Abstract
Transcription and pre-mRNA splicing are key steps in the control of gene expression and mutations in genes regulating each of these processes are common in leukaemia1,2. Despite the frequent overlap of mutations affecting epigenetic regulation and splicing in leukaemia, how these processes influence one another to promote leukaemogenesis is not understood and, to our knowledge, there is no functional evidence that mutations in RNA splicing factors initiate leukaemia. Here, through analyses of transcriptomes from 982 patients with acute myeloid leukaemia, we identified frequent overlap of mutations in IDH2 and SRSF2 that together promote leukaemogenesis through coordinated effects on the epigenome and RNA splicing. Whereas mutations in either IDH2 or SRSF2 imparted distinct splicing changes, co-expression of mutant IDH2 altered the splicing effects of mutant SRSF2 and resulted in more profound splicing changes than either mutation alone. Consistent with this, co-expression of mutant IDH2 and SRSF2 resulted in lethal myelodysplasia with proliferative features in vivo and enhanced self-renewal in a manner not observed with either mutation alone. IDH2 and SRSF2 double-mutant cells exhibited aberrant splicing and reduced expression of INTS3, a member of the integrator complex3, concordant with increased stalling of RNA polymerase II (RNAPII). Aberrant INTS3 splicing contributed to leukaemogenesis in concert with mutant IDH2 and was dependent on mutant SRSF2 binding to cis elements in INTS3 mRNA and increased DNA methylation of INTS3. These data identify a pathogenic crosstalk between altered epigenetic state and splicing in a subset of leukaemias, provide functional evidence that mutations in splicing factors drive myeloid malignancy development, and identify spliceosomal changes as a mediator of IDH2-mutant leukaemogenesis.
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Acknowledgements
We thank D. L. Fei, Y. Huang, E. Wang, I. Aifantis, M. Patel, A. S. Shih, A. Penson, E. Kim, Y. R. Chung, B. H. Durham and H. Kunimoto for technical support, J. Wilusz for sharing recent data on integrator and B. J. Druker for sharing the Beat AML RNA-seq data. A.Y. is supported by grants from the Aplastic Anemia and MDS International Foundation (AA&MDSIF) and the Lauri Strauss Leukemia Foundation. A.Y. is a Special Fellow of The Leukemia and Lymphoma Society. A.Y., S.C.-W.L. and D.I. are supported by the Leukemia and Lymphoma Society Special Fellow Award. A.Y. and D.I. are supported by JSPS Overseas Research Fellowships. D.H.W. is supported by a Bloodwise Clinician Scientist Fellowship (15030). D.H.W. and K.B. are supported by fellowships from The Oglesby Charitable Trust. S.C.-W.L. is supported by the NIH/NCI (K99 CA218896) and the ASH Scholar Award. T.C.P.S. is supported by Cancer Research UK grant number C5759/A20971. E.J.W. is supported by grants from the CPRIT (RP140800) and the Welch Foundation (H-1889-20150801). R.K.B. and O.A.-W. are supported by grants from NIH/NHLBI (R01 HL128239) and the Department of Defense Bone Marrow Failure Research Program (W81XWH-16-1-0059). A.R.K. and O.A.-W. are supported by grants from the Starr Foundation (I8-A8-075) and the Henry & Marilyn Taub Foundation. O.A.-W. is supported by grants from the Edward P. Evans Foundation, the Josie Robertson Investigator Program, the Leukemia and Lymphoma Society and the Pershing Square Sohn Cancer Research Alliance.
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A.Y., K.-T.L., A.R.K. and O.A.-W. designed the study. A.Y., B.W., S.C.-W.L., J.-B.M., X.J.Z., H.C., R.E.M., D.I., T.R.A., K.B., F.S. and E.J.W. performed mouse experiments. K.-T.L. and M.A.R. performed RNA-seq analyses and minigene assays, respectively, under the supervision of A.R.K. A.P. performed DNA methylation and ChIP–seq analyses. T.R.A. and E.J.W. provided antibodies to detect integrator components and assays for snRNA cleavage. H.D., R.K.B. and F.A. performed RNA-seq analyses. D.H.W., T.C.P.S., D.P.W., S.d.B., V.P.-L., E.M.S. and R.L.L. provided clinical samples. D.H.W. and C.C. provided clinical correlative data for primary datasets. D.H.W. performed ChIP–seq experiments under the supervision of T.C.P.S. A.M.I. provided Idh2R140Q knock-in mice. R.L.L. provided Tet2 knockout mice. A.Y., K.-T.L., D.H.W. and O.A.-W. prepared the manuscript with help from all co-authors.
