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Toolkit for evaluating genes required for proliferation and survival using tetracycline-regulated RNAi

Nature Biotechnology volume 29, pages 79–83 (2011)Cite this article

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Abstract

Short hairpin RNAs (shRNAs) are versatile tools for analyzing loss-of-function phenotypes in vitro and in vivo1. However, their use for studying genes involved in proliferation and survival, which are potential therapeutic targets in cancer and other diseases, is confounded by the strong selective advantage of cells in which shRNA expression is inefficient. We therefore developed a toolkit that combines Tet-regulated miR30-shRNA technology, robust transactivator expression and two fluorescent reporters to track and isolate cells with potent target knockdown. We demonstrated that this system improves the study of essential genes and was sufficiently robust to eradicate aggressive cancer in mice by suppressing a single gene. Further, we applied this system for in vivo negative-selection screening with pooled shRNAs and propose a streamlined, inexpensive workflow that will facilitate the use of RNA interference (RNAi) for the identification and evaluation of essential therapeutic targets.

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Figure 1: Dual-color TRMPV vectors enable Tet-regulated shRNA expression for suppression of genes involved in cell proliferation and survival.
Figure 2: TRMPV enables RNAi-based evaluation of genes involved in tumor maintenance in vivo.
Figure 3: TRMPV-induced suppression of Rpa3 cures clonal MLL-AF9;NrasG12D AML.
Figure 4: Pooled negative selection RNAi screening in vivo detects shRpa3 depletion in MLL-AF9;NrasG12D AML.

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References

  1. Martin, S.E. & Caplen, N.J. Applications of RNA interference in mammalian systems. Annu. Rev. Genomics Hum. Genet. 8, 81–108 (2007).

    Article  CAS  Google Scholar 

  2. Hannon, G.J. RNA interference. Nature 418, 244–251 (2002).

    Article  CAS  Google Scholar 

  3. Brummelkamp, T.R., Bernards, R. & Agami, R. A system for stable expression of short interfering RNAs in mammalian cells. Science 296, 550–553 (2002).

    Article  CAS  Google Scholar 

  4. Paddison, P.J., Caudy, A.A., Bernstein, E., Hannon, G.J. & Conklin, D.S. Short hairpin RNAs (shRNAs) induce sequence-specific silencing in mammalian cells. Genes Dev. 16, 948–958 (2002).

    Article  CAS  Google Scholar 

  5. Hemann, M.T. et al. An epi-allelic series of p53 hypomorphs created by stable RNAi produces distinct tumor phenotypes in vivo. Nat. Genet. 33, 396–400 (2003).

    Article  CAS  Google Scholar 

  6. Dickins, R.A. et al. Probing tumor phenotypes using stable and regulated synthetic microRNA precursors. Nat. Genet. 37, 1289–1295 (2005).

    Article  CAS  Google Scholar 

  7. Barbie, D.A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009).

    Article  CAS  Google Scholar 

  8. Luo, J. et al. A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene. Cell 137, 835–848 (2009).

    Article  CAS  Google Scholar 

  9. Ngo, V.N. et al. A loss-of-function RNA interference screen for molecular targets in cancer. Nature 441, 106–110 (2006).

    Article  CAS  Google Scholar 

  10. Scholl, C. et al. Synthetic lethal interaction between oncogenic KRAS dependency and STK33 suppression in human cancer cells. Cell 137, 821–834 (2009).

    Article  CAS  Google Scholar 

  11. Stegmeier, F., Hu, G., Rickles, R.J., Hannon, G.J. & Elledge, S.J. A lentiviral microRNA-based system for single-copy polymerase II-regulated RNA interference in mammalian cells. Proc. Natl. Acad. Sci. USA 102, 13212–13217 (2005).

    Article  CAS  Google Scholar 

  12. Gossen, M. et al. Transcriptional activation by tetracyclines in mammalian cells. Science 268, 1766–1769 (1995).

    Article  CAS  Google Scholar 

  13. Lund, A.H., Duch, M. & Pedersen, F.S. Transcriptional silencing of retroviral vectors. J. Biomed. Sci. 3, 365–378 (1996).

    Article  CAS  Google Scholar 

  14. Ellis, J., Hotta, A. & Rastegar, M. Retrovirus silencing by an epigenetic TRIM. Cell 131, 13–14 (2007).

    Article  CAS  Google Scholar 

  15. Markstein, M., Pitsouli, C., Villalta, C., Celniker, S.E. & Perrimon, N. Exploiting position effects and the gypsy retrovirus insulator to engineer precisely expressed transgenes. Nat. Genet. 40, 476–483 (2008).

    Article  CAS  Google Scholar 

  16. Pikaart, M.J., Recillas-Targa, F. & Felsenfeld, G. Loss of transcriptional activity of a transgene is accompanied by DNA methylation and histone deacetylation and is prevented by insulators. Genes Dev. 12, 2852–2862 (1998).

    Article  CAS  Google Scholar 

  17. Agha-Mohammadi, S. et al. Second-generation tetracycline-regulatable promoter: repositioned tet operator elements optimize transactivator synergy while shorter minimal promoter offers tight basal leakiness. J. Gene Med. 6, 817–828 (2004).

    Article  CAS  Google Scholar 

  18. Wold, M.S. Replication protein A: a heterotrimeric, single-stranded DNA-binding protein required for eukaryotic DNA metabolism. Annu. Rev. Biochem. 66, 61–92 (1997).

