Molecules and Cells

  • 제46권1호
  • /
  • Pages.21-32
  • /
  • 2023
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  • 1016-8478(pISSN)
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  • 0219-1032(eISSN)
한국분자세포생물학회 (Korean Society for Molecular and Cellular Biology)
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Determinants of Functional MicroRNA Targeting

  • Hyeonseo Hwang (School of Biological Sciences, Seoul National University) ;
  • Hee Ryung Chang (School of Biological Sciences, Seoul National University) ;
  • Daehyun Baek (School of Biological Sciences, Seoul National University)
  • 투고 : 2022.10.14
  • 심사 : 2022.11.15
  • 발행 : 2023.01.31
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⟨ 이전 논문 다음 논문 ⟩

초록

MicroRNAs (miRNAs) play cardinal roles in regulating biological pathways and processes, resulting in significant physiological effects. To understand the complex regulatory network of miRNAs, previous studies have utilized massivescale datasets of miRNA targeting and attempted to computationally predict the functional targets of miRNAs. Many miRNA target prediction tools have been developed and are widely used by scientists from various fields of biology and medicine. Most of these tools consider seed pairing between miRNAs and their mRNA targets and additionally consider other determinants to improve prediction accuracy. However, these tools exhibit limited prediction accuracy and high false positive rates. The utilization of additional determinants, such as RNA modifications and RNA-binding protein binding sites, may further improve miRNA target prediction. In this review, we discuss the determinants of functional miRNA targeting that are currently used in miRNA target prediction and the potentially predictive but unappreciated determinants that may improve prediction accuracy.

키워드

과제정보

This study was supported by the National Research Foundation of Korea (NRF), which is funded by the Ministry of Science and ICT, Republic of Korea (NRF-2014M3C9A3063541, NRF-2019M3E5D3073104, NRF-2020R1A2C3007032, NRF2020R1A5A1018081, and NRF-2022M3A9I2082294), the Korea Health Industry Development Institute (KHIDI), which is funded by the Ministry of Health and Welfare, Republic of Korea (HI15C3224), and the Korea National Institute of Health (KNIH) which is funded by the Korea Disease Control and Prevention Agency (KDCA) (2022-ER1605-00).