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A.M.I. has served as a consultant and advisory board member for Foundation Medicine. E.M.S. has served on advisory boards for Astellas Pharma, Daiichi Sankyo, Bayer, Novartis, Syros, Pfizer, PTC Therapeutics, AbbVie, Agios and Celgene and has received research support from Agios, Celgene, Syros and Bayer. R.L.L. is on the Supervisory Board of Qiagen and the Scientific Advisory Board of Loxo, reports receiving commercial research grants from Celgene, Roche and Prelude, has received honoraria from the speakers bureaus of Gilead and Lilly, has ownership interest (including stock and patents) in Qiagen and Loxo, and is a consultant and/or advisory board member for Novartis, Roche, Janssen, Celgene and Incyte. A.R.K. is a founder, director, advisor, stockholder and chair of the Scientific Advisory Board of Stoke Therapeutics and receives compensation from the company. A.R.K. is a paid consultant for Biogen; he is a member of the SABs of Skyhawk Therapeutics, Envisagenics BioAnalytics and Autoimmunity Biologic Solutions, and has received compensation from these companies in the form of stock. A.R.K. is a research collaborator of Ionis Pharmaceuticals and has received royalty income from Ionis through his employer, Cold Spring Harbor Laboratory. O.A.-W. has served as a consultant for H3 Biomedicine, Foundation Medicine, Merck and Janssen. O.A.-W. has received personal speaking fees from Daiichi Sankyo. O.A.-W. has received previous research funding from H3 Biomedicine unrelated to the current manuscript. D.I., R.K.B. and O.A.-W. are inventors on a provisional patent application (patent number FHCC.P0044US.P) applied for by Fred Hutchinson Cancer Research Center on the role of reactivating BRD9 expression in cancer by modulating aberrant BRD9 splicing in SF3B1 mutant cells.
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Peer review information Nature thanks Rotem Karni and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Extended data figures and tables
Extended Data Fig. 1 Mutant SRSF2-mediated splicing events in acute myeloid leukaemia (AML).
a, Representative Sashimi plots of RNA-seq data from the TCGA showing the poison exon inclusion event in EZH2 (‘Control’ represents samples that are wild type (WT) for the following seven genes: IDH1, IDH2, TET2, SRSF2, SF3B1, U2AF1, and ZRSR2; ‘IDH2 mutant’ refers to patients with an IDH2 mutation and no mutation in the other six genes; ‘SRSF2 mutant’ refers to patients with an SRSF2 mutation and no mutation in the other six genes; ‘double-mutant’ refers to patients with an IDH2 and SRSF2 mutation and no mutation in the other five genes; ‘others’ refers to patients with mutations in IDH1, TET2, SF3B1, U2AF1 or ZRSR2; figure made using Integrative Genomics Viewer (IGV 2.3)39). b, PSI values of EZH2 poison exon inclusion (the number of analysed patients is indicated; mean ± s.d.; one-way ANOVA with Tukey’s multiple comparison test). Note that patients classified as ‘others’ include one patient with an SRSF2P95L mutation with a coexisting IDH1R132G mutation (TCGA ID: 2990) and one patient with an IDH2R140Q mutation also having an SF3B1K666N mutation (TCGA ID: 2973), which were excluded from the analyses shown above. c, d, g–j, VAFs of SRSF2 mutations affecting the proline 95 residue (c, h, j) and IDH2 mutations affecting IDH2 arginine 140 or 172 (d, g, i) in TCGA (c, d), Beat AML (g, h) and Leucegene (i, j) datasets (mean ± s.d.; two-sided Student’s t-test). e, f, Heat map based on the ΔPSI of mutant SRSF2-specific splicing events in AML from Beat AML (e) and Leucegene (f) cohorts. ‘8aa DEL’ represents samples with 8 amino acid deletions in SRSF2 starting from proline 95, which has similar effects on splicing as point mutations affecting SRSF2 P95. Detailed information of splicing events shown is available in Supplementary Table 1. k, VAFs of IDH2 (x axis) and SRSF2 mutations (y axis) in IDH2 and SRSF2 double-mutant AML determined by RNA-seq data from the TCGA, Beat AML, Leucegene and our previously unpublished cohorts (Pearson correlation coefficient; P value (two-tailed) was calculated by Prism7). l, n, Unsupervised hierarchical clustering of DNA methylation levels of all probes (l) or at the promoter probes (n) in the TCGA AML cohort based on IDH2, SRSF2 and TET2 genotypes. m, o, DNA methylation levels of AML samples from each genotype are quantified and visualized from l and n as violin plots (the line represents mean, box edges show 25th and 75th percentiles and whiskers represent 2.5th and 97.5th percentiles; one-way ANOVA with Tukey’s multiple comparison test). **P < 0.01, ***P < 0.001.
Extended Data Fig. 2 Clinical relevance of coexisting IDH2 and SRSF2 mutations in AML.
a–c, Kaplan–Meier survival analysis of patients with AML from the Manchester/Christie Biobank dataset (a: based on IDH2 and SRSF2 genotype (n = 258); b: based on cytogenetic risk (n = 284)) and the TCGA (c; n = 161; based on IDH1, IDH2 and SRSF2 genotypes (log-rank (Mantel–Cox) test (two-sided)). d, Age at diagnosis of patients from the TCGA, Beat AML, and Manchester/Christie Biobank cohorts combined (the line represents mean, box edges show 25th and 75th percentiles and whiskers represent 2.5th and 97.5th percentiles; samples below 2.5th percentile and above 97.5th percentile are shown as dots; one-way ANOVA with Tukey’s multiple comparison test). e, Distribution of French–American–British (FAB) classification of patients with AML with the indicated genotypes from the TCGA cohort. f–h, Mutations coexisting with IDH2 and SRSF2 double-mutant and SRSF2 single-mutant AML from the TCGA (f), Beat AML (g), and Manchester/Christie Biobank (h) cohorts are shown with FAB classification, cytogenetic risk, prior history of myeloid disorders, and genetic risk stratification based on European LeukaemiaNet (ELN) 2008 and ELN2017 guidelines (the number of patients is indicated; P values on the right represent statistical significance of co-occurrence (red and orange) or mutual exclusivity (blue and light blue) of each gene mutation with SRSF2 (including those in IDH2 and SRSF2 double-mutant AML) or coexisting IDH2 and SRSF2 mutations; Fisher’s exact test (two-sided)). *P < 0.05, **P < 0.01, ***P < 0.001.
