition and assessed the source of their SRSF1 expression by RT–qPCR. As expected, cells transduced with the wild-type SRSF1 transgene exhibited KD of the endogenous SRSF1 and relied on the exogenous SRSF1 expression for their growth, indicating complete rescue (Fig. 6b). At the same time, the surviving mutant- and Stuffer-transduced BFP and mCherry double positive cells demonstrated no efficient KD of the endogenous SRSF1 (Fig. 6c), demonstrating that for these samples we could only recover the cells that escaped the effect of SRSF1 CRISPR interference. This shows that mutant SRSF1 cannot substitute for the wild type and confirms that arginine methylation of SRSF1 by PRMT5 is important for its function. Thus, we propose that PRMT5 loss leads to cell death, at least in part, by affecting the function of SRSF1.

PRMT5 depletion affects the binding of SRSF1 to mRNAs and proteins

The same three amino acids in SRSF1 (R93, R97 and R109) were previously shown to be important for its proper cellular localization and activity35. However, SRSF1 localization was affected only on mutating these sites to alanines and not lysines, suggesting the importance of positive charge rather than methylation35. To analyze this in THP-1 cells, we performed nuclear-cytoplasm fractionation with and without PRMT5 and compared the amounts of SRSF1 in different fractions. As a result, we did not observe a change in SRSF1 localization after PRMT5 KD (Extended Data Fig. 6a,b). We also performed experiments in HeLa cells, similar to the ones presented in Sinha et al.35, and found that when triple-flag-tagged wild-type SRSF1, triple R-to-K or triple R-to-A SRSF1 mutants were mildly overexpressed, all three demonstrate predominantly nuclear localization (Extended Data Fig. 6c). Some localization changes of the mutants could be observed only on increased overexpression (Extended Data Fig. 6d). Since mutant SRSF1 relocalization occurs only at high overexpression levels and mostly for the triple R-to-A mutant, it is unlikely that the primary effect of SRSF1 methylation is to retain it in the nucleus.

To further investigate the effect of PRMT5 on SRSF1 function we analyzed changes in SRSF1-bound mRNAs and proteins on PRMT5 depletion. First, we performed SRSF1 RNA immunoprecipitation (RIP) in cells transduced with either a negative control sgRNA or sgRNAs against PRMT5 or SRSF1. Efficient immunoprecipitation was verified by western blotting (Extended Data Fig. 7a). SRSF1 KD samples did not yield detectable RNA amounts after RIP, in contrast to negative control and PRMT5 KD samples, indicating highly specific immunoprecipitation (Extended Data Fig. 7b). Next-generation sequencing of the pulled down RNA reliably quantified mRNAs of 13,754 genes, of which 4,459 were significantly differentially bound between the negative control and PRMT5 KD conditions (q < 0.05) (Fig. 7a and Supplementary Table 3). There was a clear and significant enrichment of the differentially spliced genes among the genes that mRNAs were differentially bound by SRSF1 on PRMT5 KD (chi-square P = 2.2 × 10−16) (Fig. 7b,c), suggesting that alternative splicing of these is caused by altered interaction with SRSF1. As an example, five out of seven essential alternatively spliced genes described above (FDPS, POLD1, PNISR, PNKP, POLD2) were differentially bound by SRSF1, with four out five losing SRSF1 binding (Fig. 7a).

Fig. 7: PRMT5 depletion impacts SRSF1 binding to mRNAs and proteins.
figure7

a, Differential binding of SRSF1 to mRNAs on PRMT5 KD (differentially bound mRNAs are represented by orange color, q < 0.05). mRNAs of 13,754 genes were identified of which 4,459 were differentially bound. b, Overlap of differentially spliced genes with the genes, which mRNAs are differentially bound by SRSF1 on PRMT5 KD. c, Distribution of differentially spliced genes among the genes with differentially or nondifferentially bound mRNAs. d, Differential binding of SRSF1 to proteins on PRMT5 KD. Dashed lines indicate the chosen thresholds of two-fold change and q value of 0.05. A 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 three independently transduced samples of each sgRNA. The source data for the figure is available in Supplementary Tables 3 and 4.