참고문헌

  1. Agarwal, V., Bell, G.W., Nam, J.W., and Bartel, D.P. (2015). Predicting effective microRNA target sites in mammalian mRNAs. Elife 4, e05005.
  2. Ahuja, D., Goyal, A., and Ray, P.S. (2016). Interplay between RNA-binding protein HuR and microRNA-125b regulates p53 mRNA translation in response to genotoxic stress. RNA Biol. 13, 1152-1165. https://doi.org/10.1080/15476286.2016.1229734
  3. Alarcon, C.R., Goodarzi, H., Lee, H., Liu, X., Tavazoie, S., and Tavazoie, S.F. (2015a). HNRNPA2B1 is a mediator of m6A-dependent nuclear RNA processing events. Cell 162, 1299-1308. https://doi.org/10.1016/j.cell.2015.08.011
  4. Alarcon, C.R., Lee, H., Goodarzi, H., Halberg, N., and Tavazoie, S.F. (2015b). N6-methyladenosine marks primary microRNAs for processing. Nature 519, 482-485. https://doi.org/10.1038/nature14281
  5. Ameres, S.L., Horwich, M.D., Hung, J.H., Xu, J., Ghildiyal, M., Weng, Z., and Zamore, P.D. (2010). Target RNA-directed trimming and tailing of small silencing RNAs. Science 328, 1534-1539. https://doi.org/10.1126/science.1187058
  6. Arvey, A., Larsson, E., Sander, C., Leslie, C.S., and Marks, D.S. (2010). Target mRNA abundance dilutes microRNA and siRNA activity. Mol. Syst. Biol. 6, 363.
  7. Baek, D., Villen, J., Shin, C., Camargo, F.D., Gygi, S.P., and Bartel, D.P. (2008). The impact of microRNAs on protein output. Nature 455, 64-71. https://doi.org/10.1038/nature07242
  8. Bartel, D.P. (2009). MicroRNAs: target recognition and regulatory functions. Cell 136, 215-233. https://doi.org/10.1016/j.cell.2009.01.002
  9. Bass, B.L. and Weintraub, H. (1988). An unwinding activity that covalently modifies its double-stranded-RNA substrate. Cell 55, 1089-1098. https://doi.org/10.1016/0092-8674(88)90253-X
  10. Bernhart, S.H., Hofacker, I.L., and Stadler, P.F. (2006). Local RNA base pairing probabilities in large sequences. Bioinformatics 22, 614-615. https://doi.org/10.1093/bioinformatics/btk014
  11. Bernstein, E., Kim, S.Y., Carmell, M.A., Murchison, E.P., Alcorn, H., Li, M.Z., Mills, A.A., Elledge, S.J., Anderson, K.V., and Hannon, G.J. (2003). Dicer is essential for mouse development. Nat. Genet. 35, 215-217. https://doi.org/10.1038/ng1253
  12. Betel, D., Koppal, A., Agius, P., Sander, C., and Leslie, C. (2010). Comprehensive modeling of microRNA targets predicts functional nonconserved and non-canonical sites. Genome Biol. 11, R90.
  13. Bitetti, A., Mallory, A.C., Golini, E., Carrieri, C., Carreno Gutierrez, H., Perlas, E., Perez-Rico, Y.A., Tocchini-Valentini, G.P., Enright, A.J., Norton, W.H.J., et al. (2018). MicroRNA degradation by a conserved target RNA regulates animal behavior. Nat. Struct. Mol. Biol. 25, 244-251. https://doi.org/10.1038/s41594-018-0032-x
  14. Blow, M.J., Grocock, R.J., van Dongen, S., Enright, A.J., Dicks, E., Futreal, P.A., Wooster, R., and Stratton, M.R. (2006). RNA editing of human microRNAs. Genome Biol. 7, R27.
  15. Bohnsack, K.E., Hobartner, C., and Bohnsack, M.T. (2019). Eukaryotic 5-methylcytosine (m5C) RNA methyltransferases: mechanisms, cellular functions, and links to disease. Genes (Basel) 10, 102.
  16. Bosson, A.D., Zamudio, J.R., and Sharp, P.A. (2014). Endogenous miRNA and target concentrations determine susceptibility to potential ceRNA competition. Mol. Cell 56, 347-359. https://doi.org/10.1016/j.molcel.2014.09.018
  17. Bracken, C.P., Scott, H.S., and Goodall, G.J. (2016). A network-biology perspective of microRNA function and dysfunction in cancer. Nat. Rev. Genet. 17, 719-732. https://doi.org/10.1038/nrg.2016.134
  18. Brennan, G.P. and Henshall, D.C. (2020). MicroRNAs as regulators of brain function and targets for treatment of epilepsy. Nat. Rev. Neurol. 16, 506-519. https://doi.org/10.1038/s41582-020-0369-8
  19. Briskin, D., Wang, P.Y., and Bartel, D.P. (2020). The biochemical basis for the cooperative action of microRNAs. Proc. Natl. Acad. Sci. U. S. A. 117, 17764-17774. https://doi.org/10.1073/pnas.1920404117