Extended Data Fig. 3 Mutant IDH2 cooperates with mutant Srsf2 to generate lethal MDS with proliferative features in vivo.
a, Schematic of BM transplantation model. b, c, Chimerism of CD45.2+ cells in the peripheral blood of recipient mice over time (b) (n = 5 per group at 4 weeks; mean percentage ± s.d.; two-way ANOVA with Tukey’s multiple comparison test) and representative flow cytometry data showing the chimerism of CD45.2+ versus CD45.1+ (top) or GFP+ (bottom) cells in peripheral blood at 16 weeks post-transplant (c) (representative results from five recipient mice; the percentages listed represent the percent of cells within live cells). d, Composition of peripheral blood mononuclear cells (PBMNCs) at 28 weeks post-transplant (the number of analysed mice is indicated; mean + s.d.; two-way ANOVA with Tukey’s multiple comparison tests; statistical significances were detected in percentage of CD11b+Gr1+ cells in IDH2R140Q + Srsf2WT versus IDH2R140Q + Srsf2P95H and in IDH2R172K + Srsf2WT versus IDH2R172K + Srsf2P95H). e–h, Blood counts at 20 weeks post-transplant (white blood cells (WBC) (e); haemoglobin (Hb) (f); platelets (PLT) (g); mean corpuscular volume (MCV) (h); the number of analysed mice is indicated; mean ± s.d.; one-way ANOVA with Tukey’s multiple comparison tests). i, Plasma 2HG levels at 20 weeks post-transplant (2HG levels were quantified as previously described40; n = 5 per group were randomly selected; mean ± s.d.; one-way ANOVA with Tukey’s multiple comparison test). j, Correlations between plasma 2HG levels and number of GFP+ cells in peripheral blood at 24 weeks post-transplant (n = 5 per group; the Pearson correlation coefficient (R2) and P values (two-tailed) were calculated using PRISM 7). k, Colony numbers from serial replating assays of BM cells collected from end-stage mice from Fig. 2b are shown (mean value ± s.d. represented by lines above the box; the number of analysed mice is indicated; two-way ANOVA with Tukey’s multiple comparison test). l, Giemsa staining of IDH2R140Q Srsf2P95H double-mutant cells from the sixth plating (scale bar, 10 μm; original magnification, ×400; representative result from 9 biologically independent experiments). m, Immunophenotype of colony cells at the sixth plating. Normal BM cells were used as a control (the percentage listed represent the percent of cells within live cells; representative result from nine recipient mice). n, Cytomorphology of BM mononuclear cells (BMMNCs) from recipient mice at end stage. BM cells from IDH2 single-mutant and IDH2 and Srsf2 double-mutant groups have increased granulocytes. In addition, IDH2 and Srsf2 double-mutant groups had proliferation of monoblastic and monocytic cells as well as dysplastic features such as abnormally segmented neutrophils (black arrow and inset) and binucleated erythroid precursors with irregular nuclear contours (insets) (scale bar, 10 μm; original magnification, ×400; representative results from 3 controls and 9 recipients are shown; number of mice indicated in o–r). o–r, Blood counts at end-stage (WBC (o); Hb (p); PLT (q); MCV (r); the number of analysed mice is indicated; mean ± s.d.; Kruskal–Wallis tests with uncorrected Dunn’s test). s–u, Results from flow cytometry analysis of BM (s) and peripheral blood (t) mature lineages as well as BM haematopoietic stem/progenitor cells (HSPC) from two tibias, two femurs, and two pelvic bones (u) are quantified (LSK, Lineage−SCA1+KIT+; LT-HSC, long-term haematopoietic stem cell (HSC); ST-HSC, short-term HSC; MPP, multi-potent progenitor; LK: Lineage−SCA1−KIT+; CMP, common myeloid progenitor; GMP, granulocyte-monocyte progenitor; MEP, megakaryocyte-erythroid progenitor; the number of analysed mice is indicated; mean + s.d.; two-way ANOVA with Tukey’s multiple comparison test). v, w, Spleen weight at end stage (v; the number of analysed mice is indicated; mean ± s.d.; two-way ANOVA with Tukey’s multiple comparison test) and representative photographs of spleens from recipient mice from v (w; each photograph was taken with an inch ruler). x, Kaplan–Meier survival analysis of serially transplanted recipient mice that were lethally irradiated (n = 5 per group; log-rank (Mantel–Cox) test (two-sided)). *P < 0.05, **P < 0.01, ***P < 0.001.
