An in vivo genome-wide CRISPR screen identifies the RNA-binding protein Staufen2 as a key regulator of myeloid leukemia

Abstract

Aggressive myeloid leukemias such as blast crisis chronic myeloid leukemia and acute myeloid leukemia remain highly lethal. Here we report a genome-wide in vivo CRISPR screen to identify new dependencies in this disease. Among these, RNA-binding proteins (RBPs) in general, and the double-stranded RBP Staufen2 (Stau2) in particular, emerged as critical regulators of myeloid leukemia. In a newly developed knockout mouse, loss of Stau2 led to a profound decrease in leukemia growth and improved survival in mouse models of the disease. Further, Stau2 was required for growth of primary human blast crisis chronic myeloid leukemia and acute myeloid leukemia. Finally, integrated analysis of CRISPR, eCLIP and RNA-sequencing identified Stau2 as a regulator of chromatin-binding factors, driving global alterations in histone methylation. Collectively, these data show that in vivo CRISPR screening is an effective tool for defining new regulators of myeloid leukemia progression and identify the double-stranded RBP Stau2 as a critical dependency of myeloid malignancies.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Genome-wide in vivo CRISPR screen identifies an essential role for RBPs in aggressive myeloid leukemia progression.
Fig. 2: Generation and analysis of Stau2 knockout mice.
Fig. 3: Impact of genetic loss of Stau2 on bcCML initiation and propagation.
Fig. 4: Stau2 loss impairs growth of primary human leukemia.
Fig. 5: Downstream effectors of Stau2 function in myeloid leukemia.
Fig. 6: Stau2 regulates chromatin-binding genes.

Data availability

The CRISPR screen, STAU2 eCLIP, STAU2 knockdown RNA-seq data and the H3K4Me ChIP data that support the findings of this study have been deposited in GenBank (accession codes GSE135300, GSE134971, GSE135012 and GSE142307, respectively). The source data associated with each figure are provided with the manuscript. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

The current processing pipeline for eCLIP can be found at https://github.com/yeolab/eclip. For ChIP-seq, data analysis was performed in R and Python with the following packages: systemPipeR (https://bioconductor.org/packages/release/bioc/html/systemPipeR.html), bowtie2 (http://bowtie-bio.sourceforge.net/bowtie2/index.shtml), MACS2 https://github.com/taoliu/MACS, ChIPseeker (https://bioconductor.org/packages/release/bioc/html/ChIPseeker.html), deepTools (https://deeptools.readthedocs.io/en/develop/) and IGV (https://software.broadinstitute.org/software/igv/). The RNA-seq data analysis was performed in R and web-based programs with the following packages: kallisto (https://pachterlab.github.io/kallisto/), sleuth (https://github.com/pachterlab/sleuth), GSEA (https://www.gsea-msigdb.org/gsea/index.jsp) and Enrichr (https://amp.pharm.mssm.edu/Enrichr/). All computer code is available upon reasonable request.

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

References

  1. 1.

    Druker, B. J. et al. Effects of a selective inhibitor of the Abl tyrosine kinase on the growth of Bcr-Abl-positive cells. Nat. Med. 2, 561–566 (1996).

    CAS  PubMed  Google Scholar 

  2. 2.

    Saussele, S. & Silver, R. T. Management of chronic myeloid leukemia in blast crisis. Ann. Hematol. 94(Suppl. 2), S159–S165 (2015).

    PubMed  Google Scholar 

  3. 3.

    Kantarjian, H. O. B. et al. Improved survival in chronic myeloid leukemia since the introduction of imatinib therapy: a single-institution historical experience. Blood 119, 1981–1987 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Ito, T. et al. Regulation of myeloid leukaemia by the cell-fate determinant Musashi. Nature 466, 765–768 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Jiang, Q. et al. ADAR1 promotes malignant progenitor reprogramming in chronic myeloid leukemia. Proc. Natl Acad. Sci. USA 110, 1041–1046 (2013).