Since arginine methylation is known to regulate protein–protein interactions, we performed SRSF1 immunoprecipitation–mass-spectrometry (IP–MS) interactomics in cells with and without PRMT5. This approach identified 350 specific SRSF1 interactors (significantly enriched over an IgG control, q < 0.05). The identified interactors were enriched in proteins involved in regulating mRNA splicing, RNA transport, transcription and translation (Supplementary Table 4), consistent with previous reports on SRSF1 involvement in these processes36,37,38. Of the 350 binding partners, 162 were differentially bound to SRSF1 between the two conditions (at least a two-fold change, q < 0.05) (Fig. 7d). Most of the SRSF1 interactors showed decreased abundance in the PRMT5 KD affinity purification, suggesting that arginine methylation promotes the interaction of SRSF1 with other proteins. The decreased interactors were enriched for proteins involved in mRNA splicing, secondary structure unwinding and transport, while increased SRSF1 binding was observed for multiple ribosomal proteins (Tables 2 and 3). Such differential binding pattern indicates redistribution of SRSF1 between various cellular processes on PRMT5 KD and likely contributes to differential splicing and cell death phenotype on PRMT5 depletion (Fig. 7e).

Table 2 Summary of the top gene ontology categories for the decreased SRSF1 interactors
Table 3 Summary of the top gene ontology categories for the increased SRSF1 interactors

The SRSF1 IP–MS also allowed more extensive arginine methylation analysis of the SRSF1 protein. Arginine methylation was detected on the same sites as in the global arginine methylation profiling approach (R93, R97 and R109), and all the nine identified peptides covering these sites demonstrated decreased abundance on PRMT5 KD (Extended Data Fig. 7c). While both mono- and dimethylation were detected for positions R93 and R109, only dimethylated R97 was identified.

In summary, we found that PRMT5 depletion leads to decreased arginine methylation of SRSF1 and extensive changes in its binding to mRNA and proteins.

Discussion

Here, we demonstrated the requirement of the catalytic activity of PRMT5 for the growth of the human AML cells bearing the MLL-AF9 rearrangement. This is consistent with the previous observations in a mouse model of this leukemia type, where Prmt5 knockout or chemical inhibition decreased leukemia burden and prolonged animal survival39. Together, these indicated PRMT5 inhibition as a potential therapeutic approach in AML, and it would be interesting to investigate whether AML patients with mutations in spliceosome proteins are particularly sensitive to PRMT5 inhibition.

In this study, we performed large-scale identification of PRMT5 substrates. In a recent resource paper40, which was published while this manuscript was prepared for submission, a number of PRMT5 substrates were identified in HeLa cells. This study employed a stable isotope labeling with amino acids in cell culture (SILAC) method (MS1-based quantitation) with two biological replicates. Many of the substrates identified by Musiani et al.40 were also identified and confirmed by in vitro methylation in our report (for example, WDR33, SNRPB and others). While Musiani et al. also identified SRSF1 methylated peptides, SRSF1 was not confirmed as a substrate, since the peptides were only identified in one of the replicates, which prohibited statistical analysis. This reflects a limitation of SILAC quantitation in comparison with the TMT approach, which is more sensitive and allows for more robust statistical treatment41.

We specifically focused on the identified PRMT5 substrates, which are essential for the proliferation of AML cells, since those are most likely to be downstream of PRMT5 in conferring cell survival. Our validation and rescue experiments confirmed SRSF1 as a direct PRMT5 target and demonstrated the importance of methylation for its function.

SRSF1 belongs to the family of serine/arginine-rich splicing factors. It is known to shuttle between the cytoplasm and the nucleus42, where it binds to exonic splicing enhancers and stimulates splicing43,44,45. In addition, SRSF1 is involved in other processes, including NMD, translation and mRNA transport36,37,38. SRSF1 is overexpressed in multiple cancers and has known oncogenic properties46. Posttranslational modifications play an important role in the function of SRSF1. Particularly, phosphorylation of SRSF1 in the arginine/serine-rich (RS) domain is required for its transport to the nucleus and localization to the sites of splicing47,48. The three arginines in SRSF1 that we identified as targeted by PRMT5 were previously shown to regulate correct localization and function of SRSF1 (ref. 35). We have not observed major changes in SRSF1 localization when the methylated arginines were changed to lysines or alanines or when PRMT5 was depleted. Instead, we found that PRMT5 KD induces disruption of the SRSF1 interaction network and extensive changes in the repertoire of mRNAs bound to it. Since all three PRMT5-methylated residues are located in the glycine-rich hinge connecting the two RNA recognition motifs of SRSF1, it is possible that loss of methylation in this region largely affects the protein structure. Taken together, our results suggest that SRSF1 is a key substrate for PRMT5, which can explain why PRMT5 is essential for leukemia cells and also, potentially, other types of cancer.