Extended Data Fig. 4 Collaborative effects of mutant Idh2 and mutant Srsf2 are not dependent on Tet2 loss alone.
a, Schematic of competitive and non-competitive transplantation assays of CD45.2+ Mx1-cre control, Mx1-cre Idh2R140Q/+, Mx1-cre Srsf2P95H/+, Mx1-cre Idh2R140Q/+Srsf2P95H/+ mice, Mx1-cre Tet2fl/fl, Mx1-cre Tet2fl/flSrsf2P95H/+ mice into CD45.1+ recipient mice. b, 2HG levels of bulk PBMNCs from primary Mx1-cre mice were measured at three months post-pIpC (polyinosinic:polycytidylic acid) and normalized to internal standard (d-2-hydroxyglutaric-2,3,3,4,4-d5 acid; D5-2HG) (2HG and D5-2HG levels were quantified as described40; n = 5 per group; mean ± s.d.; one-way ANOVA with Tukey’s multiple comparison test). c, DNA extracted from sorted KIT+ BM cells from primary Mx1-cre mice at one month post-pIpC was probed with antibodies specific for 5-hydroxymethylcytosine (5hmC) (left). Relative intensity of each dot was measured by ImageJ and divided by input DNA amount for comparison (right; n = 4; intensity of each dot divided by amount of input DNA was combined per genotype; representative results from 3 biologically independent experiments with similar results; mean ± s.d.; one-way ANOVA with Tukey’s multiple comparison test). d, Chimerism of peripheral blood CD45.2+ cells in non-competitive transplantation (pIpC was injected at 4 weeks post-transplant; mean ± s.d.; n = 10 (control and Idh2R140Q), n = 8 (Srsf2P95H), and n = 9 (DKI) at 0 week; two-way ANOVA with Tukey’s multiple comparison test; P values from comparison between Srsf2P95H and each of other groups are shown). e–i, Absolute number of BM HSPCs from two tibias, two femurs, and two pelvic bones were measured in the primary (e, f) and serial (h, i) competitive transplant of Idh2 and Srs2 mutant cells, and representative flow cytometry of BM HSPCs from the primary competitive transplant of Idh2 and Srsf2 mutant cells from e, f (the percentage listed represents the percent of cells within live cells) (the number of analysed mice is indicated; mean + s.d.; two-way ANOVA with Tukey’s multiple comparison test). j, Kaplan–Meier survival analysis of CD45.1+ recipient mice transplanted non-competitively with BM cells from CD45.2+ Mx1-cre control, Mx1-cre Tet2fl/fl, Mx1-cre Srsf2P95H/+, and Mx1-cre Tet2fl/flSrsf2P95H/+ mice (pIpC was injected at 4 weeks post-transplant; n = 10 per genotype; log-rank (Mantel–Cox) test (two-sided)). k, l, Chimerism of peripheral blood CD45.2+ cells in non-competitive (k) (n = 10 (control and Tet2 knockout (Tet2KO)), n = 8 (Srsf2P95H), and n = 5 (Tet2KO + Srsf2P95H) at 0 weeks) or competitive (l) (n = 9 (control), n = 10 (Tet2KO), n = 8 (Srsf2P95H), and n = 10 (Tet2KO + Srsf2P95H) at 0 weeks) transplantation (pIpC was injected at 4 weeks post-transplant; percentages of CD45.2+ cells at pre-transplant are also shown as data at 0 weeks in l; mean ± s.d.; two-way ANOVA with Tukey’s multiple comparison test). m, n, Absolute number of BM HSPCs from two tibias, two femurs, and two pelvic bones were measured in the primary competitive transplant of Tet2 and Srsf2 mutant cells (n = 10 per genotype; mean + s.d.; two-way ANOVA with Tukey’s multiple comparison test). o, Schematic of TET2 catalytic domain (CD: catalytic domain; EV: empty vector) retroviral BM transplantation model. p, Western blot analysis confirming the expression of Myc-tagged TET2 CD in Ba/F3 cells transduced with or without TET2 CD (representative images from two biologically independent experiments with similar results). q, Chimerism of mCherry–TET2 CD+ and GFP–EV+ cells in peripheral blood of recipient mice over time (n = 10; mean percentage ± s.d.; two-way ANOVA with Sidak’s multiple comparison test). r, qPCR of Tet3 in the first colony cells from s (n = 3; mean ± s.d.; a two-sided Student’s t-test). s, Colony numbers from serial replating assays of BM cells from Mx1-cre control, Mx1-cre Srsf2P95H/+, and Mx1-cre Tet2fl/flSrsf2P95H/+ mice transduced with shRNAs targeting Tet3 (shTet3) (n = 3; mean + s.d.; two-way ANOVA with Tukey’s multiple comparison test). t, Schematic of shTet3 retroviral BM transplantation model. u, v, Chimerism of mCherry+ cells in CD45.2+ donor cells in peripheral blood of recipient mice over time (u; left, Mx1-cre Srsf2P95H/+; right, Mx1-cre Tet2fl/flSrsf2P95H/+; n = 5 per group) and at 20 weeks post-transplant (v) (mean percentage ± s.d.; two-way ANOVA with Sidak’s multiple comparison test). w, Colony numbers from serial replating assays of either Mx1-cre Srsf2+/+ or Srsf2P95H/+ BM cells transduced with an shRNA against Fto or Alkbh5. BM cells were collected at one month post-pIpC (n = 3; mean value ± s.d.; two-way ANOVA with Tukey’s multiple comparison test). x, qPCR of Fto or Alkbh5 in Ba/F3 cells transduced with shRNAs targeting mouse Fto or Alkbh5 (n = 3; mean value ± s.d.; one-way ANOVA with Tukey’s multiple comparison test). *P < 0.05, **P < 0.01, ***P < 0.001.
