SRSF2 plays an unexpected role as reader of m5C on mRNA, linking epitranscriptomics to cancer

Abstract

A common mRNA modification is 5-methylcytosine (m⁵C), whose role in gene-transcript processing and cancer remains unclear. Here, we identify serine/arginine-rich splicing factor 2 (SRSF2) as a reader of m⁵C and impaired SRSF2 m⁵C binding as a potential contributor to leukemogenesis. Structurally, we identify residues involved in m⁵C recognition and the impact of the prevalent leukemia-associated mutation SRSF2^P95H. We show that SRSF2 binding and m⁵C colocalize within transcripts. Furthermore, knocking down the m⁵C writer NSUN2 decreases mRNA m⁵C, reduces SRSF2 binding, and alters RNA splicing. We also show that the SRSF2^P95H mutation impairs the ability of the protein to read m⁵C-marked mRNA, notably reducing its binding to key leukemia-related transcripts in leukemic cells. In leukemia patients, low NSUN2 expression leads to mRNA m⁵C hypomethylation and, combined with SRSF2^P95H, predicts poor outcomes. Altogether, we highlight an unrecognized mechanistic link between epitranscriptomics and a key oncogenesis driver.

Keywords: NSUN2; RNA methylation; RNA modification; RNA splicing; SRSF2; SRSF2(P95H); cancer; epitranscriptomics; leukemia; m(5)C.

Figure 1.. SRSF2 binds preferentially to m⁵C-modified RNAs

(A) SRSF2 binds to m⁵C-RNA with higher affinity than to the unmodified control (n = 3). (B) Among the SR-family proteins, only SRSF2 preferentially binds m⁵C-modified RNA (n = 3). (C) Biotin pull-down followed by western blotting shows that endogenous SRSF2 binds to oligo-m⁵C with higher affinity than to oligo-C (n = 3). (D) In vitro RNA pull-down with recombinant His-tagged SRSF2 demonstrates the direct binding of SRSF2 to m⁵C (n = 3). (E) NanoBRET assays in cells transiently transfected with Nluc-SRSF2 protein and treated with varying concentrations of RNA tracer-m⁵C or tracer-C (n = 3). (F) Concentration-dependent attenuation of BRET from Nluc-SRSF2 upon titration with cold-C or cold-m⁵C in the presence of a fixed concentration of the corresponding tracer (n = 2). (G) The SRSF2 N terminus binds to m⁵C with higher affinity than to C. IC₅₀, half-maximal inhibitory concentration. Pooled data in (A)–(F) are represented as mean ± SEM. p values in (A)–(C) and in (D) were calculated using paired or unpaired two-tailed Student’s t test, respectively. p values in (E)–(F) and in (G) were determined using extra sum-of-squares F test and two-tailed F test, respectively. See also Figure S1 and Table S1.

Figure 2.. Transcriptome-wide SRSF2-binding profile, mRNA m⁵C landscape, and co-occurrence of SRSF2 binding and m⁵C

(A) RNA-binding sites and transcripts of SRSF2 identified by PAR-CLIP-seq in HeLa cells (n = 2). (B) SRSF2 preferentially binds exons. The percentages in the bar chart were scaled using the total region length of each genomic region as the normalization factor. (C) Canonical SSNG motif enriched at the centers of SRSF2-binding sites. Top: enriched motif, the E value is the enrichment p value (Fisher’s exact test) times the number of candidate motifs tested. (D) Integrative Genomics Viewer (IGV) tracks displaying exemplary SRSF2-binding sites. (E) RIP-qPCR validation of SRSF2 binding (n = 2, mean ± SEM, unpaired two-tailed Student’s t test). (F) RNA m⁵C MeRIP-seq revealed the presence of m⁵Cs within many transcripts (n = 2). (G) mRNA m⁵C peaks were found mainly in CDS regions, particularly those immediately downstream of translation start sites. (H) Frequent proximity of SRSF2-binding sites and m⁵C peak centers. See also Figure S2 and Table S2.