    CAS  PubMed  Google Scholar 

  6. 6.

    Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Kharas, M. G. et al. Musashi-2 regulates normal hematopoiesis and promotes aggressive myeloid leukemia. Nat. Med. 16, 903–908 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Park, S. M. et al. Musashi2 sustains the mixed-lineage leukemia-driven stem cell regulatory program. J. Clin. Invest. 125, 1286–1298 (2015).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Zimdahl, B. et al. Lis1 regulates asymmetric division in hematopoietic stem cells and in leukemia. Nat. Genet. 46, 245–252 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Schupbach, T. & Wieschaus, E. Maternal-effect mutations altering the anterior–posterior pattern of the Drosophila embryo. Roux’s Arch. Dev. Biol. 195, 302–317 (1986).

    Google Scholar 

  13. 13.

    St Johnston, D., Beuchle, D. & Nusslein-Volhard, C. Staufen, a gene required to localize maternal RNAs in the Drosophila egg. Cell 66, 51–63 (1991).

    Google Scholar 

  14. 14.

    Li, P., Yang, X., Wasser, M., Cai, Y. & Chia, W. Inscuteable and Staufen mediate asymmetric localization and segregation of prospero RNA during Drosophila neuroblast cell divisions. Cell 90, 437–447 (1997).

    CAS  PubMed  Google Scholar 

  15. 15.

    Slack, C., Overton, P. M., Tuxworth, R. I. & Chia, W. Asymmetric localisation of Miranda and its cargo proteins during neuroblast division requires the anaphase-promoting complex/cyclosome. Development 134, 3781–3787 (2007).

    CAS  PubMed  Google Scholar 

  16. 16.

    Heraud-Farlow, J. E. & Kiebler, M. A. The multifunctional Staufen proteins: conserved roles from neurogenesis to synaptic plasticity. Trends Neurosci. 37, 470–479 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Kusek, G. et al. Asymmetric segregation of the double-stranded RNA binding protein Staufen2 during mammalian neural stem cell divisions promotes lineage progression. Cell Stem Cell 11, 505–516 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Vessey, J. P. et al. An asymmetrically localized Staufen2-dependent RNA complex regulates maintenance of mammalian neural stem cells. Cell Stem Cell 11, 517–528 (2012).

    CAS  PubMed  Google Scholar 

  19. 19.

    Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80–84 (2014).

    CAS  Google Scholar 

  20. 20.

    Reavie, L. et al. Regulation of c-Myc ubiquitination controls chronic myelogenous leukemia initiation and progression. Cancer Cell 23, 362–375 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Trotta, R. et al. BCR/ABL activates mdm2 mRNA translation via the La antigen. Cancer Cell 3, 145–160 (2003).

    CAS  PubMed  Google Scholar 

  22. 22.

    Zhao, C. et al. Loss of β-catenin impairs the renewal of normal and CML stem cells in vivo. Cancer Cell 12, 528–541 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Airiau, K., Mahon, F. X., Josselin, M., Jeanneteau, M. & Belloc, F. PI3K/mTOR pathway inhibitors sensitize chronic myeloid leukemia stem cells to nilotinib and restore the response of progenitors to nilotinib in the presence of stem cell factor. Cell Death Dis. 4, e827 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Bacher, U., Haferlach, T., Schoch, C., Kern, W. & Schnittger, S. Implications of NRAS mutations in AML: a study of 2502 patients. Blood 107, 3847–3853 (2006).

    CAS  PubMed  Google Scholar 

  25. 25.

    Wang, Y. et al. The Wnt/β-catenin pathway is required for the development of leukemia stem cells in AML. Science 327, 1650–1653 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Mizukawa, B. et al. The cell polarity determinant CDC42 controls division symmetry to block leukemia cell differentiation. Blood 130, 1336–1346 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Shi, J. et al. Role of SWI/SNF in acute leukemia maintenance and enhancer-mediated Myc regulation. Genes Devel. 27, 2648–2662 (2013).

