Interrogation of Functional miRNA–Target Interactions by CRISPR/Cas9 Genome Engineering

  • Yale S. Michaels
  • Qianxin Wu
  • Tudor A. FulgaEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1580)


Post-transcriptional silencing by microRNAs (miRNAs) is a critical constituent of eukaryotic gene regulation. miRNAs are short (~22nt) noncoding RNAs capable of specifically targeting the miRNA-induced-silencing-complex (miRISC) to transcripts bearing a complementary miRNA response element (MRE). Although recent methodological advances have greatly improved our understanding of miRNA biogenesis and the mechanisms by which miRNAs repress their cognate targets, exploring the physiological relevance of direct miRNA–target interactions in vivo has remained an outstanding challenge. Here we describe the experimental protocol underlying a novel approach, which allows direct interrogation of specific miRNA–MRE interactions by CRISPR/Cas9-mediated genome engineering. In this instance, the CRISPR/Cas9 system is first used to catalyze homology-directed replacement of candidate MREs with molecular barcodes at endogenous loci. Subsequently, the effect of MRE mutation on transcript abundance (i.e., MRE activity) can be rapidly evaluated by routine quantitative PCR. This strategy enables functional investigation of a putative miRNA–target pair in a pool of transiently transfected cells, obviating the need for generation of clonal cell lines or transgenic animals. This protocol can be implemented in any cell line in less than 2 weeks, and can readily be scaled up for multiplex studies. To facilitate the conceptual workflow underlying this strategy, we also describe a genome-wide resource for automated design and computational evaluation of CRISPR/Cas9 guide RNAs targeting all predicted MREs in various species (miR-CRISPR).

Key words

microRNAs miRNA miRNA response elements (MRE) Genome engineering CRISPR/Cas9 



We thank Andrew Basset for critical comments on the manuscript and the Fulga lab for feedback and advice. Y.S.M is supported by the Clarendon Scholarship; WIMM Prize Fellowship; Christopher Welch Scholarship. Q.W is supported by the Medical Research Council (WIMM Strategic Award, MRC #G0902418 to T.A.F.). T.A.F. is supported by the Medical Research Council (WIMM Strategic Award, MRC #G0902418); Biotechnology and Biological Sciences Research Council (Project Grants #BB/L010275/1 and #BB/N006550/1); Welcome Trust ISSF Award (#105605/Z/14/Z).


  1. 1.
    Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116:281–297CrossRefPubMedGoogle Scholar
  2. 2.
    Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136:215–233CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120:15–20CrossRefPubMedGoogle Scholar
  4. 4.
    Peterson SM, Thompson JA, Ufkin ML, Sathyanarayana P, Liaw L, Congdon CB (2014) Common features of microRNA target prediction tools. Front Genet 5:23CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Li Y, Zhang Z (2015) Computational biology in microRNA. Wiley Interdiscip Rev RNA 6:435–452CrossRefPubMedGoogle Scholar
  6. 6.
    Oulas A, Karathanasis N, Louloupi A, Pavlopoulos GA, Poirazi P, Kalantidis K, Iliopoulos I (2015) Prediction of miRNA targets. Methods Mol Biol 1269:207–229CrossRefPubMedGoogle Scholar
  7. 7.
    Friedman RC, Farh KK, Burge CB, Bartel DP (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 19:92–105CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, Castle J, Bartel DP, Linsley PS, Johnson JM (2005) Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433:769–773CrossRefPubMedGoogle Scholar
  9. 9.
    Selbach M, Schwanhausser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N (2008) Widespread changes in protein synthesis induced by microRNAs. Nature 455:58–63CrossRefPubMedGoogle Scholar
  10. 10.
    Xu G, Fewell C, Taylor C, Deng N, Hedges D, Wang X, Zhang K, Lacey M, Zhang H, Yin Q, Cameron J, Lin Z, Zhu D, Flemington EK (2010) Transcriptome and targetome analysis in MIR155 expressing cells using RNA-seq. RNA 16:1610–1622CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Mansfield JH, Harfe BD, Nissen R, Obenauer J, Srineel J, Chaudhuri A, Farzan-Kashani R, Zuker M, Pasquinelli AE, Ruvkun G, Sharp PA, Tabin CJ, McManus MT (2004) MicroRNA-responsive ‘sensor’ transgenes uncover Hox-like and other developmentally regulated patterns of vertebrate microRNA expression. Nat Genet 36:1079–1083CrossRefPubMedGoogle Scholar
  12. 12.
    Chi SW, Zang JB, Mele A, Darnell RB (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 460:479–486PubMedPubMedCentralGoogle Scholar
  13. 13.
    Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, Rothballer A, Ascano M Jr, Jungkamp AC, Munschauer M, Ulrich A, Wardle GS, Dewell S, Zavolan M, Tuschl T (2010) Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141:129–141CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Helwak A, Kudla G, Dudnakova T, Tollervey D (2013) Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153:654–665CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Agarwal V, Bell GW, Nam JW, Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. Elife doi:  10.7554/eLife.05005
  16. 16.
    Bassett AR, Azzam G, Wheatley L, Tibbit C, Rajakumar T, McGowan S, Stanger N, Ewels PA, Taylor S, Ponting CP, Liu JL, Sauka-Spengler T, Fulga TA (2014) Understanding functional miRNA-target interactions in vivo by site-specific genome engineering. Nat Commun 5:4640CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Sternberg SH, Doudna JA (2015) Expanding the biologist’s toolkit with CRISPR-Cas9. Mol Cell 58:568–574CrossRefPubMedGoogle Scholar
  18. 18.
    Maruyama T, Dougan SK, Truttmann MC, Bilate AM, Ingram JR, Ploegh HL (2015) Increasing the efficiency of precise genome editing with CRISPR-Cas9 by inhibition of nonhomologous end joining. Nat Biotechnol 33:538–542CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Chu VT, Weber T, Wefers B, Wurst W, Sander S, Rajewsky K, Kuhn R (2015) Increasing the efficiency of homology-directed repair for CRISPR-Cas9-induced precise gene editing in mammalian cells. Nat Biotechnol 33:543–548CrossRefPubMedGoogle Scholar
  20. 20.
    Pyzocha NK, Ran FA, Hsu PD, Zhang F (2014) RNA-guided genome editing of mammalian cells. Methods Mol Biol 1114:269–277CrossRefPubMedGoogle Scholar
  21. 21.
    Aricescu AR, Lu W, Jones EY (2006) A time- and cost-efficient system for high-level protein production in mammalian cells. Acta Crystallogr D Biol Crystallogr 62:1243–1250CrossRefPubMedGoogle Scholar
  22. 22.
    Fraley SI, Hardick J, Jo Masek B, Athamanolap P, Rothman RE, Gaydos CA, Carroll KC, Wakefield T, Wang TH, Yang S (2013) Universal digital high-resolution melt: a novel approach to broad-based profiling of heterogeneous biological samples. Nucleic Acids Res 41:e175CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Yale S. Michaels
    • 1
  • Qianxin Wu
    • 1
  • Tudor A. Fulga
    • 1
    Email author
  1. 1.Radcliffe Department of Medicine, Weatherall Institute of Molecular MedicineUniversity of OxfordOxfordUK

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