We did not observe extensive changes in total mRNA levels for the majority of the genes differentially spliced on PRMT5 depletion. However, decreased protein expression was observed for some of them. Alternative splicing in such cases could lead to mRNA retention in the nucleus, also known as intron detention49,50, inefficient translation of the alternatively spliced mRNAs50,51 or instability of the resulting protein isoforms. This agrees with the report by Braun et al. that demonstrated increased intron detention in PRMT5 inhibitor-treated glioma cells21. We found further similarities between the alternative splicing events induced by PRMT5 loss in human AML and glioma cells, suggesting common mechanisms of PRMT5 function. We found several essential genes that exhibit changes in splicing and a concomitant downregulation on protein level. These most likely explain the cell death phenotype on PRMT5 downregulation. It could be interesting to test whether chemical targeting of these essential proteins could synergize with PRMT5 inhibition in impeding cancer cell proliferation.

For the majority of the identified essential substrates R-to-K mutants were functional in our rescue assays. However, we found that PRMT5 often methylates several proteins in the same pathway or complex; for example, SNRPB and SNRPN, CCT7 and CCT4, PABPC4 and PABPN1. This could suggest functional redundancy and requirement to mutate to all of them to see an effect. Therefore, strong proliferation defect we observe on PRMT5 depletion likely represents a combined phenotype from affecting multiple cellular processes. Despite the complexity of the PRMT5 functions, the comprehensive list of substrates we report could be used to identify additional pathways to target in cancer. One of the current clinical trials with the PRMT5 inhibitor (NCT03573310) is using SDMA plasma levels as a readout of treatment efficacy. It would be interesting to investigate if the validated essential substrates could be used as more specific biomarkers for PRMT5 activity and treatment response.

In summary, this study provides a comprehensive resource of PRMT5 substrates in human acute myeloid leukemia (AML) and directly links the alterations in splicing patterns and cell death on PRMT5 depletion to the arginine methylation of the splicing factor SRSF1.

Methods

Plasmids, sgRNA cloning and site-directed mutagenesis

pHR-SFFV-KRAB-dCas9-2A-CHERRY, pLentiCas9-blast, pU6-sgRNA-EF1α-puro-T2A-BFP, pLenti PGK Hygro dest and pLEX_307 were purchased from Addgene (catalog nos. 60954, 60955, 52962, 41392 and 19066), PB-CAG-hph-dest was a gift from J. Silva. For sgRNA cloning, oligos were annealed in annealing buffer (200 mM potassium acetate, 60 mM HEPES-KOH pH 7.4, 4 mM magnesium acetate) and ligated into BstXI + BlpI digested pU6-sgRNA-EF1α-puro-T2A-BFP. Site-directed mutagenesis was performed using QuickChange Lightning Multi Site-Directed Mutagenesis Kit (Agilent) or Q5 Site-Directed Mutagenesis Kit (NEB).

sgRNA design

CRISPRko sgRNAs were designed using the sgRNA Designer: CRISPRko—Broad Institute (https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design). CRISPRi sgRNAs were designed as described previously52. Supplementary Table 5 contains sequences of all the sgRNAs used in the study.