Extended Data Fig. 5 IDH2 mutations augment the RNA splicing defects of SRSF2 mutant leukaemia.
a–c, Venn diagram showing numbers of differentially spliced events from the Beat AML cohort (a), unpublished collaborative cohort 2 (b) and mouse Lin−KIT+ bone marrow cells at 12 weeks post-pIpC (c) based on IDH2 and SRSF2 mutant genotypes. d, Venn diagram showing the numbers of overlapping alternatively spliced events between IDH2 and SRSF2 double-mutant AMLs and mouse models (***P = 2.2 × 10−16; binominal test). e–g, ∆|PSI| (∆|PSI| = |PSI|Double – |PSI|SRSF2) values for each overlapping mis-spliced event in SRSF2 single-mutant and IDH2 and SRSF2 double-mutant AML from the TCGA (e), Beat AML cohort (f) and unpublished collaborative cohort 2 (g) are plotted along the y axis. Spliced events shown in green and red represent events that are more robust in IDH2 and SRSF2 double-mutant and SRSF2 single-mutant AML, respectively, in terms of |PSI| values. The mean |PSI| value of each event was visualized as violin plots on the bottom (n = 292, n = 1,741, and n = 187, respectively; PSI values were calculated using PSI-Sigma; the line represents mean, box edges show 25th and 75th percentiles and whiskers represent 2.5th and 97.5th percentiles; samples below 2.5th percentile and above 97.5th percentile are shown as dots; paired two-tailed Student t-test). h, i, Venn diagram of numbers of differentially spliced events from the TCGA (h) and Beat AML (i) datasets based on IDH2, TET2 and SRSF2 genotypes. j, k, Absolute numbers of each class of alternative splicing event from TCGA (j) and Beat AML (k) datasets are shown. SES, single-exon skipping; MES, multiple-exon skipping; MXS, mutually-exclusive splicing; A5SS, alternative 5′ splice site; A3SS, alternative 3′ splice site. l, m, Differentially spliced events (|∆PSI| > 10% and P < 0.01 were used as thresholds) in indicated genotype from the TCGA (l) (n = 730 differentially spliced events) and Beat AML (m) (n = 1,339 differentially spliced events) cohorts are ranked by y axis and class of event (PSI and P values adjusted for multiple comparisons were calculated using PSI-Sigma). n–p, Sequence logos of nucleotide motifs of exons preferentially promoted or repressed in splicing in SRSF2 single-mutant (top) or IDH2 and SRSF2 double-mutant (bottom) AML from the TCGA cohort (n), Beat AML cohort (o) and mouse models (p). q, Percentage of each class of alternative splicing event in indicated genotype from TCGA cohort is shown in a pie-chart. r–t, Differentially spliced events (|∆PSI| > 10% and P < 0.01 were used as thresholds) in indicated genotype from the Beat AML (r) (n = 2,183, 5,648, and 79 differentially spliced events, respectively), unpublished collaborative cohort 2 (s) (n = 558, 1,926, and 94 differentially spliced events, respectively) and Leucegene cohort (t) (n = 2,571, 787, and 122 differentially spliced events, respectively) are ranked by y-axis and class of event (PSI and P values adjusted for multiple comparisons were calculated using PSI-Sigma). u, w, Representative Sashimi plots of RNA-seq data showing the intron retention events in REC8 (u) and PHF6 (q) from the TCGA dataset. v, x, PSI values for intron retention events in REC8 (v) and PHF6 (x) in normal PBMNCs (GSE5833541), BMMNCs (GSE6141041), cord blood CD34+ cells (GSE4884642), and AML samples with indicated genotypes (the line represents the median, box edges show 25th and 75th percentiles and whiskers represent 2.5th and 97.5th percentiles; samples below 2.5th percentile and above 97.5th percentile are shown as dots; PSI and P values adjusted for multiple comparisons were calculated using PSI-Sigma; one-way ANOVA with Tukey’s multiple comparison test; *P < 0.05; **P < 0.01; ***P < 0.001). y, Volcano plots of aberrant splicing events in TCGA AML data comparing SRSF2 single-mutant and IDH2/SRSF2 double-mutant AML (n = 122 differentially spliced events; PSI and P values adjusted for multiple comparisons were calculated using PSI-Sigma; |∆PSI| > 10% and P < 0.01 were used as thresholds).
Extended Data Fig. 6 Aberrant INTS3 transcripts undergo nonsense-mediated decay, and effect of INTS3 loss extends to other members of the integrator complex.
