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General rules for functional microRNA targeting

Abstract

The functional rules for microRNA (miRNA) targeting remain controversial despite their biological importance because only a small fraction of distinct interactions, called site types, have been examined among an astronomical number of site types that can occur between miRNAs and their target mRNAs. To systematically discover functional site types and to evaluate the contradicting rules reported previously, we used large-scale transcriptome data and statistically examined whether each of approximately 2 billion site types is enriched in differentially downregulated mRNAs responding to overexpressed miRNAs. Accordingly, we identified seven non-canonical functional site types, most of which are novel, in addition to four canonical site types, while also removing numerous false positives reported by previous studies. Extensive experimental validation and significantly elevated 3′ UTR sequence conservation indicate that these non-canonical site types may have biologically relevant roles. Our expanded catalog of functional site types suggests that the gene regulatory network controlled by miRNAs may be far more complex than currently understood.

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Figure 1: Overview of analysis pipeline.
Figure 2: Sensitivity evaluation and discovery of functional NST candidates.
Figure 3: Discovery of functional context-dependent NST candidates and an expanded view on miRNA targeting.
Figure 4: Validation of the NSTs and CDNSTs by luciferase reporter assay.
Figure 5: Validation of NSTs and CDNSTs in independent microarray data sets.
Figure 6: Estimated impact of the NSTs and CDNSTs on overall mRNA repression.

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References

  1. Friedman, R.C., Farh, K.K.H., Burge, C.B. & Bartel, D.P. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 19, 92–105 (2009).

    Article  CAS  Google Scholar 

  2. Bartel, D.P. MicroRNAs: target recognition and regulatory functions. Cell 136, 215–233 (2009).

    Article  CAS  Google Scholar 

  3. Selbach, M. et al. Widespread changes in protein synthesis induced by microRNAs. Nature 455, 58–63 (2008).

    Article  CAS  Google Scholar 

  4. Melo, S.A. & Kalluri, R. miR-29b moulds the tumour microenvironment to repress metastasis. Nat. Cell Biol. 15, 139–140 (2013).

    Article  CAS  Google Scholar 

  5. Kasinski, A.L. & Slack, F.J. MicroRNAs en route to the clinic: progress in validating and targeting microRNAs for cancer therapy. Nat. Rev. Cancer 11, 849–864 (2011).

    Article  CAS  Google Scholar 

  6. Lewis, B.P., Burge, C.B. & Bartel, D.P. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15–20 (2005).

    Article  CAS  Google Scholar 

  7. Farh, K.K. et al. The widespread impact of mammalian microRNAs on mRNA repression and evolution. Science 310, 1817–1821 (2005).

    Article  CAS  Google Scholar 

  8. Krek, A. et al. Combinatorial microRNA target predictions. Nat. Genet. 37, 495–500 (2005).

    Article  CAS  Google Scholar 

  9. Grimson, A. et al. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol. Cell 27, 91–105 (2007).

    Article  CAS  Google Scholar 

  10. Baek, D. et al. The impact of microRNAs on protein output. Nature 455, 64–71 (2008).

    Article  CAS  Google Scholar 

  11. Brennecke, J., Stark, A., Russell, R.B. & Cohen, S.M. Principles of microRNA–target recognition. PLoS Biol. 3, e85 (2005).

    Article  Google Scholar 

  12. Anderson, E.M. et al. Experimental validation of the importance of seed complement frequency to siRNA specificity. RNA 14, 853–861 (2008).

    Article  CAS  Google Scholar 

  13. Lim, L.P. et al. Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433, 769–773 (2005).

    Article  CAS  Google Scholar 

  14. Gaidatzis, D., van Nimwegen, E., Hausser, J. & Zavolan, M. Inference of miRNA targets using evolutionary conservation and pathway analysis. BMC Bioinformatics 8, 69 (2007).

    Article  Google Scholar 

  15. Nielsen, C.B. et al. Determinants of targeting by endogenous and exogenous microRNAs and siRNAs. RNA 13, 1894–1910 (2007).

    Article  CAS  Google Scholar 

  16. Shin, C. et al. Expanding the microRNA targeting code: functional sites with centered pairing. Mol. Cell 38, 789–802 (2010).

