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. 2018 Dec;32(12):2659-2671.
doi: 10.1038/s41375-018-0152-7. Epub 2018 Jun 1.

SRSF2 mutations drive oncogenesis by activating a global program of aberrant alternative splicing in hematopoietic cells

Yang Liang  1   2 , Toma Tebaldi  1   3 , Kai Rejeski  1   4 , Poorval Joshi  1 , Giovanni Stefani  5 , Ashley Taylor  1 , Yuanbin Song  1 , Radovan Vasic  1 , Jamie Maziarz  1   6 , Kunthavai Balasubramanian  1 , Anastasia Ardasheva  1 , Alicia Ding  1 , Alessandro Quattrone  3 , Stephanie Halene  7
Affiliations

SRSF2 mutations drive oncogenesis by activating a global program of aberrant alternative splicing in hematopoietic cells

Yang Liang et al. Leukemia. 2018 Dec.

Abstract

Recurrent mutations in the splicing factor SRSF2 are associated with poor clinical outcomes in myelodysplastic syndromes (MDS). Their high frequency suggests these mutations drive oncogenesis, yet the molecular explanation for this process is unclear. SRSF2 mutations could directly affect pre-mRNA splicing of a vital gene product; alternatively, a whole network of gene products could be affected. Here we determine how SRSF2 mutations globally affect RNA binding and splicing in vivo using HITS-CLIP. Remarkably, the majority of differential binding events do not translate into alternative splicing of exons with SRSF2P95H binding sites. Alternative splice alterations appear to be dominated by indirect effects. Importantly, SRSF2P95H targets are enriched in RNA processing and splicing genes, including several members of the hnRNP and SR families of proteins, suggesting a "splicing-cascade" phenotype wherein mutation of a single splicing factor leads to widespread modifications in multiple RNA processing and splicing proteins. We show that splice alteration of HNRNPA2B1, a splicing factor differentially bound and spliced by SRSF2P95H, impairs hematopoietic differentiation in vivo. Our data suggests a model whereby the recurrent mutations in splicing factors set off a cascade of gene regulatory events that together affect hematopoiesis and drive cancer.

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Conflict of interest statement