Cell lines and cell culture

THP-1 were obtained from ATCC, MOLM-13 and MONOMAC-6 from DSMZ. HeLa cells were a kind gift from the X. Jiang Laboratory. All the cells used in the study were mycoplasma negative, no cell authentication was performed. Human embryonic kidney (HEK)293FT packaging cells and HeLa cells were cultured in DMEM, high glucose, GlutaMAX supplement, pyruvate medium (Thermo Fisher Scientific) containing 10% heat-inactivated fetal calf serum (FCS) (HyClone) and 1× penicillin and streptomycin (Thermo Fisher Scientific). THP-1 monocytic leukemia cells were cultured in RPMI 1640, GlutaMAX supplement medium (Thermo Fisher Scientific) containing 10% heat-inactivated FCS (HyClone) and 1× penicillin and streptomycin (Thermo Fisher Scientific). MOLM-13 and MONOMAC-6 monocytic leukemia cells were cultured in RPMI 1640, GlutaMAX supplement medium (Thermo Fisher Scientific) containing 20% heat-inactivated FCS (SIGMA-ALDRICH), 1× NEAA (Gibco), 1× sodium pyruvate (Gibco) and 1× penicillin and streptomycin (Thermo Fisher Scientific). For MONOMAC-6, human insulin was added to the culture medium at the concentration of 10 µg ml−1. Mouse MLL-AF9 secondary leukemia cells were generated in house and were cultured in RPMI 1640, GlutaMAX supplement medium (Thermo Fisher Scientific) containing 20% heat-inactivated FCS (HyClone), 1× penicillin and streptomycin (Thermo Fisher Scientific) and 20% of culture media supernatant from the IL-3-secreting cell line (homemade).

Western blotting

The following primary and secondary antibodies were used: PRMT5 (Abcam, ab109451), Vinculin (Sigma, SAB4200080), SDMA (Cell Signaling, 13222), glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (Abcam, ab181602), SRSF1 (Abcam, ab38017), Lamin B1 (Abcam, ab16048), b-Actin (Abcam, ab6276), peroxidase-labeled anti-mouse and anti-rabbit IgG (Vector Laboratories), IRDye 800CW Goat anti-Rabbit IgG and IRDye 680RD Goat anti-Mouse IgG (LI-COR). Western blotting quantification was performed using Image Studio Lite software (Li-COR Biosciences).

Nuclear-cytoplasm fractionation

Here, 20 million cells were lyzed in 1.3 ml of ice-cold lysis buffer (10 mM Tris-HCl, pH 7.9; 10 mM KCl; 1.5 mM MgCl2; protease inhibitor cocktail) and spun down at 400g for 5 min at 4 °C. The pellet was resuspended in 600 µl of ice-cold lysis buffer +0.2% NP-40 and precipitated at 3,300g for 15 min at 4 °C. The resulting supernatant was collected as the cytoplasmic fraction. The pellet was washed with 3 ml of ice-cold PBS and precipitated at 3,300g for 5 min at 4 °C. The resulting pellet was resuspended in TOPEX + buffer (300 mM NaCl; 50 mM Tris-HCl, pH 7.5; 0.5% Triton X-100; 1% SDS; 1 mM DTT; 33 U ml−1 Benzonase (Novagen, 70664); protease inhibitor cocktail) and collected as a nuclear fraction.

Cell transfection

Lipofection of HeLa cells was performed using Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions.

Immunofluorescence

HeLa cells were plated on glass slides 1 d after transfection and immunostained the next day as follows: fixed in 4% PFA for 15 min, permeabilized in 0.5% Triton X-100 in PBS for 10 min, blocked in 0.1% Triton X-100 in PBS with goat serum (1:80) for 30 min, stained with primary antibody in 0.1% Triton X-100 in PBS with goat serum (1:100) for 1 h at 37 °C, washed three times in 0.1% Triton X-100 in PBS for 5 min, stained with secondary antibody in 0.1% Triton X-100 in PBS for 1 h, washed three times in 0.1% Triton X-100 in PBS for 5 min and mounted in Vectashield mounting media (Vector) with DAPI. Antibodies used: SRSF1 (1:500, Abcam, ab38017), FLAG (1:500, Sigma, F1804), Alexa Flour 488 goat anti-rabbit IgG and Alexa Flour 594 goat anti-mouse IgG (1:500, Thermo Fisher Scientific). Leica TCS SP5-II confocal microscope on inverted stand, with a ×63/1.4 numerical aperture oil objective, was used to take single optical slices in DAPI, Alexa488 and Alexa594 channels.

Flow cytometry

Flow cytometry was performed using a BD LSR II flow cytometer, BD FACSAria III Cell Sorter and Beckman Coulter CytoFlex.

