The splicing factor RBM17 drives leukemic stem cell maintenance by evading nonsense-mediated decay of pro-leukemic factors

Abstract

Chemo-resistance in acute myeloid leukemia (AML) patients is driven by leukemic stem cells (LSCs) resulting in high rates of relapse and low overall survival. Here, we demonstrate that upregulation of the splicing factor, RBM17 preferentially marks and sustains LSCs and directly correlates with shorten patient survival. RBM17 knockdown in primary AML cells leads to myeloid differentiation and impaired colony formation and in vivo engraftment. Integrative multi-omics analyses show that RBM17 repression leads to inclusion of poison exons and production of nonsense-mediated decay (NMD)-sensitive transcripts for pro-leukemic factors and the translation initiation factor, EIF4A2. We show that EIF4A2 is enriched in LSCs and its inhibition impairs primary AML progenitor activity. Proteomic analysis of EIF4A2-depleted AML cells shows recapitulation of the RBM17 knockdown biological effects, including pronounced suppression of proteins involved in ribosome biogenesis. Overall, these results provide a rationale to target RBM17 and/or its downstream NMD-sensitive splicing substrates for AML treatment.

Fig. 1. Heightened expression of RBM17 correlates with poor prognosis in human AML patients.

a –log10 p-value (two-tailed Student’s t test) of each mRNA splicing factor expression in LSC-enriched vs LSC-depleted subsets from 78 AML patients (y axis) and their correlation (-log10 p-value) (log-rank test) with AML patients’ overall survival (x axis). b Kaplan–Meier curves showing outcomes of AML patients from the TCGA with above (n = 124) vs below (n = 155) median expression of RBM17. (P = 0.00568, log-rank test). c RBM17 transcript level in LSC-enriched (n = 138) vs LSC-depleted (n = 89) subsets from 78 AML patient samples (GSE76008). Average fold change of RBM17 expression in LSC-enriched over LSC-depleted subsets is indicated in the figure. Data are presented as mean ± SD, two-tailed Student’s t test. d Gene expression data (GSE35008) from sorted AML bone marrow samples were compared with data from healthy controls and revealed significantly increased RBM17 expression in AML LT-HSCs (Lin^–CD34 + CD38^–CD90 + , AML with normal karyotype, n = 3) compared with healthy control (n = 4). Data shown as mean ± SD, two-tailed Student’s t test. e Intracellular flow cytometric measurements of RBM17 protein level in the primitive CD34⁺ subset vs the committed CD34^- subsets from 8 primary AML samples. P = 0.0092, P value was calculated using paired t-test, two-tailed. f Expression of RBM17 in 152 AML specimens and each molecular genetic risk group from the TCGA-LAML cohort (Good: n = 38; Intermediate: n = 76; Poor: n = 38). Data are presented as mean ± SD, two-tailed Student’s t test, P(Good vs Intermediate) = 0.0196, P(Good vs Poor)=0.0051. g Expression of RBM17 in 236 AML specimens and each molecular genetic risk group from Beat AML cohort (Favorable: n = 85, Intermediate: n = 68, Adverse: n = 83). Data are presented as mean ± SD, two-tailed Student’s t test, P(Favorable vs Intermediate) = 0.0188, P(Favorable vs Adverse)=0.0041. h A heatmap showing the expression of differentially expressed transcripts identified from RBM17-high AML cases versus RBM17-low AML cases. i, j Gene set enrichment analysis (GESA) of the gene signature of high-RBM17 AML cases compared with previously published i LSC signatures and j ribonucleoprotein complex biogenesis and spliceosomal complex assembly pathways. The significance of NES was calculated using Kolmogorov–Smirnov statistics. *P < 0.05, **P < 0.01, ***P < 0.001. Source data are provided as a Source Data file.

Fig. 2. RBM17 is required to support primitive leukemic cell functions.

a Western blot validation of RBM17 knockdown (KD) in transduced GFP⁺ HL60 cells. n = 3 independent experiments. b, c Flow cytometric evaluation of myeloid differentiation (P(shscramble vs shRBM17#1)=0.0108, P(shscramble vs shRBM17#2)=0.0011)) (b) and apoptosis (P(shscramble vs shRBM17#1)=0.0111, P(shscramble vs shRBM17#2)=0.0039) (c) following RBM17 knockdown in primary AML cells. n = 3, mean ± SD, two-tailed Student’s t test. d–h Colony formation capacity assessed in 5 AML samples following RBM17 KD. n = 3, mean ± SD, two-tailed Student’s t test. i Representative flow cytometry plots showing the GFP⁺ cell percentage in the human cell population post-transplant for AML#001 (top) and total human grafts (CD45⁺CD33⁺ cell percentage) post-transplant for AML#006 (bottom). j Quantification of AML engraftment after 9 weeks (#001) in whole bone marrow. Shown is the ratio of the GFP⁺ cell percentage in the human cell population post-transplant to the initial pre-transplant GFP⁺ cell percentage. Data are presented as mean ± SD, n = 5, two-tailed Student’s t test, P(shscramble vs shRBM17#1)=0.0415, P(shscramble vs shRBM17#2)=0.0173. k AML engraftment (#006) after 12 weeks in bone marrow. Shown is the percentage of human (CD45⁺) myeloid cells (CD33⁺) found in bone marrow. n = 5, mean ± SD, two-tailed Student’s t test, P(shscramble vs shRBM17#1)=0.0026, P(shscramble vs shRBM17#2)=0.0002. l–n Representative histogram (l) and quantification (m, n) of flow cytometric immunophenotyping of myeloid differentiation in post-transplant grafts from j, k. n = 5, mean ± SD, two-tailed Student’s t test. o CFU output from transduced Lin^- CD34⁺ cord blood (CB). n = 5, mean ± SD, two-tailed Student’s t test. p Relative engraftment of CD34⁺CD38^- enriched normal CB HSC with and without RBM17 knockdown. Shown is the ratio of the GFP⁺ cell percentage in the human cell population post-transplant to the initial pre-transplant GFP⁺ cell percentage. n = 6, mean ± SD, two-tailed Student’s t test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Source data are provided as a Source Data file.