    CAS  PubMed  Google Scholar 

  28. 28.

    Harris, W. J. et al. The histone demethylase KDM1A sustains the oncogenic potential of MLL-AF9 leukemia stem cells. Cancer Cell 21, 473–487 (2012).

    CAS  PubMed  Google Scholar 

  29. 29.

    Park, H. et al. Adenylosuccinate lyase enhances aggressiveness of endometrial cancer by increasing killer cell lectin-like receptor C3 expression by fumarate. Lab. Investig. 98, 449–461 (2018).

    CAS  PubMed  Google Scholar 

  30. 30.

    Zhang, D. Z. et al. Basic transcription factor 3 is required for proliferation and epithelial-mesenchymal transition via regulation of FOXM1 and JAK2/STAT3 signaling in gastric cancer. Oncol. Res. 25, 1453–1462 (2017).

    PubMed  Google Scholar 

  31. 31.

    Beck-Cormier, S. et al. Notchless is required for axial skeleton formation in mice. PLoS ONE 9, e98507 (2014).

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Fei, T. et al. Genome-wide CRISPR screen identifies HNRNPL as a prostate cancer dependency regulating RNA splicing. Proc. Natl Acad. Sci. USA 114, E5207–E5215 (2017).

    CAS  PubMed  Google Scholar 

  33. 33.

    Wang, E et al. Targeting an RNA-binding protein network in acute myeloid leukemia. Cancer Cell 35, 369–384 (2019).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 10, 10008–10020 (2008).

    Google Scholar 

  35. 35.

    Deng, X., Su, R., Feng, X., Wei, M. & Chen, J. Role of N(6)-methyladenosine modification in cancer. Curr. Opin. Genet. Dev. 48, 1–7 (2018).

    CAS  PubMed  Google Scholar 

  36. 36.

    Bielli, P., Busa, R., Paronetto, M. P. & Sette, C. The RNA-binding protein Sam68 is a multifunctional player in human cancer. Endocr. Relat. Cancer 18, R91–R102 (2011).

    CAS  Google Scholar 

  37. 37.

    Seita, J. et al. Gene Expression Commons: an open platform for absolute gene expression profiling. PLoS ONE 7, e40321 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Neering, S. J. et al. Leukemia stem cells in a genetically defined murine model of blast-crisis CML. Blood 110, 2578–2585 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Van Nostrand, E. L. et al. Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods 13, 508–514 (2016).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Peng, C. et al. PTEN is a tumor suppressor in CML stem cells and BCR-ABL-induced leukemias in mice. Blood 115, 626–635 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Humbert, M. et al. Deregulated expression of Kruppel-like factors in acute myeloid leukemia. Leukemia Res. 35, 909–913 (2011).

    CAS  Google Scholar 

  42. 42.

    Kim, Y. K., Furic, L., Desgroseillers, L. & Maquat, L. E. Mammalian Staufen1 recruits Upf1 to specific mRNA 3′ UTRs so as to elicit mRNA decay. Cell 120, 195–208 (2005).

    CAS  PubMed  Google Scholar 

  43. 43.

    Maes, T. et al. ORY-1001, a potent and selective covalent KDM1A inhibitor, for the treatment of acute leukemia. Cancer Cell 33, 495–511 e412 (2018).

    CAS  PubMed  Google Scholar 

  44. 44.

    Lytle, N. K. et al. A multiscale map of the stem cell state in pancreatic adenocarcinoma. Cell 177, 572–586 (2019).

    CAS  PubMed  Google Scholar 

  45. 45.