Virus production and lentiviral transduction

HEK293FT cells were co-transfected with a construct of interest and pAX8 and pCMV-VSV using a standard calcium phosphate protocol. The viral supernatant was collected 72 h after HEK293FT transfection and used for transduction. All the leukemia cells were transduced using a RetroNectin Bound Virus Infection Method (TaKaRa) according to the manufacturer’s instructions. Transduction was performed in presence of polybrene at 8 µg ml−1. Antibiotic selection was added 24 h after transduction.

RNA extraction, cDNA synthesis and RT–qPCR analysis

Total RNA was extracted using RNeasy Plus Mini Kit (Qiagen) in accordance with the manufacturer’s protocol. One microgram of total RNA was subjected to reverse transcription using Transcriptor Universal cDNA Master (Roche). RT–qPCR reactions were set up in triplicate using LightCycler 480 SYBR Green I Master (Roche) and primers listed in Supplementary Table 6. RT–qPCR experiments were performed on a LightCycler 480 Instrument II (Roche). Unless stated otherwise, relative quantitation was performed using a standard curve and the obtained values were normalized to RPLP0 expression. For the validation of differential splicing events, the values obtained for a differential event were divided on the values for the constitutive splicing event of the same gene.

Growth curves and competition assays

For the growth curve experiments, the cells were collected 2 d after the addition of puromycin (3 d post-transduction) and plated at the density of 500,000 cells per well of a six-well plate. The cells were counted every 72 h and 500,000 cells were replated for the next timepoint. For the competition assay, the cells were collected 2 d after the addition of puromycin (3 d post-transduction) and were mixed with untransduced cells at an approximately 8:1 ratio of transduced to untransduced cells. The percentage of BFP (transduced) cells was then recorded on a flow cytometer at the specified timepoints.

Rescue experiments

THP-1-wtCas9 or cdCas9-KRAB cells were transduced with the constructs constitutively expressing a wild-type or an R-to-K mutant substrate. After selection, the cells were transduced with a corresponding sgRNA and their proliferative capacity measured in a competition assay with untransduced cells using flow cytometry.

RNA sequencing

RNA was extracted using a Qiagen RNeasy Kit. The libraries were prepared using an Illumina TruSeq v.2 kit. The samples were sequenced using NextSeq 500.

Protein extraction and TMT labeling

Five million cells were lyzed in 330 µl of ice-cold RIPA buffer with 3 U of Benzonase and protease and phosphatase inhibitors (cOmplete and PhosStop, Roche) using a sonication probe with five cycles of 50% amplitude with 5 s intervals. Proteins were precipitated by addition of 1.2 ml of ice-cold acetone for 2 h at −20 °C and then pelleted by centrifugation at 20,000g. The samples were resuspended in 150 µl of the digestion buffer, containing 8 M urea, 0.1 M HEPES, pH 8.0 using a sonication probe with five cycles of 50% amplitude with 5 s intervals. Protein concentration was estimated with Pierce BCA Protein Assay Kit. An aliquot containing 300 µg of protein material was adjusted to 200 µl with the digestion buffer. Proteins were reduced and alkylated by addition of 10 mM TCEP and 40 mM chloroacetamide and digested with lysC (1:50 protease:protein ratio) at 37 °C for 4 h. The sample was then diluted eight-fold with 0.1 M HEPES, pH 8 buffer and digested with trypsin (1:25 protease:protein ratio) at 37 °C overnight. The obtained peptides were desalted on a 30 mg Oasis HLB cartridge (WAT094225) according to the manufacturer’s recommendations, vacuum-dried to 2–3 µl volume, evaporated using a vacuum centrifuge with 2 µl of DMSO to prevent complete evaporation and resuspended in TMT labeling buffer (0.1 M HEPES, pH 8). Peptide concentration was measured by Pierce BCA Protein Assay Kit. Next, 60 µg of peptide material for each sample was labeled with 0.5 mg of the TMT labeling reagent according to the manufacturer’s recommendations. Ten samples labeled by complementary 10plex TMT reagents were pooled for the use in downstream procedures.