Fig. 3. Identification of RNA targets of and alternative splicing variants enforced by RBM17 in AML.

a eCLIP-seq analysis of RBM17 binding sites in K562 cells. Input-normalized peak signals are shown as log2 fold change. Red points indicate eCLIP-enriched RBM17 peaks (FDR < 0.05 and log2 (FC) > 3) in biological replicates. b Significantly enriched reproducible binding sites or RBM17 across the transcriptome. c Hexamer enrichment for RBM17 binding peaks in K562 cells based on eCLIP-seq. The top 4 enriched motifs are shown on the x axis. Insert shows enriched motifs for different genomic regions. d Scatterplot of splicing events promoted (blue circles) and repressed (red circles) by RBM17 knockdown (shRBM17) in K562 cells compared to control shscramble with a cutoff FDR < 0.1 and ΔPSI > 0.05. Splicing change is quantified using ΔPSI (percent spliced in). e Quantification of types of AS events affected by RBM17, as revealed by analysis of RNA-seq data. AS events are labeled as cassette exon (CE), retained intron (RI), alternative 3’ splice site (A3SS), alternative 5’ splice site (A5SS) and mutually exclusive exon (MXE). Number of splicing events directly bound by RBM17 is indicated beside the bar of each splicing type. f Overlay of transcripts between RBM17 eCLIP-seq analysis and RNA-seq splicing analysis. g GO enrichment analysis of terms enriched in splicing events directly bound by RBM17.

Fig. 4. RBM17 knockdown leads to the production of NMD sensitive transcripts.

a Pie chart distribution of predicted protein consequences, including changes in “protein domain” and “coding potential”. Bottom bar plot indicates the distribution of alternative splicing events predicted to lead to coding potential changes in shRBM17 groups. b Depiction of (1) RBM17 binding to intronic regions of EZH and EIF4A2, and resultant promotion of their retention and PTC introduction following knockdown of RBM17; (2) RBM17 binding to the cassette exon of RBM39, HNRNPDL and RBM41 leading to cassette exon inclusion and introduction of PTCs post-RBM17 knockdown; (3) RBM17 binding to exon belonging to 5’UTR of SRRM1 and subsequent inclusion of this exon that contain alternative start codon upon RBM17 knockdown, inducing ORF frameshift and PTC. c Cytoscape network analysis of proteins significantly deregulated by RBM17 knockdown in K562 cells. d Heat map of protein expression fold change of 13 NMD-sensitive transcripts with and without RBM17 knockdown. e Bootstrapping analysis of 44 proteins from 8825 total proteins identified from the RBM17 knockdown proteome. P value was calculated using two-tailed Student’s t test.

Fig. 5. RBM17 suppresses EIF4A2 poison exon inclusion.

a Ranked protein expression change curve 5 days after transduction with shRNAs against RBM17 in K562 cells. Protein names that also have significant RBM17 binding with their transcripts (eCLIP-seq) and have significant alternative splicing changes after RBM17 knockdown (at day 5 by RNA-seq) are displayed (colored in yellow). b–d RT-PCR validation of EIF4A2 (b), RBM39 (c) and EZH2 (d) variants using RNA extracted from K562, HL60 and primary AML cells with and without RBM17 knockdown. n = 3 independent experiments. e Upper: Diagram of EIF4A2 splicing variants and the primers for RT-PCR detection of cryptic exon inclusion. Bottom: IGV plot of RNA-seq data illustrating the EIF4A2 cryptic exon promoted by RBM17 knockdown and anti-RBM17 eCLIP-seq tracks. f qPCR quantification of UPF1 knockdown in K562 cells. n = 3, mean ± SD, two-tailed Student’s t test. g EIF4A2 intron 10-included isoform in K562 cells with and without UPF1 knockdown and actinomycin D treatment. n = 4, mean ± SD, two-tailed Student’s t test. h EIF4A2 intron 10-excluded isoform in K562 cells with and without UPF1 knockdown and actinomycin D treatment. n = 4, mean ± SD, two-tailed Student’s t test. i Western blot detection of EIF4A2 after RBM17 knockdown in K562 and HL60 cell lines. n = 3 independent experiments. ***P < 0.001, ****P < 0.0001. Source data are provided as a Source Data file.