    Wang, T. et al. Gene essentiality profiling reveals gene networks and synthetic lethal interactions with oncogenic Ras. Cell 168, 890–903 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Yau, E. H. et al. Genome-wide CRISPR screen for essential cell growth mediators in mutant KRAS colorectal cancers. Cancer Res. 77, 6330–6339 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Bajaj, J. et al. CD98-mediated adhesive signaling enables the establishment and propagation of acute myelogenous leukemia. Cancer Cell 30, 792–805 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Jin, L., Hope, K. J., Zhai, Q., Smadja-Joffe, F. & Dick, J. E. Targeting of CD44 eradicates human acute myeloid leukemic stem cells. Nat. Med. 12, 1167–1174 (2006).

    PubMed  Google Scholar 

  49. 49.

    Kwon, H. Y et al. Tetraspanin 3 is required for the development and propagation of acute myelogenous leukemia. Cell Stem Cell 17, 152–164 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Miller, P. G. et al. In vivo RNAi screening identifies a leukemia-specific dependence on integrin β 3 signaling. Cancer Cell 24, 45–58 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Yamauchi, T. et al. Genome-wide CRISPR-Cas9 screen identifies leukemia-specific dependence on a pre-mRNA metabolic pathway regulated by DCPS. Cancer Cell 33, 386–400 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Popper, B. et al. Staufen2 deficiency leads to impaired response to novelty in mice. Neurobiol. Learn. Mem. 150, 107–115 (2018).

    CAS  PubMed  Google Scholar 

  53. 53.

    Mager, L. F. et al. The ESRP1-GPR137 axis contributes to intestinal pathogenesis. eLife 6, e28366 (2017).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Puppo, M. et al. miRNA-mediated KHSRP silencing rewires distinct post-transcriptional programs during TGF-β-induced epithelial-to-mesenchymal transition. Cell Rep. 16, 967–978 (2016).

    CAS  PubMed  Google Scholar 

  55. 55.

    Fox, R. G. et al. Image-based detection and targeting of therapy resistance in pancreatic adenocarcinoma. Nature 534, 407–411 (2016).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Chang, S. H. et al. ELAVL1 regulates alternative splicing of eIF4E transporter to promote postnatal angiogenesis. Proc. Natl Acad. Sci. USA 111, 18309–18314 (2014).

    CAS  PubMed  Google Scholar 

  57. 57.

    Jiang, S. & Baltimore, D. RNA-binding protein Lin28 in cancer and immunity. Cancer Lett. 375, 108–113 (2016).

    CAS  PubMed  Google Scholar 

  58. 58.

    Schmiedel, D et al. The RNA binding protein IMP3 facilitates tumor immune escape by downregulating the stress-induced ligands ULPB2 and MICB. eLife 5, e13426 (2016).

    PubMed  PubMed Central  Google Scholar 

  59. 59.

    Dubnau, J. et al. The Staufen/pumilio pathway is involved in Drosophila long-term memory. Curr. Biol. 13, 286–296 (2003).

    CAS  PubMed  Google Scholar 

  60. 60.

    Lebeau, G. et al. Staufen 2 regulates mGluR long-term depression and Map1b mRNA distribution in hippocampal neurons. Learn. Mem. 18, 314–326 (2011).

    CAS  PubMed  Google Scholar 

  61. 61.

    Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184–191 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    McCaskill, J. S. The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers 29, 1105–1119 (1990).

    CAS  PubMed  Google Scholar 

  63. 63.

    Deigan, K. E., Li, T. W., Mathews, D. H. & Weeks, K. M. Accurate SHAPE-directed RNA structure determination. Proc. Natl Acad. Sci. USA 106, 97–102 (2009).

    CAS  PubMed  Google Scholar 

  64. 64.

    Huang, J. K. et al. Systematic evaluation of molecular networks for discovery of disease genes. Cell Sys. 6, 484–495 (2018).

    CAS  Google Scholar 

  65. 65.