a, Representative Sashimi plots of RNA-seq data from the TCGA showing intron retention in INTS3. b, c, PSI values for INTS3 exon 5 skipping (b) and intron 4 retention (c) in normal PBMNC (GSE5833541), BMMNC (GSE6141041), cord blood CD34+ cells (GSE4884642) and AML samples with indicated genotypes (the number of RNA-seq samples analysed is indicated; PSI and P values adjusted for multiple comparisons were calculated using PSI-Sigma; the line represents mean, box edges show 25th and 75th percentiles and whiskers represent 2.5th and 97.5th percentiles; samples below 2.5th percentile and above 97.5th percentile are shown as dots; one-way ANOVA with Tukey’s multiple comparison test). d, Sanger sequencing of cDNA showing wild-type or mutant SRSF2 expression in isogenic K562 knock-in cells. #a nonsynonymous mutation that alters P95; ##a synonymous mutation that does not change the amino acid. e, RT–PCR and western blot analysis of INTS3 in isogeneic HL-60 cells with various combinations of IDH2 and SRSF2 mutations. IR: intron retention; ES: exon skipping. Representative results from three biologically independent experiments with similar results. f, RT–PCR and western blot of INTS3 in non-isogenic myeloid leukaemia cell lines. SRSF2 genotypes are shown together (representative results from three independent experiments with similar results). g, Western blot analysis of K562 SRSF2P95H knock-in cells transduced with shRNAs against UPF1 (representative results from three biologically independent experiments with similar results). h, Primers used to specifically measure INTS3 isoform with intron 4 retention and exon 5 skipping, and those for the normal INTS3 isoform. i, j, Half-life of INTS3 transcripts with exon 5 skipping (i) and intron 4 retention (j) were measured by qPCR (n = 3; mean ± s.d.; a two-sided Student’s t-test). k, l, Western blot analysis of protein lysates of samples from patients with AML with the indicated IDH2 and SRSF2 genotypes (k). Expression level of each integrator subunit was quantified using ImageJ and relative expression levels are shown in l, in which the mean expression levels of control samples were set as 1 (n = 6 for control, IDH2 single-mutant, and SRSF2 single-mutant AML, and n = 7 for IDH2 and SRSF2 double-mutant AML; detailed information of the primary patient samples used for this analysis is provided in Supplementary Table 23; mean ± s.d.; one-way ANOVA with Tukey’s multiple comparison test). m, Western blot analysis of protein lysates from isogenic K562 cells with indicated IDH2 and SRSF2 genotypes (left) or with INTS3 knockdown (right) (representative results from three biologically independent experiments are shown). n, Western blot analysis of murine Lin−KIT+ BM cells at 12 weeks post-pIpC based on Idh2 and Srsf2 mutant genotypes. Expression level of INTS3 was quantified using ImageJ and relative expression levels are shown below; n = 2 mice per genotype were analysed. o, Correlation among indicated Integrator subunits and P value were calculated in Excel and R2 values are visualized as a heat map generated by Prism 7 (top). Correlation between INTS3 and INTS9 protein expression is shown (bottom) (n = 25 from k; the Pearson correlation coefficient (R2) and P values (two-tailed) were calculated in Excel). *P < 0.05, **P < 0.01, ***P < 0.001.
Extended Data Fig. 7 DNA hypermethylation at INTS3 enhances INTS3 mis-splicing, which is associated with RNAPII stalling.
a, Sequence of human INTS3 exon 4, intron 4 and exon 5, and schematic of INTS3 minigene constructs. GG(A/U)G motifs, (C/G)C(A/U)G motifs, and CG dinucleotides are highlighted in blue, red, and green, respectively. b, Schematic of INTS3 minigene constructs. c, Table revealing the number of GGNG or CCNG motifs in exon 4, entire cDNA of INTS3 or entire genomic DNA (gDNA) of INTS3 per 100 nucleotides. d–i, Radioactive RT–PCR results of INTS3 minigene assays using indicated versions of the minigene in isogenic K562 cells. Percentage of intron 4 retention were normalized against exogenous eGFP (n = 3; mean percentage ± s.d.; one-way ANOVA with Tukey’s multiple comparison test). j, Mean percentage of methylated CpGs at ARID3A in samples from patients with AML with indicated genotypes determined by eRRBS (n = 3 patients per genotype), followed by IGV plots of RNA-seq data of ARID3A from the TCGA. k, Results of eRRBS (n = 1 per genotype) and RNAPII-Ser2P ChIP-walking experiments are represented as shown in Fig. 3f (n = 3; mean percentage ± s.d.; two-way ANOVA with Tukey’s multiple comparison test). l, m, RT–PCR results detecting INTS3 intron retention in isogenic K562 cells containing various combinations of IDH2 and SRSF2 mutations that were treated with cell-permeable 2HG at 0.5 μM (l) or 5-AZA-CdR at 5 μM (m) for 8 days (representative results from three biologically independent experiments with similar results). n, RNAII pausing index in isogenic SRSF2WT or SRSF2P95H mutant K562 cells was calculated as previously described20 as a ratio of normalized ChIP–seq reads of RNAPII-Ser5P on TSSs (±250 bp) over that of the corresponding bodies (+500 to +1,000 from TSSs) (the line represents the median, box edges show 25th and 75th percentiles and whiskers represent 2.5th and 97.5th percentiles; each box plot was made by analysing ChIP–seq data from one cell line; two-sided Student’s t-test). o, Metagene plots showing genome-wide RNAPII-Ser5P occupancy in primary samples from patients with AML with indicated genotypes (patient samples used for this analysis are described in Supplementary Table 23). p, q, RNAPII occupancy representing ChIP–seq reads of RNAPII-Ser2P over gene bodies was calculated for isogenic K562 cells (p) and AML samples (q) (the line represents the median, box edges show 25th and 75th percentiles and whiskers represent 2.5th and 97.5th percentiles; each box plot was made by analysing ChIP–seq data from one cell line (p) or one primary AML sample (q); two-sided Student’s t-test (p) and one-way ANOVA with Tukey’s multiple comparison test (q)). r, s, Genome browser view of ChIP–seq signal for RNAPII Ser5P at INTS5 (r) and INTS14 (s) in isogenic K562 cells with or without SRSF2 mutation (n = 1) and primary AML samples with indicated genotype (results generated from n = 2 primary AML samples are shown). t, RNAPII abundance over the differentially spliced regions between IDH2 and SRSF2 wild-type control and SRSF2 single-mutant AML determined by RNAPII-Ser2P ChIP–seq (y axis, log2(counts per million); the line represents the median, box edges show 25th and 75th percentiles and whiskers represent 2.5th and 97.5th percentiles; each box plot was made by analysing ChIP–seq data from one primary AML sample; one-way ANOVA with Tukey’s multiple comparison test). *P < 0.05, **P < 0.01, ***P < 0.001.