DISCLOSURE DECLARATION

The authors declare no conflict of interest

Figures

Figure 1
Figure 1. The SRSF2 P95H mutation alters SRSF2 in vivo RNA interactome
(a) Overview of the HITS-CLIP procedure. Top: generation of lentiviral vector constructs expressing C-terminally Flag-tagged SRSF2 WT and P95H in a doxycycline inducible manner. Center: HITS-CLIP key experimental steps. Bottom: computational identification of differentially bound regions, preferentially bound by WT (in red) or by P95H (in cyan) SRSF2. (b) Dose dependent inducible expression of Flag-tagged SRSF2 (WT and P95H). (c) Colony Forming Unit Assay for control CD34+ cells (mCherry), and cells with transient induction of SRSF2 WT or P95H. Left panel: total number of colonies. Right panel: composition of colonies (mean values + SEM). Total colony numbers and colony type percentages were compared between WT and P95H by two-tailed t-test (*P<0.05, **P<0.01, ***P<0.001). (d) Percentage of HITS-CLIP reads aligned to exonic, intronic and intergenic regions. In the left panel, percentages were scaled using total region lengths as a normalizing factor. (e) Percentage of HITS-CLIP crosslinking induced deletions mapping to each RNA nucleotide. (f) Volcano plot displaying fold changes and false discovery rate values for each HITS-CLIP binding site. Significant differentially bound regions are highlighted in red and cyan (preferentially bound by WT or P95H SRSF2 respectively). (g) Venn diagram of genes with at least one SRSF2 differential binding site. (h) Percentage of SRSF2 differential binding sites located within 5′UTR, 3′UTR, and CDS regions of protein coding genes.
Figure 2
Figure 2. The SRSF2 P95H mutation alters SRSF2 in vivo RNA motif specificity
(a) Top enriched motifs for WT and P95H SRSF2 binding sites, identified by discriminative analysis of kmer composition. Corresponding p-values are displayed on top of each logo. (b) Relative enrichment of the SSNG (S=C/G, N=C/G/A/U) RNA consensus motifs in RNA regions preferentially bound by WT versus P95H SRSF2. The number of motif occurrences and differential enrichment p-values are displayed for each bar.
Figure 3
Figure 3. Differentially bound SRSF2 targets are enriched in RNA binding and splicing genes
(a) Functional annotation enrichment analysis of differentially bound transcripts by WT and P95H SRSF2. The number of genes belonging to each category is displayed. (b) Protein-protein interaction network of SRSF2 RNA interactors associated with splicing. The size of each node is proportional to the number of differential SRSF2 binding sites: WT (in red), P95H (in cyan) or both (in violet).
Figure 4
Figure 4. SRSF2 P95H mutations promote alternative splicing with inclusion of CCNG rich exons and enrichment in RNA binding and splicing genes
(a) Determination of differential alternative splice events via rMATS analysis in HEL cells engineered to express SRSF2 WT vs P95H. Left panel: five classes of alternative splicing events were considered: cassette exon (CE), alternative 5′ splice site (A5SS), alternative 3′ splice site (A3SS), mutually exclusive exons (MXE) and retained intron (RI). Right panel: the number of significant events with more inclusion in WT or P95H cells is displayed. (b) Relative enrichment of the SSNG (S=C/G, N=C/G/A/T) RNA consensus motifs in cassette exons preferentially spliced in WT vs preferentially spliced in P95H SRSF2 expressing cells. The number of motif occurrences and enrichment p-values are displayed for each bar. (c) Overlap between genes with differential binding and differential splicing in HEL cells expressing either WT or P95H SRSF2. The significance of the overlap is displayed. (d) Functional annotation enrichment analysis of differentially spliced genes in WT vs P95H HEL cells. The number of genes falling into each category is indicated beside each bar.
Figure 5
Figure 5. SRSF2 mutations results in differential binding and splicing of HNRNP proteins
(a–c) Differential binding and splicing in HNRNP proteins is shown for HNRNPA2B1 (a), HNRNPH1 (b), and HNRNPM (c). Left panels: transcript maps showing WT (red) and P95H (cyan) SRSF2 binding profiles. The maps display mean normalized HITS-CLIP signal with nucleotide resolution. Standard errors for each position are shown as ribbons under mean lines. Crosslinking-induced deletions are marked in black. Exon boundaries are represented as vertical dotted lines. Differential interaction sites are highlighted on the transcript. Center panels: RT-PCR capturing differentially bound exons was performed in 3–4 replicates in HEL cells with or without doxycycline induction of SRSF2 WT or P95H expression, and with or without knockdown of endogenous SRSF2, and in human fetal liver CD34+ cells transduced with empty, SRSF2 WT or SRSF2 P95H expressing lentivirus. Right panels: primary patient derived samples - alternative splice events were quantified in normal CD34+ (n=2), WT MDS/AML (n=6), and MUT MDS/AML (n=6) samples. The magnitude of the alternative splice event in % was calculated as ratio of alternative splice event to total expression. Considered bands are marked by an asterisk (*). Predicted band sizes and transcript sizes are indicated to the right of the gel. (d) Direct differential splicing of the HNRNPA2B1 exon 9 by WT vs P95H SRSF2 verified by a minigene splicing assay. RG6-HNRNPA2B1 plasmid was co-transfected with empty vector, SRSF2 WT, SRSF2 P95H/L/R, and control or SRSF2 siRNA. Alternative splicing of exon 9 was determined via semi-quantitative PCR by measuring the ratio of alternative exon exclusion over total (exclusion+inclusion) band intensities (n=3). (e) Colony Forming Unit Assay for control cells (siNTC), cells silenced for HNRNPM or HNRNPH1, cells induced to splice out HNRNPA2B1 exon 9, and cells treated for all three modifications together (siALL). The same number of cells was plated in all experiments. The total number and the composition of colonies is displayed. Total colony numbers and colony type percentages were compared to the control (siNTC). Standard errors (SE) for colony numbers are displayed. In panels (a–e), significance values were determined by one way ANOVA with Sidák Post Hoc Test (*P<0.05, **P<0.01, ***P<0.001).
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