    Article  CAS  Google Scholar 

  17. Chi, S.W., Zang, J.B., Mele, A. & Darnell, R.B. Argonaute HITS-CLIP decodes microRNA–mRNA interaction maps. Nature 460, 479–486 (2009).

    Article  CAS  Google Scholar 

  18. Hendrickson, D.G., Hogan, D.J., Herschlag, D., Ferrell, J.E. & Brown, P.O. Systematic identification of mRNAs recruited to Argonaute 2 by specific microRNAs and corresponding changes in transcript abundance. PLoS One 3, e2126 (2008).

    Article  Google Scholar 

  19. Chi, S.W., Hannon, G.J. & Darnell, R.B. An alternative mode of microRNA target recognition. Nat. Struct. Mol. Biol. 19, 321–327 (2012).

    Article  CAS  Google Scholar 

  20. König, J., Zarnack, K., Luscombe, N.M. & Ule, J. Protein–RNA interactions: new genomic technologies and perspectives. Nat. Rev. Genet. 13, 77–83 (2012).

    Article  Google Scholar 

  21. Majoros, W.H. et al. MicroRNA target site identification by integrating sequence and binding information. Nat. Methods 10, 630–633 (2013).

    Article  CAS  Google Scholar 

  22. Helwak, A., Kudla, G., Dudnakova, T. & Tollervey, D. Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153, 654–665 (2013).

    Article  CAS  Google Scholar 

  23. Kudla, G., Granneman, S., Hahn, D., Beggs, J.D. & Tollervey, D. Cross-linking, ligation, and sequencing of hybrids reveals RNA–RNA interactions in yeast. Proc. Natl. Acad. Sci. USA 108, 10010–10015 (2011).

    Article  CAS  Google Scholar 

  24. Khorshid, M., Hausser, J., Zavolan, M. & van Nimwegen, E. A biophysical miRNA–mRNA interaction model infers canonical and noncanonical targets. Nat. Methods 10, 253–255 (2013).

    Article  CAS  Google Scholar 

  25. Loeb, G.B. et al. Transcriptome-wide miR-155 binding map reveals widespread noncanonical microRNA targeting. Mol. Cell 48, 760–770 (2012).

    Article  CAS  Google Scholar 

  26. Corcoran, D.L. et al. PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data. Genome Biol. 12, R79 (2011).

    Article  CAS  Google Scholar 

  27. Agarwal, V., Bell, G.W., Nam, J.W. & Bartel, D.P. Predicting effective microRNA target sites in mammalian mRNAs. eLife 4, e05005 (2015).

    Article  Google Scholar 

  28. Lorenz, R. et al. ViennaRNA Package 2.0. Algorithms Mol. Biol. 6, 26 (2011).

    Article  Google Scholar 

  29. Wuchty, S., Fontana, W., Hofacker, I.L. & Schuster, P. Complete suboptimal folding of RNA and the stability of secondary structures. Biopolymers 49, 145–165 (1999).

    Article  CAS  Google Scholar 

  30. Garcia, D.M. et al. Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat. Struct. Mol. Biol. 18, 1139–1146 (2011).

    Article  CAS  Google Scholar 

  31. Schirle, N.T., Sheu-Gruttadauria, J. & MacRae, I.J. Structural basis for microRNA targeting. Science 346, 608–613 (2014).

    Article  CAS  Google Scholar 

  32. Ui-Tei, K., Naito, Y., Nishi, K., Juni, A. & Saigo, K. Thermodynamic stability and Watson–Crick base pairing in the seed duplex are major determinants of the efficiency of the siRNA-based off-target effect. Nucleic Acids Res. 36, 7100–7109 (2008).

    Article  CAS  Google Scholar 

  33. Arvey, A., Larsson, E., Sander, C., Leslie, C.S. & Marks, D.S. Target mRNA abundance dilutes microRNA and siRNA activity. Mol. Syst. Biol. 6, 363 (2010).

    Article  Google Scholar 

  34. Elmén, J. et al. Antagonism of microRNA-122 in mice by systemically administered LNA-antimiR leads to up-regulation of a large set of predicted target mRNAs in the liver. Nucleic Acids Res. 36, 1153–1162 (2008).