  20. Care, A., Catalucci, D., Felicetti, F., Bonci, D., Addario, A., Gallo, P., Bang, M.L., Segnalini, P., Gu, Y., Dalton, N.D., et al. (2007). MicroRNA-133 controls cardiac hypertrophy. Nat. Med. 13, 613-618. https://doi.org/10.1038/nm1582
  21. Cazalla, D., Yario, T., and Steitz, J.A. (2010). Down-regulation of a host microRNA by a Herpesvirus saimiri noncoding RNA. Science 328, 1563-1566. https://doi.org/10.1126/science.1187197
  22. Cesana, M., Cacchiarelli, D., Legnini, I., Santini, T., Sthandier, O., Chinappi, M., Tramontano, A., and Bozzoni, I. (2011). A long noncoding RNA controls muscle differentiation by functioning as a competing endogenous RNA. Cell 147, 358-369. https://doi.org/10.1016/j.cell.2011.09.028
  23. Cesarini, V., Silvestris, D.A., Tassinari, V., Tomaselli, S., Alon, S., Eisenberg, E., Locatelli, F., and Gallo, A. (2018). ADAR2/miR-589-3p axis controls glioblastoma cell migration/invasion. Nucleic Acids Res. 46, 2045-2059. https://doi.org/10.1093/nar/gkx1257
  24. Cheray, M., Etcheverry, A., Jacques, C., Pacaud, R., Bougras-Cartron, G., Aubry, M., Denoual, F., Peterlongo, P., Nadaradjane, A., Briand, J., et al. (2020). Cytosine methylation of mature microRNAs inhibits their functions and is associated with poor prognosis in glioblastoma multiforme. Mol. Cancer 19, 36.
  25. Chi, S.W., Zang, J.B., Mele, A., and Darnell, R.B. (2009). Argonaute HITSCLIP decodes microRNA-mRNA interaction maps. Nature 460, 479-486. https://doi.org/10.1038/nature08170
  26. Choi, W.Y., Giraldez, A.J., and Schier, A.F. (2007). Target protectors reveal dampening and balancing of Nodal agonist and antagonist by miR-430. Science 318, 271-274. https://doi.org/10.1126/science.1147535
  27. Denzler, R., Agarwal, V., Stefano, J., Bartel, D.P., and Stoffel, M. (2014). Assessing the ceRNA hypothesis with quantitative measurements of miRNA and target abundance. Mol. Cell 54, 766-776. https://doi.org/10.1016/j.molcel.2014.03.045
  28. Dominissini, D., Moshitch-Moshkovitz, S., Schwartz, S., Salmon-Divon, M., Ungar, L., Osenberg, S., Cesarkas, K., Jacob-Hirsch, J., Amariglio, N., Kupiec, M., et al. (2012). Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485, 201-206. https://doi.org/10.1038/nature11112
  29. Fang, Z. and Rajewsky, N. (2011). The impact of miRNA target sites in coding sequences and in 3'UTRs. PLoS One 6, e18067.
  30. Fededa, J.P., Esk, C., Mierzwa, B., Stanyte, R., Yuan, S., Zheng, H., Ebnet, K., Yan, W., Knoblich, J.A., and Gerlich, D.W. (2016). MicroRNA-34/449 controls mitotic spindle orientation during mammalian cortex development. EMBO J. 35, 2386-2398. https://doi.org/10.15252/embj.201694056
  31. Fontana, L., Pelosi, E., Greco, P., Racanicchi, S., Testa, U., Liuzzi, F., Croce, C.M., Brunetti, E., Grignani, F., and Peschle, C. (2007). MicroRNAs 17-5p-20a-106a control monocytopoiesis through AML1 targeting and M-CSF receptor upregulation. Nat. Cell Biol. 9, 775-787. https://doi.org/10.1038/ncb1613
  32. Friedman, R.C., Farh, K.K., Burge, C.B., and Bartel, D.P. (2009). Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 19, 92-105. https://doi.org/10.1101/gr.082701.108
  33. Fuchs Wightman, F., Giono, L.E., Fededa, J.P., and de la Mata, M. (2018). Target RNAs strike back on microRNAs. Front. Genet. 9, 435.
  34. Gaidatzis, D., van Nimwegen, E., Hausser, J., and Zavolan, M. (2007). Inference of miRNA targets using evolutionary conservation and pathway analysis. BMC Bioinformatics 8, 69.
  35. Garcia, D.M., Baek, D., Shin, C., Bell, G.W., Grimson, A., and Bartel, D.P. (2011). 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. https://doi.org/10.1038/nsmb.2115
  36. Ghanbarian, H., Yildiz, M.T., and Tutar, Y. (2022). MicroRNA targeting. Methods Mol. Biol. 2257, 105-130. https://doi.org/10.1007/978-1-0716-1170-8_6
  37. Ghini, F., Rubolino, C., Climent, M., Simeone, I., Marzi, M.J., and Nicassio, F. (2018). Endogenous transcripts control miRNA levels and activity in mammalian cells by target-directed miRNA degradation. Nat. Commun. 9, 3119.