    Wallace, Z. S., Rosenthal, S. B., Fisch, K. M., Ideker, T. & Sasik, R. On entropy and information in gene interaction networks. Bioinformatics 35, 815–822 (2019).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We are grateful to S. Levi for technical support and M. Kritzik for help with manuscript preparation. We thank P. Adams and P.M. Vertino for scientific advice, W. Pear (University of Pennsylvania) and A.M. Pendergast (Duke University) for the BCR-ABL construct and D.G. Gilliland for the NUP98-HOXA9 construct. J.B. is a recipient of a Scholar Award from the American Society of Hematology and a postdoctoral fellowship from the National Cancer Center. M.H. was supported by the National Institutes of Health (NIH) Training Grant T32HL086344 and K.S. received support from NIH Training Grants T32HL086344 and T32CA009523. E.L.V.N. was a Merck Fellow of the Damon Runyon Cancer Research Foundation (DRG-2172-13) and is supported by the National Human Genome Research Institute (HG009530). J.S.-B. was the Fraternal Order of Eagles Fellow of the Damon Runyon Cancer Research Foundation. This work was supported by NIH grants R35 CA210043 awarded to A.R; U54HG007005 and U41HG009889 awarded to G.W.Y.; and DK099335, DP1 CA174422 and R35 CA197699 awarded to T.R.

Author information

Affiliations

Authors

Contributions

J.B. designed and performed experiments and helped write the paper; M.H. and R.S. performed bioinformatics analysis related to CRISPR screen, RNA-seq, ChIP-seq and network analysis; Y.S., K.C., K.S. and M.C. provided experimental support; E.L.V.N., B.A.Y., S.M.B. and G.W.Y. carried out the eCLIP experiments and analysis; J.S.-B. and A.R. helped design the in vivo CRISPR screen and provided experimental advice; D.R., C.C., V.G.O. and H.E.B. provided primary leukemia patient samples. T.R. conceived the project, planned and guided the research and wrote the paper.

Corresponding authors

Correspondence to Jeevisha Bajaj or Tannishtha Reya.

Ethics declarations

Competing interests

G.W.Y. is co-founder, member of the Board of Directors, on the SAB, equity holder and paid consultant for Locana and Eclipse BioInnovations. G.W.Y. is also a visiting professor at the National University of Singapore and receives travel reimbursement. E.L.V.N. is co-founder, member of the Board of Directors, on the SAB, equity holder and paid consultant for Eclipse BioInnovations. G.W.Y.’s and E.L.V.N.’s interests have been reviewed and approved by the University of California San Diego in accordance with its conflict of interest policies.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Genome-wide CRISPR screen in myeloid leukemia.

a, Histograms show the growth rates for samples Pre-selection (PreSel) and InVivo with respect to Post-selection (PostSel, control) for all sgRNAs in the Brie library. b, Histograms of sgRNA counts after in vitro selection (PostSel, grey histogram) and after in vivo selection (InVivo, black histogram). c, Graphical representation of sgRNA count ratio [log2 (InVivo/PostSel)] of all sgRNA species, including the controls (grey) or control sgRNA species alone (amplified by a factor of 10 for visibility, black). d, Plot showing method to identify genes with true selection in vivo. The x-coordinate is the z-statistic of the log2 count ratio at the gene level, between InVivo and PostSel. The green line is the actual distribution with the secondary peak; the dashed blue line is the empirical null distribution, and the pink histogram signifies estimated non-null genes (genes with true selection). e, Graphical representation of the CRISPR screen on a plot showing −log10lfdr versus log2(InVivo/PostSel). f, Venn diagram shows overlap between reported cell essential genes for K562 bcCML cells19 (n = 1660) and genes that are depleted by 3-fold or more in the in vivo CRISPR screen (n = 3540); p < 0.0001 (hypergeometric probability formula). g, Relative RNA expression of the indicated genes in NIH-3T3 cells expressing the indicated shRNAs against novel leukemia regulators relative to control shLacZ (n = 3 technical replicates per group). h, Impact on colony forming ability of K562 cells transduced with lentiviral shRNAs against the indicated novel leukemia regulators relative to control (shLacZ) (n = 3 independent culture wells per group; two-tailed Student’s t-tests). i, Relative RNA expression of the indicated genes in K562 cells expressing the indicated shRNAs against novel leukemia regulators relative to control shLacZ (n = 3 technical replicates). j, Enriched molecular programs identified from the Enrichr analysis of genes that were depleted by 3-fold or more in vivo as compared to input (n = 3540; Fisher’s exact test). Source data