    Article  Google Scholar 

  35. Krützfeldt, J. et al. Silencing of microRNAs in vivo with 'antagomirs'. Nature 438, 685–689 (2005).

    Article  Google Scholar 

  36. Elmén, J. et al. LNA-mediated microRNA silencing in non-human primates. Nature 452, 896–899 (2008).

    Article  Google Scholar 

  37. Nicolas, F.E. et al. Experimental identification of microRNA-140 targets by silencing and overexpressing miR-140. RNA 14, 2513–2520 (2008).

    Article  CAS  Google Scholar 

  38. Zhao, Y. et al. Dysregulation of cardiogenesis, cardiac conduction, and cell cycle in mice lacking miRNA-1-2. Cell 129, 303–317 (2007).

    Article  CAS  Google Scholar 

  39. Vigorito, E. et al. microRNA-155 regulates the generation of immunoglobulin class-switched plasma cells. Immunity 27, 847–859 (2007).

    Article  CAS  Google Scholar 

  40. Rodriguez, A. et al. Requirement of bic/microRNA-155 for normal immune function. Science 316, 608–611 (2007).

    Article  CAS  Google Scholar 

  41. Giraldez, A.J. et al. Zebrafish MiR-430 promotes deadenylation and clearance of maternal mRNAs. Science 312, 75–79 (2006).

    Article  CAS  Google Scholar 

  42. Kishore, S. et al. A quantitative analysis of CLIP methods for identifying binding sites of RNA-binding proteins. Nat. Methods 8, 559–564 (2011).

    Article  CAS  Google Scholar 

  43. Pollard, K.S., Hubisz, M.J., Rosenbloom, K.R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).

    Article  CAS  Google Scholar 

  44. Eichhorn, S.W. et al. mRNA destabilization is the dominant effect of mammalian microRNAs by the time substantial repression ensues. Mol. Cell 56, 104–115 (2014).

    Article  CAS  Google Scholar 

  45. Kim, D., Kim, J. & Baek, D. Global and local competition between exogenously introduced microRNAs and endogenously expressed microRNAs. Mol. Cells 37, 412–417 (2014).

    Article  Google Scholar 

  46. Mayr, C. Evolution and biological roles of alternative 3 UTRs. Trends Cell Biol. 26, 227–237 (2016).

    Article  CAS  Google Scholar 

  47. Su, A.I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl. Acad. Sci. USA 101, 6062–6067 (2004).

    Article  CAS  Google Scholar 

  48. Linsley, P.S. et al. Transcripts targeted by the microRNA-16 family cooperatively regulate cell cycle progression. Mol. Cell. Biol. 27, 2240–2252 (2007).

    Article  CAS  Google Scholar 

  49. He, L. et al. A microRNA component of the p53 tumour suppressor network. Nature 447, 1130–1134 (2007).

    Article  CAS  Google Scholar 

  50. Ouzounova, M. et al. MicroRNA miR-30 family regulates non-attachment growth of breast cancer cells. BMC Genomics 14, 139 (2013).

    Article  CAS  Google Scholar 

  51. Nagaraja, A.K. et al. A link between mir-100 and FRAP1/mTOR in clear cell ovarian cancer. Mol. Endocrinol. 24, 447–463 (2010).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank V.N. Kim for generous research funding and insightful discussions, and we thank D.P. Bartel, J.W. Nam, S.W. Chi, and members of the Baek laboratory for helpful discussions. This work was supported by the Institute for Basic Science (IBS-R008-D1), the Ministry of Science ICT and Future Planning, Republic of Korea (NRF-2011-0014523, NRF-2014M3C9A3063541, F15SN01D1305, and 2012M3A9D1054622), the Ministry of Health and Welfare, Republic of Korea (HI15C1578 and HI15C3224), and the TJ Park Science Fellowship of the POSCO TJ Park Foundation.

Author information

Authors and Affiliations

Authors

Contributions

D.B. conceived and supervised the study. D.K., Jinman Park, Sukjun Kim, and J.K. performed computational analyses. Y.M.S. performed cloning and luciferase experiments. Junhee Park, H.H., J.Y.B., and SoHui Kim performed cloning experiments. D.B., D.K., Jinman Park, and Y.M.S. wrote the manuscript.