Extended Data Fig. 8 Loss of INTS3 impairs uridine-rich small nuclear RNA processing and blocks myeloid differentiation.
a, Schematic of snRNA processing site and qPCR primers for detecting cleaved or uncleaved snRNA. b, qPCR (top; n = 3; mean ± s.d.; a two-sided Student’s t-test) and representative western blot of INTS3 in HL-60 cells transduced with shRNAs targeting human INTS3 (bottom, representative results from three biologically independent experiments). c–e, s, t, qPCR results of U2 (c, s) and U4 (d, t) snRNAs in isogenic HL-60 cells and U7 snRNA in murine cells from Extended Data Fig. 6n (e). Ratio of uncleaved/total snRNAs expression was compared (n = 3, mean ratio ± s.d.; one-way ANOVA with Tukey’s multiple comparison test; the largest P values calculated among 2 × 2 comparisons of two components from different groups are shown. For example, P values were calculated from the following four comparisons; bars 1 versus 3, 2 versus 3, 1 versus 4, 2 versus 4). f, Schematic of the U7 snRNA–GFP reporter. g, v, Flow cytometry analysis of 293T cells transduced with U7 snRNA-GFP reporter and IDH2, SRSF2 and INTS3 constructs as labelled on the right (representative results from three biologically independent experiments are shown). h, w, Quantification of per cent GFP− and GFP+ 293T cells (n = 3 biologically independent experiments, mean percentage ± s.d.; one-way ANOVA with Tukey’s multiple comparison test; P values are shown as in c). i, l, y, Flow cytometry analysis of CD11b expression in isogenic HL-60 cells after ATRA treatment for two days (representative results from three biologically independent experiments are shown). j, m, z, Quantification of percentages of CD11b+ HL-60 cells over time (n = 3; mean percentage ± s.d.; two-way ANOVA with Tukey’s multiple comparison test). k, n, Cytomorphology of isogenic HL-60 cells after ATRA treatment for two days (Giemsa staining; scale bar, 10 μm; original magnification, ×400; representative results from three biologically independent experiments are shown). o, p, qPCR of Ints3 (o) (mean ± s.d.; Kruskal–Wallis tests with uncorrected Dunn’s test) and western blot of INTS3 (p) in Ba/F3 cells transduced with shRNAs targeting mouse Ints3. q, r, Representative cytomorphology (q) and immunophenotype (r) of colony cells at the sixth colony. Normal BMMNCs were used as a control (the percentage listed represent the percent of cells within live cells; representative results from three biologically independent experiments are shown). u, x, Western blot of proteins extracted from HL-60 cells (u) assayed in s-t and y-z and 293T cells (x) assayed in v and w (representative results from three biologically independent experiments). *P < 0.05, **P < 0.01, ***P < 0.001, #P < 0.05, ##P < 0.01, ###P < 0.001.
Extended Data Fig. 9 Mutant Idh2 cooperates with Ints3 loss to generate a lethal myeloid neoplasm in vivo.
a, Schematic of shRNA targeting Ints3 (shInts3) retroviral BM transplantation model. b, Flow cytometry data showing the chimerism of CD45.2+ versus CD45.1+ (top) or GFP+ (bottom) cells in peripheral blood at four weeks post-transplant (the percentages listed represent the percent of cells within live cells; representative results from five recipient mice). c, Composition of PBMNCs at four weeks post-transplant (n = 5 per group; mean + s.d.; represented by lines above the box. statistical significance was detected in percentage of CD11b+Gr1+ cells; by two-way ANOVA with Tukey’s multiple comparison test). d–g, Chimerism of GFP+ cells in peripheral blood (d) and blood counts of recipients at four weeks post-transplant (Hb (e); PLT (f); MCV (g); n = 5 per group; mean ± s.d.; one-way ANOVA with Tukey’s multiple comparison test). h, Giemsa staining of BMMNCs from moribund mice with indicated genotypes (red and yellow arrows represent blastic cells and dysplastic neutrophils, respectively; inset, representative neutrophils with abnormal segmentation; scale bar, 10 μm; original magnification, ×400; representative results from five mice per genotype). i, Flow cytometry data of BM, spleen, liver, and peripheral blood from Idh2R140Q mice treated with shInts3 (representative results from five mice). j, Schematic of HL-60 xenograft model in which recipient mice from cohort 1 were euthanized at day 18 post-transplant and mice from cohort 2 were observed for survival analysis until end stage. k–n, Blood counts (WBC (k); Hb (l); PLT (m)) and spleen weight (n) of mice from cohort 1 at day 18 post-transplant (mean ± s.d.; n = 5 per group; a two-sided Student’s t-test). o, p, Representative flow cytometry data of BM, spleen, and peripheral blood from the recipient mice from cohort 1 (o) (the percentage represents the percent of cells within live cells) and the mean percentage of GFP+ cells (p) (n = 5 per group; mean + s.d.; two-way ANOVA with Sidak’s multiple comparison test). q, r, Representative flow cytometry data of BM, spleen and peripheral blood from cohort 1 (q) (the percentage represents the percent of cells within GFP+ live cells) and the mean percentage of hCD34−, hCD11b+ and hCD13+ cells (r) (n = 4 per group; mean + s.d.; two-way ANOVA with Sidak’s multiple comparison test). s, Kaplan–Meier survival analysis of recipient mice from cohort 2 (n = 5 per group; log-rank (Mantel-Cox) test (two-sided)). *P < 0.05, **P < 0.01, ***P < 0.001.