Fig. 6. EIF4A2 is required for sustaining primitive leukemic cell fitness.

a EIF4A2 mRNA expression in different subtypes of AML as compared to healthy bone marrow control, on the basis of data from Microarray Innovations in Leukemia (MILE) study (GSE13204). Data are presented as mean ± SD, two-tailed Student’s t test. b EIF4A2 transcript level in LSC-enriched (n = 138) vs LSC-depleted (n = 89) subsets from 78 AML patient samples (GSE76008). Average fold change of EIF4A2 expression in LSC-enriched over LSC-depleted subsets is indicated in the figure. Data are presented as mean ± SD, two-tailed Student’s t test. c EIF4A2 protein level as assessed proteomics analysis in functionally defined LSC and non-LSC populations from 5 AML patient samples (PXD008307). Data are presented as mean ± SEM, two-tailed Student’s t test. d Gene expression data (GSE35008) from sorted AML bone marrow samples were compared with data from healthy controls and revealed significantly increased EIF4A2 expression in AML LT-HSCs (Lin^-CD34 + CD38^-CD90 + , AML with normal karyotype, n = 3) compared with healthy control (n = 4). Data are presented as mean ± SD, two-tailed Student’s t test. e WB validation of EIF4A2 knockdown in GFP + transduced HL60 cells. f, g Flow cytometric evaluation of myeloid differentiation (f) and apoptosis (g) following EIF4A2 knockdown in primary AML cells. n = 3, mean ± SD, two-tailed Student’s t test. h–j Colony formation capacity following EIF4A2 knockdown in 3 AML samples. n = 3, mean ± SD, two-tailed Student’s t test. k, l Representative flow cytometry plots (k) and quantification (l) of AML engraftment (#007) after 8 weeks in bone marrow. n = 6 for each group. Shown is the percentage of human (CD45⁺) myeloid cells (CD33⁺) found in bone marrow. Data are presented as mean ± SD, two-tailed Student’s t test. Source data are provided as a Source Data file.

Fig. 7. EIF4A2 overexpression partially rescues the RBM17 knockdown phenotype in AML cells.

a, b Correlation analysis between RBM17 and EIF4A2 mRNA levels using the above microarray data about functionally defined 138 LSC-enriched and 89 non-LSC populations (P < 0.0001) (a) and TCGA dataset (P = 0.0013) (b). AML patient samples were ranked based on RBM17 expression, the population above the median value of RBM17 expression level was defined as ‘high’ RBM17 expression, while the population below the median value of RBM17 expression level was defined as ‘low’ RBM17 expression. Data are presented as mean ± SD, two-tailed Student’s t test. c, d GSEA enrichment plots showing RBM17 knockdown in K562 cells leads to downregulation of the EIF4A2_KD_DN gene set and upregulation of the EIF4A2_KD_UP gene set. The significance of NES was calculated using Kolmogorov-Smirnov statistics. e WB images showing expression of RBM17 and EIF4A2 in HL60 cells engineered to co-express TNGFR or EIF4A2 with and without RBM17 knockdown. n = 3 independent experiments. f, g Flow cytometry analysis of AnnexinV (f) and myeloid differentiation (g) in HL60 cells on day 8 following co-expression of TNGFR or EIF4A2 and knockdown of control scramble or RBM17. Data are presented as mean ± SD, n = 6. h GSEA enrichment plots showing EIF4A2 and RBM17 knockdown in K562 cells leads to downregulation of the GO ribosome biogenesis gene set. The significance of NES was calculated using Kolmogorov-Smirnov statistics. i Heat map showing downregulated proteins from the ribosome biogenesis gene set induced by EIF4A2 and RBM17 knockdown. j Representative histogram and quantification of flow cytometric detection of op-puro incorporation in HL60 cells on day7 following EIF4A2/RBM17 knockdown. Data shown as mean ± SD, n = 3, two-tailed Student’s t test. k Representative histogram and quantification of flow cytometric detection of op-puro incorporation in HL60 cells on day10 following simultaneous expression of TNGFR or EIF4A2 with or without RBM17 knockdown. Data shown as mean ± SD, n = 6, two-tailed Student’s t test. Source data are provided as a Source Data file.

Fig. 8. Schematic model depicting the role of RBM17 in primitive AML cells.

RBM17 is abnormally higher expressed in the most primitive cell fractions of AML compared to AML blasts, which contributes to efficient splicing of many pro-leukemic factors EZH2, RBM39 and HNRNPDL, along with EIF4A2 that functions in translation control, to sustain LSC functions. Knockdown of RBM17 promotes inclusions of cryptic exons or introns into mRNAs of these pro-leukemic factors, leading to their mRNA degradations due to NMD and consequently resulting in translation blockade, cell apoptosis, limited colony-forming and engraftment capacities, and promoted differentiation in primitive AML cells.