Extended Data Fig. 2 Genome-wide CRISPR screen identifies Staufen2 as a regulator of myeloid leukemia.

a, Relative expression of the indicated genes in human leukemia stem cells as compared to more differentiated cancer populations from Gene Expression Commons. b, Relative expression of STAU1 in human leukemia stem cells as compared to more differentiated cancer populations from the Riken database. c, STAU1 expression in human HSCs and human leukemia stem cells (mean±S.E.M.; n = 4 independent HSC and n = 9 independent LSC samples; two-tailed Student’s t-test). d, Relative RNA expression of the indicated genes in NIH-3T3 cells expressing shRNAs against RNA-binding proteins relative to control shLacZ (n = 3 technical replicates per group). e, Number of colonies formed by Cas9+ bcCML lin- cells transduced with the CRISPR guides against the indicated genes relative to a control non-targeting gRNA (mean±S.D.; n = 3 independent culture wells per group; two-tailed Student’s t-test). f, Relative expression of Stau2 transcript in the indicated populations isolated from the normal bone marrow of wild-type mice and sorted Lin+ and Lin and leukemia stem cell (LSC) populations from established wild-type bcCML (n = 3 technical replicates for KLS, Lin+, Leukemic Lin+, Lin, LSC, and n = 2 technical replicates for HSC and Lin). g-h, Relative Stau2 expression in KLS cells transduced with BCR-ABL (g) or BCR-ABL and NUP98HOXA9 (h) 72-96 h post-infection (n = 3 technical replicates per group). i, Impact of indicated doses of Gleevec for 48 h on Stau2 expression in established Lin bcCML cells (n = 3 technical replicates per group). Source data

Extended Data Fig. 3 Impact of genetic loss of Stau2 on normal HSC function.

a, Relative Stau2 expression in whole bone marrow cells isolated from Stau2+/+ and Stau2−/− mice (n = 3 technical replicates per group). b, Total number of cells (mean±S.E.M.) in the bone marrow of Stau2+/+ and Stau2−/− mice (n = 4 animals per cohort; data combined from 3 independent experiments; two-tailed Student’s t-test). c-d, Frequency of committed progenitors (c) and differentiated cells (d) in the bone marrow of Stau2+/+ and Stau2−/− mice (mean±S.E.M.; n = 4 animals per cohort; data combined from 3 independent experiments; two-tailed Student’s t-test). e, Frequency of donor-derived Stau2+/+ or Stau2−/− cells in the bone marrow of recipient mice transplanted with 500 HSCs of each genotype, 4 months post-transplant (mean±S.D.; n = 4 animals per cohort; data combined from 3 independent experiments). f-g, Frequency of indicated donor-derived hematopoietic stem and progenitor and differentiated cell populations in the bone marrow of recipient mice transplanted with Stau2+/+ or Stau2−/− HSCs 4 months post-transplant (mean±S.D.; n = 4 animals per cohort; data combined from 3 independent experiments). h, Frequency of donor-derived Stau2+/+ or Stau2−/− cells in the bone marrow of secondary transplant recipients, 4 months post-transplant (mean±S.E.M; n = 4 animals per cohort; data combined from 3 independent experiments). i-j, Frequency of indicated donor-derived hematopoietic stem and progenitor and differentiated cell populations in the bone marrow of secondary transplant recipients, 4 months post-transplant (mean±S.D.; n = 4 animals per cohort; data combined from 3 independent experiments). k, Relative Stau1 expression in normal hematopoietic cells from Stau2+/+ and Stau2−/− mice (left panel) or in Lin- leukemia cells (right panel). Source data