Corresponding author

Correspondence to Daehyun Baek.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Sensitivity evaluation with the 6mer ST.

The detection sensitivity of the analysis pipeline was evaluated with the canonical 6mer ST. Otherwise as in Figure 2a.

Supplementary Figure 2 ST enrichment analysis of the CSTs.

(ad) The iteration process of the analysis pipeline is demonstrated with sequential removal of the most significant STs, which happen to be known CSTs in the order of the 8mer ST (a), the 7mer-m8 ST (b), the 7mer-A1 ST (c), and the 6mer ST (d). After removing the 3′ UTRs containing the target sites of the CSTs, the enrichment P values of the remaining STs are more accurately estimated because of the complete removal of the interdependency between the CSTs and the remaining STs. The analyses using the 74 microarrays with and without AU-bias correction are plotted on the left and right, respectively. Otherwise as in Figure 2b.

Supplementary Figure 3 ST enrichment analysis without AU-bias correction.

(a) ST enrichment analysis conducted without AU-bias correction after removing 3′ UTRs containing the CSTs. Otherwise as in Figure 2b. (b) ST enrichment analysis iterated after removing 3′ UTRs with the CSTs and 6mer-A1, offset 7mer, and offset 6mer STs. A few STs that marginally pass the multiple-test cutoff in this plot were found to be insignificant after combining the results from the 74 microarrays with and without AU-bias correction using Fisher’s method (Online Methods). Otherwise as in Figure 2b.

Supplementary Figure 4 An expanded view on miRNA targeting without AU-bias correction.

An expanded view on miRNA targeting is as shown including the CSTs and the newly discovered NSTs and CDNSTs with analysis conducted using the 74 microarrays without AU-bias correction. Otherwise as in Figure 3b.

Source data

Supplementary Figure 5 Validation for the NSTs and CDNSTs by luciferase reporter assay.

(a) Luciferase reporter assay of a non-functional ST 1, ØOOØOØØOØØB, reported by our analysis was used as a negative control (Online Methods). Otherwise as in Figure 4. (b) Luciferase reporter assay of non-functional ST 2, OØOOØOØØOØB. (c) Luciferase reporter assay of non-functional ST 3, ØOØØOOOØØOB. (d) Luciferase reporter assay for validation of the relative proficiencies of the newly discovered NSTs and CDNSTs for a single miRNA. The target 3′ UTR, KATNA1, containing a strong single 8mer site of miR-124 was selected for evaluation. To determine relative proficiencies, the 8mer site was sequentially mutated to the other CSTs (7mer-m8, 7mer-A1, and 6mer), NSTs (6mer-A1, offset 7mer, and offset 6mer), and CDNSTs (CDNSTs 1–4) and their efficacies were evaluated (Online Methods). Otherwise as in Figure 4. (e) Luciferase reporter assay for target sites of miR-155-5p on ZC2HC1B. (f) Luciferase reporter assay for target sites of miR-223-3p on GALNT18.

Source data

Supplementary Figure 6 Validation of NSTs and CDNSTs in independent microarrays without AU-bias correction.

(a) Validation with microarrays obtained from miRNA knockout experiments in mouse and maternal-zygotic (MZ) Dicer null in zebrafish without AU-bias correction. Otherwise as in Figure 5a. (b) Validation with microarrays obtained from miRNA knockdown experiments in ES2, OVSAYO, and MCF7 cell lines without AU-bias correction. Otherwise as in Figure 5b. (c) Validation with microarrays in response to sRNAs ectopically introduced into HCT116, ES2, OVSAYO, and MCF7 cell lines without AU-bias correction. Otherwise as in Figure 5c.

Source data

Supplementary Figure 7 Validation analyses of NSTs and CDNSTs in individual microarrays with AU-bias correction.

(a) MZ Dicer null in zebrafish. Otherwise as in Figure 5a. (b) miRNA knockout in mouse. Otherwise as in Figure 5a. (c) sRNA overexpression in HCT116 cell line. Otherwise as in Figure 5c. (d) miRNA overexpression in ES2, OVSAYO, and MCF7 cell lines. Otherwise as in Figure 5c.

