Frequent pathway mutations of splicing machinery in myelodysplasia

Journal name:
Nature
Volume:
478,
Pages:
64–69
Date published:
DOI:
doi:10.1038/nature10496
Received
Accepted
Published online

Abstract

Myelodysplastic syndromes and related disorders (myelodysplasia) are a heterogeneous group of myeloid neoplasms showing deregulated blood cell production with evidence of myeloid dysplasia and a predisposition to acute myeloid leukaemia, whose pathogenesis is only incompletely understood. Here we report whole-exome sequencing of 29 myelodysplasia specimens, which unexpectedly revealed novel pathway mutations involving multiple components of the RNA splicing machinery, including U2AF35, ZRSR2, SRSF2 and SF3B1. In a large series analysis, these splicing pathway mutations were frequent (~45 to ~85%) in, and highly specific to, myeloid neoplasms showing features of myelodysplasia. Conspicuously, most of the mutations, which occurred in a mutually exclusive manner, affected genes involved in the 3′-splice site recognition during pre-mRNA processing, inducing abnormal RNA splicing and compromised haematopoiesis. Our results provide the first evidence indicating that genetic alterations of the major splicing components could be involved in human pathogenesis, also implicating a novel therapeutic possibility for myelodysplasia.

At a glance

Figures

  1. Components of the splicing E/A complex mutated in myelodysplasia.
    Figure 1: Components of the splicing E/A complex mutated in myelodysplasia.

    RNA splicing is initiated by the recruitment of U1 snRNP to the 5′SS. SF1 and the larger subunit of the U2 auxiliary factor (U2AF), U2AF65, bind the branch point sequence (BPS) and its downstream polypyrimidine tract, respectively. The smaller subunit of U2AF (U2AF35) binds to the AG dinucleotide of the 3′SS, interacting with both U2AF65 and a SR protein, such as SRSF2, through its UHM and RS domain, comprising the earliest splicing complex (E complex). ZRSR2 also interacts with U2AF and SR proteins to perform essential functions in RNA splicing. After the recognition of the 3′SS, U2 snRNP, together with SF3A1 and SF3B1, is recruited to the 3′SS to generate the splicing complex A. The mutated components in myelodysplasia are indicated by arrows.

  2. Mutations of multiple components of the splicing machinery.
    Figure 2: Mutations of multiple components of the splicing machinery.

    Each mutation in the eight spliceosome components is shown with an arrowhead. Confirmed somatic mutations are discriminated by red arrows. Known domain structures are shown in coloured boxes as indicated. Mutations predicted as SNPs by MutationTaster (http://www.mutationtaster.org/) are indicated by asterisks. The number of each mutation is indicated in parenthesis. ZRSR2 mutations in females are shown in blue.

  3. Frequencies and distribution of spliceosome pathway gene mutations in myeloid neoplasms.
    Figure 3: Frequencies and distribution of spliceosome pathway gene mutations in myeloid neoplasms.

    a, Frequencies of spliceosome pathway mutations among 582 cases with various myeloid neoplasms. b, Distribution of mutations in eight spliceosome genes, where diagnosis of each sample is shown by indicated colours.

  4. Altered RNA splicing caused by a U2AF35 mutant.
    Figure 4: Altered RNA splicing caused by a U2AF35 mutant.

    a, Western blot analyses showing expression of transduced wild-type or mutant (S34F) U2AF35 in HeLa cells used for the analyses of expression and exon microarrays. b, The GSEA demonstrating a significant enrichment of the set of 17 NMD pathway genes among significantly differentially expressed genes between wild-type and mutant U2AF35-transduced HeLa cells. The significance of the gene set was empirically determined by 1,000 gene-set permutations. c, The confirmation of the microarray analysis for the expression of nine genes that contributed to the core enrichment in the NMD gene set. Means±s.e. are provided for the indicated NMD genes. P values were determined by the Mann–Whitney U test. d, Significantly upregulated and downregulated probe sets (FDR = 0.01) in mutant U2AF35-transduced cells compared with wild-type U2AF35-transduced cells in triplicate exon array experiments are shown in a heat map. The origin of each probe set is depicted in the left lane, where red and green bars indicate the Core and non-Core sets, respectively. e, Pair-wise scatter plots of the normalized intensities of entire probe sets (grey) across different experiments. The Core and non-Core set probes that were significantly differentially expressed between the wild-type and mutant U2AF35-transduced cells are plotted in red and green, respectively. f, Distribution of the Core (red) and non-Core (green) probe sets within the entire probe sets ordered by splicing index (S.I.; Supplementary Methods IV), calculated between wild-type and mutant U2AF35-transduced cells. In the right panel, the differential enrichment of both probe sets was confirmed by Mann–Whitney U test. g, Difference in read counts for the indicated fractions per 108 total reads in RNA sequencing between wild-type and mutant U2AF35-expressing HeLa cells analysis. Increased/decreased read counts in mutant U2AF35-expressing cells are plotted upward/downward, respectively. h, Comparison of the genome coverage by the indicated fractions in wild-type- and mutant-U2AF35-expressing cells. The genome coverage was calculated for each fraction within the 108 reads randomly selected from the total reads and averaged for ten independent selections. i, The odds ratio of the junction reads within the total mapped reads was calculated between the two experiments (red circle), which was evaluated against the 10,000 simulated values under the null hypothesis (histogram in blue).