Immunoaffinity enrichment of peptides containing mono- and dimethylated arginines

Enrichment was performed using a PTMScan Mono-Methyl Arginine Motif [mme-RG] Kit no. 12235 and PTMScan Symmetric Di-Methyl Arginine Motif [sdme-RG] Kit no. 13563 according to the manufacturer’s instructions (Cell Signaling Technology). The procedure was first benchmarked against a positive control sample PTMScan Trypsin Digested Control Peptides I no. 12219 provided with the kit (number of identified peptides was according to specification, data not shown). The procedure was scaled down to using only 20 µl of beads (1/4 of the volume recommended for one sample) for 600 µg of peptide material in a pooled 10plex TMT sample. To minimize the loss of beads all the washes were performed on the Pierce Spin Cups Cellulose Acetate Filter (no. 69702).

LC–MS/MS data acquisition and data analysis

The detailed procedures are described in Supplementary Information. Briefly, the data were acquired using MS3 multinotch selection TMT method as described previously19 on Orbitrap Fusion LUMOS platform. Data were analyzed using Proteome Discoverer 2.3 with Mascot v.2.3.2 as search engine and ptmRS as a PTM (post-translational modification) localization method. Differential expression test was performed using the limma package28 and P values were adjusted by the Storey method.

In vitro methyltransferase assay

The assay was performed in 50 mM HEPES (pH 8.0), 50 mM NaCl, 1 mM EDTA, 5 mM DTT and 0.2 mM S-adenosylmethionine. Each reaction was performed in 25 µ for 12 h at 37 °C with 300 ng of the corresponding peptide and 0.2 µl of active human recombinant PRMT5–MEP50 complex (Sigma, SRP0145) or water (negative control). The product of the reaction was desalted on a stageTip with 100 µl of 0.1% TFA and eluted in 30 µl of 50% ACN 0.1% FA. Then, 2 µl of the desalted product was directly infused into LTQ Orbitrap XL using nanoAcquity ultra-performance liquid chromatography (Waters) and analyzed at 100,000 resolution. The data were deconvoluted with Xtract with signal-to-noise threshold of ten, spectra extracted from the.xml outputs and plotted with the ggplot2 R package.

Defining PRMT5 methylation motif

PRMT5 methylation motif was predicted using the iceLogo online tool (https://iomics.ugent.be/icelogoserver/). Next, 20 amino acid sequences with the arginine modification site in the center were compared to the Swiss-Prot human proteome reference set.

SRSF1 RNA immunoprecipitation and sequencing

The cells were transduced with either a negative control sgRNA or sgRNAs inducing KD of PRMT5 or SRSF1. For each condition three biological replicates of 20 million cells were collected 6 d after transduction and subjected to RNA immunoprecipitation protocol using Magna RIP Kit (Millipore) according to the manufacturer’s instructions. The immunoprecipitation was performed with 5 µg of anti-SRSF1 antibody 96 (Santa Cruz). The obtained RNA was used for the preparation of sequencing libraries using the TruSeq RNA Library Prep Kit v.2, starting at the Elute, Prime, Fragment step. The details of the RIP-sequencing data analysis can be found in the Supplementary Information.

SRSF1 interactomics analysis

The cells were lyzed in lysis buffer (50 mM EPPS, pH 7.4, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, protease inhibitors), briefly sonicated, cleared from cell debris, pre-cleared with protein A Sepharose beads and immunoprecipitated with anti-SRSF1 antibody 96 (Santa Cruz) and protein A Sepharose beads. After immunoprecipitation, the beads were washed five times in wash buffer (50 mM EPPS, pH 7.4, 150 mM NaCl) and subjected to trypsin digestion overnight at 37 °C (20 mM EPPS pH 8.5, 5 mM TCEP, 20 mM chloracetamide, 10 ng µl−1 LysC and 20 ng µl−1 Trypsin). The digest was labeled with 20 g l−1 TMT tags, as recommended by the manufacturer, and half of the material was fractionated by Pierce High pH Reversed-Phase Peptide Fractionation Kit concatenating two fractions into a superfraction (for example, 1 and 5). After desalting the samples were evaporated using vacuum centrifuge, resuspended in 0.1% TFA and analyzed by LC–MS/MS.

Emetine treatment

The cells were transduced with either a negative control or a PRMT5 sgRNA and cultured for 6 d. After that, either water or emetine (at a final concentration of 100 µg ml−1) were added for 3 h. The cells were harvested and analyzed by RT–qPCR.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

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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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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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.

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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. 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

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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

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