Extended Data Fig. 10 Gene expression and biological consequences of INTS3 loss, and effect of IDH1 and IDH2 mutations on splicing in low-grade glioma.
a–d, GSEA based on RNA-seq data generated from isogenic IDH2R140Q mutant HL-60 cells with or without INTS3 depletion. Representative results from gene sets associated with leukaemogenesis and myeloid differentiation (a), oncogenic signalling pathways (b), RNAPII elongation-linked transcription (c) and DNA damage response (d) with statistical significance (P < 0.01) are shown (y axis; enrichment score; NES: normalized enrichment score; FDR: false discovery rate; RNA-seq data generated from isogenic HL-60 cells in duplicate were analysed using GSEA34). e, f, PSI values for INTS3 intron 4 (e) and 5 (f) retention events across 33 cancer cell types (the same datasets were analysed in Fig. 4f). ACC,: adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; DLBC, diffuse large B-cell lymphoma; ESCA, oesophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LGG, low-grade glioma; LIHC, liver hepatocellular carcinoma; LUSC, lung squamous cell carcinoma; MESO, mesothelioma; OV, ovarian serous cystadenocarcinoma; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumours; THCA, thyroid carcinoma; THYM, thymoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma. The line represents the median, box edges show 25th and 75th percentiles and whiskers represent 2.5th and 97.5th percentiles; samples below 2.5th percentile and above 97.5th percentile are shown as dots; one-way ANOVA with Dunnett’s multiple comparison test; ***P < 0.001 represents the P values from all the comparisons between AML and any of other 32 non-AML cancer type. g, Western blot analysis confirming overexpression of 3× Flag-tagged INTS3 in RN2 (MLL-AF9 NrasG12D) leukaemia cells (representative results from three biologically independent experiments). h, Colony numbers from serial replating assays of RN2 cells with or without INTS3 overexpression (n = 3; mean + s.d. represented by lines above the box; two-way ANOVA with Sidak’s multiple comparison test). i, Schematic of INTS3 retroviral BM transplantation models in which recipient mice from cohort 1 were euthanized at day 18 post-transplant and mice from cohort 2 were observed for survival analysis until end-stage. j–l, Blood counts (WBC (j); Hb (k); PLT (l)) of mice from cohort 1 at day 18 post-transplant (mean ± s.d.; n = 4 (‘empty’ group); n = 5 (‘INTS3’ group) recipient mice; a two-sided Student’s t-test). m, Representative photograph of spleens and livers from cohort 1 with an inch scale (left), and spleen (middle) and liver weight (right) (n = 4 (empty); n = 5 (INTS3); mean ± s.d.; two-sided Student’s t-test). n, o, Representative Giemsa staining (n) (red arrows represent differentiated cells; scale bar, 10 μm; original magnification, ×400) and percentages of blasts, differentiated myeloid cells, and other cells in BMMNCs (o) from moribund mice from cohort 2 (n = 3 per genotype; 100 cells per mouse were classified; mean percentage + s.d.; two-way ANOVA with Sidak’s multiple comparison test). p, q, Representative flow cytometry analysis of BM, spleen, liver, and peripheral blood (p) and percentages of CD45.2+ cells in Ter119− live cells (q) in recipient from cohort 1 (n = 4 (empty); n = 5 (INTS3); mean ± s.d.; two-way ANOVA with Tukey’s multiple comparison test). r, s, Representative flow cytometry analysis showing KIT expression in RN2 cells with or without INTS3 overexpression (r) and quantification of KIT+ cells (s) from cohort 1 (n = 4 (Empty); n = 5 (INTS3); mean ± s.d.; one-way ANOVA with Tukey’s multiple comparison test). t, u, Volcano plots of aberrant splicing events in the LGG TCGA dataset based on IDH2 (t) or IDH1 (u) mutant genotypes. |∆PSI| > 10% and P < 0.01 were used as thresholds (n = 849 and n = 433 differentially spliced events, respectively; RNA-seq data were analysed using PSI-Sigma). v, Percentage of each class of alternative splicing event in IDH2 (left) and IDH1 (right) mutant LGG is shown in pie-chart. w, Venn diagram of numbers of alternatively spliced events from the LGG TCGA dataset based on IDH1 and IDH2 mutant genotypes. ‘Control’ represents LGG with wild-type IDH1 and IDH2. *P < 0.05, **P < 0.01, ***P < 0.001.
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Supplementary Figure 1: Source data for western blot, gel electrophoresis, and DNA dot plot with molecular weight markers indicated.
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Yoshimi, A., Lin, KT., Wiseman, D.H. et al. Coordinated alterations in RNA splicing and epigenetic regulation drive leukaemogenesis. Nature 574, 273–277 (2019). https://doi.org/10.1038/s41586-019-1618-0
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DOI: https://doi.org/10.1038/s41586-019-1618-0
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