Figure 3.. Depletion of NSUN2 reduces m⁵C levels, alters the mRNA-binding affinity of SRSF2, and results in RNA-splicing changes similar to SRSF2 depletion

(A) Overall decrease in mRNA m⁵C levels upon NSUN2 knockdown detected by quantitative liquid chromatography-mass spectrometry (LC-MS) analysis (n = 3, mean ± SEM). (B) m⁵C MeRIP-seq from control and NSUN2 KD HeLa cells (n = 2). (C) Pie chart depicting the percentage and number of SRSF2-binding sites lost or gained in NSUN2 KD cells (n = 2). (D) Preferential SRSF2 binding to SSNG-containing sequences was altered after NSUN2 knockdown. (E) IGV tracks showing a decrease in SRSF2-RNA binding and m⁵C levels in NSUN2 KD versus control cells. (F) RNA-seq experimental design using siCtrl, siNSUN2, and siSRSF2 cells (n = 2). (G) Majority of NSUN2 KD-mediated DS genes are associated with SRSF2. (H) Exemplary sashimi plots showing concerted alternative splicing changes that occurred in cells depleted of SRSF2 or NSUN2. (I) SRSF2-binding sites and m⁵C sites occur frequently around NSUN2- and SRSF2-associated splicing events. (J) Significant overlap between SRSF2-binding targets and overlapped DS genes identified in both siNSUN2 and siSRSF2 cells (genes from dark orange region in G). p values in (A), (B), and (J) were calculated using unpaired two-tailed Student’s t test and hypergeometric test, respectively. See also Figure S3 and Tables S2 and S3.

Figure 4.. The SRSF2^P95H mutation reduces the m⁵C-binding affinity of SRSF2

(A) Only the SRSF2^P95H mutant protein shows a decreased binding preference for m⁵C-RNA (n = 2, mean ± SEM). (B) NanoBRET target engagement assays using N-terminal SRSF2^P95H and titration with cold-m⁵C in the presence of serial dilutions of tracer-C. (C) Left: NMR structure of SRSF2/RNA complex, protein, gray cartoon; RNA, orange sticks. Middle: an m⁵C base (red stick) is modeled at the position of C3 base. Right: close-up view of the m⁵C-binding pocket of wild-type SRSF2 (upper) and P95H mutant (modeled histidine, blue). (D) Binding isotherms from FP assays show preferential binding of the N-terminal domain of SRSF2 WT and P95H mutant to methylated and unmethylated RNA hexanucleotide, respectively (n = 3, mean ± SEM). See also Figure S4 and Table S1.

Figure 5.. Involvement of mRNA m⁵C regulatory transcripts in leukemia

(A) SRSF2 RNA-binding sites and transcripts identified by PAR-CLIP-seq in K562 cells (n = 2). (B) Many SRSF2^WT preferential binding sites are NSUN2-dependent binding and 104 of the corresponding transcripts are leukemia-associated. (C) IGV profiles show reduced binding of SRSF2 in NSUN2 KD or SRSF2^P95H mutant K562 cells. (D) Schematic of RNA-seq experimental design using K562 cells (n = 2). (E) SRSF2-binding sites occur preferentially around NSUN2- and SRSF2^P95H-associated splicing events. (F) Pie chart displaying the percentage of DS genes that are differentially bound by SRSF2 in NSUN2-depleted or SRSF2 mutant cells. (G) Differentially spliced SRSF2-binding targets in NSUN2-depleted or SRSF2 mutant cells are significantly enriched in the RNA-splicing category. See also Figure S5 and Tables S4 and S5.

Figure 6.. Transcriptome-wide distribution of RNA m⁵C in monocytes of CMML patients with high or low NSUN2 levels

(A) RNA-BisSeq experimental design using ribo-depleted RNAs from peripheral blood CD14⁺ monocytes of eight CMML patients. (B) NSUN2-low patients have a significantly lower number of m⁵C sites than NSUN2-high patients (mean ± SEM). (C) mRNA m⁵C sites occur more frequently in CDS regions than in UTR regions. (D) Boxplot showing the median m⁵C levels of methylated protein-coding transcripts in NSUN2-high patients are significantly higher than that of the same transcripts in NSUN2-low patients. (E) Heatmap showing correlation of mRNA m⁵C levels in NSUN2-high and -low patients. (F) Genes with differential m⁵C levels are associated with inflammatory response pathways. The p values in (B) and (D) were calculated with the unpaired two-tailed Student’s t test. See also Figure S6 and Table S6.

Figure 7.. Low NSUN2 expression is associated with poor prognosis in AML patients with the SRSF2^P95H mutation

(A) NSUN2 expression is lower in CMML and AML patients than in healthy controls. The p value comparing the data from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database was computed by the web server Gene Expression Profiling Interactive Analysis 2 (GEPIA2). All other p values were calculated with the Wilcoxon test. (B and D) AML patients with SRSF2^P95H and low NSUN2 expression have worse overall survival in the Bamopoulos et al. (B) and Beat AML (D) cohorts. p values were determined with the log-rank test. (C and E) SRSF2^P95H with low NSUN2 expression is associated with higher risk of death (log-rank test). (F and G) High leukemia-associated oncogene expression in AML patients with SRSF2^P95H and low NSUN2 expression (Wilcoxon test). See also Figure S7.