Source data

Supplementary Figure 8 Validation analyses of NSTs and CDNSTs in individual microarrays without AU-bias correction.

(a) MZ Dicer null in zebrafish. Otherwise as in Figure 5a. (b) miRNA knockout in mouse. Otherwise as in Figure 5a. (c) sRNA overexpression in HCT116 cell line. Otherwise as in Figure 5c. (d) miRNA overexpression in ES2, OVSAYO, and MCF7 cell lines. Otherwise as in Figure 5c.

Source data

Supplementary Figure 9 Estimated impact of the NSTs and CDNSTs on overall mRNA repression.

Using the 74 microarray data sets, the amount of overall mRNA repression of the NSTs and CDNSTs is approximated to be ~56% that of the CSTs. From the microarray data monitoring the transcriptome response after sRNAs were ectopically introduced into HCT116, ES2, OVSAYO, and MCF7 cell lines, the overall mRNA repression of the NSTs and CDNSTs was estimated to be ~69% that of the CSTs. The number of targets for the CDNSTs was limited to those with the top 25% context scores. Otherwise as in Figure 6c.

Supplementary Figure 10 Estimated impact of the NSTs and CDNSTs on overall mRNA derepression/repression without AU-bias correction.

(a) The amount of overall mRNA derepression of the NSTs and CDNSTs approximated with microarray data monitoring the transcriptome response after miRNA knockdown/knockout in human and mouse cells and MZ Dicer null in zebrafish was estimated to be ~83% that of the CSTs (Online Methods). Otherwise as in Figure 6c. (b) Using the 74 microarray data sets, the amount of overall mRNA repression of the NSTs and CDNSTs is approximated to be ~70% that of the CSTs. From the microarray data monitoring the transcriptome response after sRNAs were ectopically introduced into HCT116, ES2, OVSAYO, and MCF7 cell lines, the overall mRNA repression of the NSTs and CDNSTs was estimated to be ~60% that of the CSTs. Otherwise as in Figure 6c.

Supplementary Figure 11 Estimated impact of the NSTs and CDNSTs on overall mRNA derepression/repression after considering only target 3′ UTRs containing a single target site.

(a) The amount of overall mRNA derepression of the NSTs and CDNSTs from the microarray data monitoring the transcriptome response after miRNA knockdown/knockout in human and mouse cells and MZ Dicer null in zebrafish was approximated by considering only target 3′ UTRs containing a single target site. The amount was estimated to be ~32% and 31% that of the CSTs in microarray data with and without AU-bias correction, respectively (Online Methods). Otherwise as in Figure 6c. (b) The amount of overall mRNA repression of the NSTs and CDNSTs was approximated to be ~28% and 33% that of the CSTs in the 74 microarray data sets with and without AU-bias correction, respectively. From the microarray data monitoring the transcriptome response after sRNAs were ectopically introduced into HCT116, ES2, OVSAYO, and MCF7 cell lines, the overall mRNA repression of the NSTs and CDNSTs was estimated to be ~33% and 29% that of the CSTs with and without AU-bias correction, respectively. Otherwise as in Figure 6c.

Supplementary Figure 12 Comparison of the ST enrichment analysis.

(a,b) After removing the 3′ UTRs containing the target sites of the CSTs, ST enrichment analysis was conducted with 3′ UTRs organized into ten bins (a) or five bins (b). The analyses using the 74 microarrays with and without AU-bias correction are plotted on the left and right, respectively. With five bins, there is a loss of >2 million STs represented by the reduction of peaks at the highly downregulated 10% (red vertical line), whose enrichment P values do not pass the Bonferroni cutoff with the highly downregulated 20% 3′ UTRs. Otherwise as in Figure 2b.

Supplementary Figure 13 Detailed flowchart of our analysis pipeline for the discovery of functional NST candidates, including our various filtering parameters.

Supplementary Figure 14 Detailed flowchart of our analysis pipeline for the discovery of functional context-dependent NST candidates, including our various filtering parameters.

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Kim, D., Sung, Y., Park, J. et al. General rules for functional microRNA targeting. Nat Genet 48, 1517–1526 (2016). https://doi.org/10.1038/ng.3694

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