Extended Data Fig. 4 Role of Stau2 in bcCML progression.

a, Representative FACS plots show leukemia burden in control sick mice at disease end-point (top panel) and in Stau2−/− mice that did not develop disease 60d post-transplant (bottom panel; n = 2 animals per cohort). b, Relative Stau2 expression in Lin- bcCML cells transduced with inducible shLuc or shStau2. Cells were treated with PBS (control) or Doxycycline (to induce the shRNA expression) for 48 h in vitro and analyzed by qRT-PCR (n = 3 technical replicates per group except shStau2 Dox where n = 2). c, bcCML Lin- cells expressing doxycycline inducible control (shLuc) or Stau2 shRNAs (shStau2) were transplanted and recipients were given doxycycline water from day 6 post-transplant and survival monitored (n = 2 animals per cohort; data shown from one representative experiment, which was repeated with similar results, see Fig. 3g). d, Frequency of leukemia cells in the bone marrow and spleen of mice transplanted with shLuc and shStau2. shRNA expression was induced with doxycycline 6 days post-transplant and the bone marrow and spleen were analyzed 14d post-transplant (n = 2 animals per cohort, line represents median). e-f, Representative FACS plots (e) and graph (f) show the LSC frequency in mice transplanted with Lin-bcCML cells transduced with inducible shRNAs against control (shLuc) or Stau2 (n = 2 animals per cohort, line represents median). g-h, Lin- Stau2+/+ and Stau2−/− cells were transplanted into recipients. Thirteen days after transplant, mice were injected with bromodeoxyuridine (BrdU) and cells analyzed for incorporation 18 h later. Representative FACS plots show BrdU and 7AAD staining of BCR-ABL+Nup98HOXA9+ Stau2+/+ and Stau2−/− leukemic cells (g). Average frequency of cells in distinct phases of the cell cycle (mean±S.D.; n = 3 animals per cohort) is shown (h). i-j, Representative FACS plots (i) and graph (j) show analysis of early and late apoptosis in BCR-ABL+NUP98-HOXA9+ leukemia cells 13 days post-transplant (mean±S.E.M; n = 3 animals per cohort). Source data

Extended Data Fig. 5 Impact of STAU2 knockdown on human myeloid leukemia and normal human HSPCs.

a, Graphs show relative STAU2 expression in K562 cells transduced with either LacZ (control) or STAU2 shRNAs (A and B; n = 3 technical replicates per group). b, Relative STAU2 expression in two representative primary human myeloid leukemia samples transduced with shLacZ or shSTAU2. Expression was analyzed 72 h post transduction (n = 3 technical replicates per group). c, Representative FACS plot (left) and graph (right) showing the frequency of CD34+ cells in engrafted human leukemia samples in the bone marrow of NSG mice 7-8 weeks post-transplant. Each dot represents a mouse and each color represents a primary human sample (bcCML in blue and AML in red; mean±S.E.M.; n = 4 animals per cohort). d, Representative FACS plot (left) and graph (right) showing the frequency of CD11b+ differentiated cells in the engrafted human leukemia samples in the bone marrow of NSG mice 7-8 weeks post-transplant. Each dot represents a mouse and each color represents a primary human sample (bcCML in blue and AML in red; mean±S.E.M.; n = 4 animals per cohort). e, Representative FACS plot (left) and graph (right) showing the relative frequency of CD34+ CD38 cells in the engrafted normal primary human CD34+ HSPC samples in the bone marrow of NSG mice 8 weeks post-transplant. Each dot represents a mouse and each color represents an individual human sample (mean±S.E.M.; n = 4 animals per cohort). Source data

Extended Data Fig. 6 STAU2 targets genes identified by eCLIP Analysis.