    Article  CAS  Google Scholar 

  19. Hochedlinger, K., Yamada, Y., Beard, C. & Jaenisch, R. Ectopic expression of Oct-4 blocks progenitor-cell differentiation and causes dysplasia in epithelial tissues. Cell 121, 465–477 (2005).

    Article  CAS  Google Scholar 

  20. Weinstein, I.B. Cancer. Addiction to oncogenes–the Achilles heal of cancer. Science 297, 63–64 (2002).

    Article  CAS  Google Scholar 

  21. Das, A.T. et al. Viral evolution as a tool to improve the tetracycline-regulated gene expression system. J. Biol. Chem. 279, 18776–18782 (2004).

    Article  CAS  Google Scholar 

  22. Meacham, C.E., Ho, E.E., Dubrovsky, E., Gertler, F.B. & Hemann, M.T. In vivo RNAi screening identifies regulators of actin dynamics as key determinants of lymphoma progression. Nat. Genet. 41, 1133–1137 (2009).

    Article  CAS  Google Scholar 

  23. Silva, J.M. et al. Profiling essential genes in human mammary cells by multiplex RNAi screening. Science 319, 617–620 (2008).

    Article  CAS  Google Scholar 

  24. Schlabach, M.R. et al. Cancer proliferation gene discovery through functional genomics. Science 319, 620–624 (2008).

    Article  CAS  Google Scholar 

  25. Bassik, M.C. et al. Rapid creation and quantitative monitoring of high coverage shRNA libraries. Nat. Methods 6, 443–445 (2009).

    Article  CAS  Google Scholar 

  26. Shin, K.J. et al. A single lentiviral vector platform for microRNA-based conditional RNA interference and coordinated transgene expression. Proc. Natl. Acad. Sci. USA 103, 13759–13764 (2006).

    Article  CAS  Google Scholar 

  27. Zuber, J. et al. Mouse models of human AML accurately predict chemotherapy response. Genes Dev. 23, 877–889 (2009).

    Article  CAS  Google Scholar 

  28. Huesken, D. et al. Design of a genome-wide siRNA library using an artificial neural network. Nat. Biotechnol. 23, 995–1001 (2005).

    Article  CAS  Google Scholar 

  29. McCurrach, M.E. & Lowe, S.W. Methods for studying pro- and antiapoptotic genes in nonimmortal cells. Methods Cell Biol. 66, 197–227 (2001).

    Article  CAS  Google Scholar 

  30. Schmitt, C.A. et al. Dissecting p53 tumor suppressor functions in vivo. Cancer Cell 1, 289–298 (2002).

    Article  CAS  Google Scholar 

  31. Taylor, J., Schenck, I., Blankenberg, D. & Nekrutenko, A. Using Galaxy to perform large-scale interactive data analyses. Curr. Protoc. Bioinformatics 10, 10.15 (2007).

    Google Scholar 

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Acknowledgements

We thank R. Dickins and C. Miething for discussions in setting up this system, as well as A. Lujambio, A. Rappaport, M. Saborowski and C. Vakoc for testing TRMPV in other models. We also thank Y. Dou (Univ. of Michigan) for providing human MLL-AF9. We gratefully acknowledge B. Ma and S. Muller for excellent technical assistance. We also thank E. Hodges, K. Chang, M. Rooks and the McCombie laboratory for help with Solexa sequencing as well as A. Gordon for bioinformatics support. P. Moody and T. Spencer were of great assistance in flow cytometry. Finally, we thank S. Kogan for histology. This work was supported by the Howard Hughes Medical Institute, the Starr Foundation and the Don Monti Memorial Research Foundation. J.Z. is the Andrew Seligson Memorial Fellow at Cold Spring Harbor Laboratory, and K.M. is the Robert and Teresa Lindsay Fellow of the Watson School of Biological Sciences. L.E.D. is supported by an Overseas Biomedical Research Fellowship of the National Health and Medical Research Council of Australia.

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  1. Johannes Zuber and Katherine McJunkin: These authors contributed equally to this work.

Authors and Affiliations

  1. Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA

    Johannes Zuber, Katherine McJunkin, Christof Fellmann, Lukas E Dow, Meredith J Taylor, Gregory J Hannon & Scott W Lowe

  2. Watson School of Biological Sciences, Cold Spring Harbor, New York, USA

    Katherine McJunkin, Gregory J Hannon & Scott W Lowe

  3. Howard Hughes Medical Institute, Cold Spring Harbor, New York, USA

    Gregory J Hannon & Scott W Lowe

  4. Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland

    Christof Fellmann

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  1. Johannes Zuber
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Contributions

J.Z. and K.M. designed and performed experiments. C.F. and L.E.D. contributed new reagents and performed experiments. M.J.T. managed mouse monitoring and husbandry. G.J.H. and S.W.L. supervised this project. J.Z., K.M. and S.W.L. wrote the paper.

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Correspondence to Scott W Lowe.

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The authors declare no competing financial interests.

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Zuber, J., McJunkin, K., Fellmann, C. et al. Toolkit for evaluating genes required for proliferation and survival using tetracycline-regulated RNAi. Nat Biotechnol 29, 79–83 (2011). https://doi.org/10.1038/nbt.1720

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