  5. Functional analysis of mutant U2AF35.
    Figure 5: Functional analysis of mutant U2AF35.

    a, Expression of endogenous and exogenous U2AF35 transcripts in HeLa cells before and after induction determined by RNA sequencing. U2AF35 transcripts were differentially enumerated for endogenous and exogenous species, which were discriminated by the Flag sequence. b, Cell proliferation assays of U2AF35-transduced HeLa cells, where cell numbers were measured using cell-counting apparatus and are plotted as mean absorbance±s.d. c, The flow cytometry analysis of propidium iodide (PI)-stained HeLa cells transduced with the different U2AF35 constructs. Mean fractions±s.d. in G0/G1, S and G2/M populations after the induction of U2AF35 expression are plotted. d, Fractions of the annexin V-positive (AnnV+) populations among the 7-amino-actinomycin D (7AAD)-negative population before and after the induction of U2AF35 expression are plotted as mean±s.d. for indicated samples. The significance of difference was determined by paired t-test. e, Competitive reconstitution assays for CD34-negative KSL cells transduced with indicated U2AF35 mutants. Chimaerism in the peripheral blood 6weeks after transplantation are plotted as mean %EGFP-positive Ly5.1 cells±s.d., where outliers were excluded from the analysis. The significance of differences was evaluated by the Grubbs test with Bonferroni’s correction for multiple testing. *not significant.

Accession codes

Primary accessions

DDBJ/GenBank/EMBL

Gene Expression Omnibus

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Author information

  1. These authors contributed equally to this work.

    • Kenichi Yoshida,
    • Masashi Sanada,
    • Yuichi Shiraishi,
    • Daniel Nowak &
    • Yasunobu Nagata

Affiliations

  1. Cancer Genomics Project, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan

    • Kenichi Yoshida,
    • Masashi Sanada,
    • Yasunobu Nagata,
    • Yusuke Sato,
    • Aiko Sato-Otsubo,
    • Ayana Kon,
    • Masashi Shiosaka,
    • Ryoichiro Kawahata &
    • Seishi Ogawa
  2. Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan

    • Yuichi Shiraishi &
    • Satoru Miyano
  3. Department of Hematology and Oncology, Medical Faculty Manheim of the University of Heidelberg, 1–3 Theodor-Kutzer-Ufer, Mannheim 68167, Germany

    • Daniel Nowak,
    • Florian Nolte &
    • Wolf-Karsten Hofmann
  4. Division of Stem Cell Therapy, Center for Stem Cell Biology and Regenerative Medicine, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan

    • Ryo Yamamoto,
    • Makoto Otsu &
    • Hiromitsu Nakauchi
  5. Laboratory of Functional Genomics, Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan

    • Masao Nagasaki
  6. Laboratory of Sequence Data Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan

    • George Chalkidis &
    • Satoru Miyano
  7. Division of Systems Biomedical Technology, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan

    • Yutaka Suzuki &
    • Sumio Sugano
  8. Nakauchi Stem Cell and Organ Regeneration Project, Exploratory Research for Advanced Technology, Japan Science and Technology Agency, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan

    • Tomoyuki Yamaguchi &
    • Hiromitsu Nakauchi
  9. Department of Hematology, Institute of Clinical Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki, 305-8571, Japan

    • Naoshi Obara,
    • Mamiko Sakata-Yanagimoto &
    • Shigeru Chiba
  10. Division of Hematology, Tokyo Metropolitan Ohtsuka Hospital, 2-8-1 Minami-Ohtsuka, Toshima-ku, Tokyo 170-0005, Japan

    • Ken Ishiyama &
    • Shuichi Miyawaki
  11. Division of Hematology, Internal Medicine, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-ku, Yokohama, Kanagawa 227-8501, Japan

    • Hiraku Mori
  12. Munich Leukemia Laboratory, Max-Lebsche-Platz 31, Munich 81377, Germany

    • Claudia Haferlach &
    • Torsten Haferlach
  13. Hematology/Oncology, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, California 90048, USA

    • H. Phillip Koeffler
  14. National University of Singapore, Cancer Science Institute of Singapore, 28 Medical Drive, Singapore 117456, Singapore

    • H. Phillip Koeffler
  15. Division of Hematology-Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital, Chang Gung University, 199 Tung Hwa North Rd, Taipei 105, Taiwan

    • Lee-Yung Shih

Contributions

Y.Sh., Y.Sa., A.S.-O., Y.N., M.N., G.C., R.K. and S.Miyano were committed to bioinformatics analyses of resequencing data. M.Sa., A.S.-O. and Y.Sa. performed microarray experiments and their analyses. R.Y., T.Y., M.O., M.Sa., A.K., M.Sh. and H.N. were involved in the functional analyses of U2AF35 mutants. N.O., M.S.-Y., K.I., H.M., W.-K.H., F.N., D.N., T.H., C.H., S.Miyawaki, S.C., H.P.K. and L.-Y.S. collected specimens and were also involved in planning the project. K.Y., Y.N., Y.Su., A.S.-O. and S.S. processed and analysed genetic materials, library preparation and sequencing. K.Y., M.Sa., Y.Sh., A.S.-O., Y. Sa. and S.O. generated figures and tables. S.O. led the entire project and wrote the manuscript. All authors participated in the discussion and interpretation of the data and the results.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Sequence data have been deposited in the DDBJ repository under accession number DRA000433. Microarray data have been deposited in the GEO database under accession numbers GSE31174 (for SNP arrays), GSE31171 (for exon arrays) and GSE31172 (for expression arrays).

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Supplementary information

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  1. Supplementary Information (7.1M)

    This file contains Supplementary Methods 1-8 (see Contents for more details), additional references, Supplementary Figures 1-18 with legends and Supplementary Tables 1-11.

Additional data