a, Z-scores of individual k-mers from Stau2 eCLIP (in the CDS, the UTRs and introns). b, Motifs identified by HOMER analysis of Stau2 eCLIP peaks located within different annotated regions. c, Violin plot of the average base pairing probabilities across each reproducible STAU2 peak and their flanking regions. The swarmplot of light-blue dots represent median values of 100 iterations of random shuffled base pair probability means to show significance (compared to the white dots showing actual median bpp). d, Metagene diagram showing the pattern of significant binding across a generic mRNA transcript. e, eCLIP traces showing STAU2 binding peaks in the indicated 3’UTR regions of GFER and NRAS and in the CDS of KDM1A.

Extended Data Fig. 7 Impact of Stau2 and Kdm1a inhibition on normal and malignant hematopoiesis.

a, PCA-plot showing the distribution of the three control (Ctrl, shLacZ) and three STAU2 knockdown (shSTAU2) samples. b, Heatmap shows the changes in RNA expression of selected tumor suppressor genes upon STAU2 knockdown. c, Relative RNA expression of indicated genes in K562 cells expressing indicated shRNAs, normalized to control shLacZ (n = 3 technical replicates per group). d, Number of colonies formed by K562 cells transduced with the CRISPR-Cas9 guides against the indicated genes relative to a control non-targeting gRNA (mean±S.D.; n = 3 independent culture wells per group; two-tailed Student’s t-test). e, Relative luciferase transcript levels in 293 T cells expressing control 3′UTR reporter, GFER 3’UTR with STAU2 binding sites (bases 309-399 downstream of the stop codon) and GFER del 3’UTR with a partial STAU2 binding site (truncated at 339 bp downstream of the stop codon) (mean±S.E.M.; n = 3 independent samples per group, each in triplicate; two-tailed Student’s t-tests). f, Impact of inhibiting KDM1A at the indicated concentrations on the colony forming ability of normal hematopoietic stem cells (LincKit+Sca1+CD150+CD48) (mean±S.D.; n = 3 independent culture wells per group). g, Schematic shows KDM1A inhibitor treatment strategy to determine impact of KDM1A inhibition on normal hematopoiesis. h-j, Graphs show the average numbers of bone marrow cells (h), stem cells (HSC: KLSCD150+CD48) and multipotent progenitors (MPP: KLSCD150CD48) (i), and differentiated hematopoietic cells (j) in the bone marrow of mice treated with vehicle or KDM1A inhibitor (mean±S.D.; n = 3 animals per cohort). k, Relative RNA expression of indicated chromatin regulators in K562 cells expressing control (shLacZ) or STAU2 shRNA (n = 3 technical replicates per group). l, Heatmaps showing H3K4Me2 and H3K4Me3 marks in K562 cells expressing control (shLacZ) or STAU2 shRNA (shSTAU2). m, ChIP-Seq traces showing H3K4Me2 and H3K4Me3 binding in the promoter regions of PTEN and KLF6 genes. Source data

Supplementary information

Source data

Source Data Fig. 1

Statistical Source Data

Source Data Fig. 2

Statistical Source Data

Source Data Fig. 3

Statistical Source Data

Source Data Fig. 4

Statistical Source Data

Source Data Fig. 5

Statistical Source Data

Source Data Fig. 6

Statistical Source Data

Source Data Extended Data Fig. 1

Statistical Source Data

Source Data Extended Data Fig. 2

Statistical Source Data

Source Data Extended Data Fig. 3

Statistical Source Data

Source Data Extended Data Fig. 4

Statistical Source Data

Source Data Extended Data Fig. 5

Statistical Source Data

Source Data Extended Data Fig. 7

Statistical Source Data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bajaj, J., Hamilton, M., Shima, Y. et al. An in vivo genome-wide CRISPR screen identifies the RNA-binding protein Staufen2 as a key regulator of myeloid leukemia. Nat Cancer 1, 410–422 (2020). https://doi.org/10.1038/s43018-020-0054-2

Download citation