  38. Ghosh, T., Aprea, J., Nardelli, J., Engel, H., Selinger, C., Mombereau, C., Lemonnier, T., Moutkine, I., Schwendimann, L., Dori, M., et al. (2014). MicroRNAs establish robustness and adaptability of a critical gene network to regulate progenitor fate decisions during cortical neurogenesis. Cell Rep. 7, 1779-1788. https://doi.org/10.1016/j.celrep.2014.05.029
  39. Giraldez, A.J., Cinalli, R.M., Glasner, M.E., Enright, A.J., Thomson, J.M., Baskerville, S., Hammond, S.M., Bartel, D.P., and Schier, A.F. (2005). MicroRNAs regulate brain morphogenesis in zebrafish. Science 308, 833-838. https://doi.org/10.1126/science.1109020
  40. Gregory, P.A., Bert, A.G., Paterson, E.L., Barry, S.C., Tsykin, A., Farshid, G., Vadas, M.A., Khew-Goodall, Y., and Goodall, G.J. (2008). The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1. Nat. Cell Biol. 10, 593-601. https://doi.org/10.1038/ncb1722
  41. Gregory, R.I., Chendrimada, T.P., Cooch, N., and Shiekhattar, R. (2005). Human RISC couples microRNA biogenesis and posttranscriptional gene silencing. Cell 123, 631-640. https://doi.org/10.1016/j.cell.2005.10.022
  42. Grimson, A., Farh, K.K., Johnston, W.K., Garrett-Engele, P., Lim, L.P., and Bartel, D.P. (2007). MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol. Cell 27, 91-105. https://doi.org/10.1016/j.molcel.2007.06.017
  43. Gumienny, R. and Zavolan, M. (2015). Accurate transcriptome-wide prediction of microRNA targets and small interfering RNA off-targets with MIRZA-G. Nucleic Acids Res. 43, 1380-1391. https://doi.org/10.1093/nar/gkv050
  44. Hammond, S.M., Bernstein, E., Beach, D., and Hannon, G.J. (2000). An RNA-directed nuclease mediates post-transcriptional gene silencing in Drosophila cells. Nature 404, 293-296. https://doi.org/10.1038/35005107
  45. Han, J., LaVigne, C.A., Jones, B.T., Zhang, H., Gillett, F., and Mendell, J.T. (2020a). A ubiquitin ligase mediates target-directed microRNA decay independently of tailing and trimming. Science 370, eabc9546.
  46. Han, X., Luan, T., Sun, Y., Yan, W., Wang, D., and Zeng, X. (2020b). MicroRNA 449c mediates the generation of monocytic myeloid-derived suppressor cells by targeting STAT6. Mol. Cells 43, 793-803.
  47. Hansen, T.B., Jensen, T.I., Clausen, B.H., Bramsen, J.B., Finsen, B., Damgaard, C.K., and Kjems, J. (2013). Natural RNA circles function as efficient microRNA sponges. Nature 495, 384-388. https://doi.org/10.1038/nature11993
  48. Harris, T.A., Yamakuchi, M., Ferlito, M., Mendell, J.T., and Lowenstein, C.J. (2008). MicroRNA-126 regulates endothelial expression of vascular cell adhesion molecule 1. Proc. Natl. Acad. Sci. U. S. A. 105, 1516-1521. https://doi.org/10.1073/pnas.0707493105
  49. Hassan, T., Smith, S.G., Gaughan, K., Oglesby, I.K., O'Neill, S., McElvaney, N.G., and Greene, C.M. (2013). Isolation and identification of cell-specific microRNAs targeting a messenger RNA using a biotinylated anti-sense oligonucleotide capture affinity technique. Nucleic Acids Res. 41, e71.
  50. Hausser, J., Landthaler, M., Jaskiewicz, L., Gaidatzis, D., and Zavolan, M. (2009). Relative contribution of sequence and structure features to the mRNA binding of Argonaute/EIF2C-miRNA complexes and the degradation of miRNA targets. Genome Res. 19, 2009-2020. https://doi.org/10.1101/gr.091181.109
  51. Hong, X., Hammell, M., Ambros, V., and Cohen, S.M. (2009). Immunopurification of Ago1 miRNPs selects for a distinct class of microRNA targets. Proc. Natl. Acad. Sci. U. S. A. 106, 15085-15090. https://doi.org/10.1073/pnas.0908149106
  52. Iyer, V.R., Eisen, M.B., Ross, D.T., Schuler, G., Moore, T., Lee, J.C., Trent, J.M., Staudt, L.M., Hudson, J., Jr., Boguski, M.S., et al. (1999). The transcriptional program in the response of human fibroblasts to serum. Science 283, 83-87. https://doi.org/10.1126/science.283.5398.83
  53. Jain, M., Olsen, H.E., Paten, B., and Akeson, M. (2016). The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community. Genome Biol. 17, 239.
  54. Jan, C.H., Friedman, R.C., Ruby, J.G., and Bartel, D.P. (2011). Formation, regulation and evolution of Caenorhabditis elegans 3'UTRs. Nature 469, 97-101. https://doi.org/10.1038/nature09616
  55. Jang, H., Park, S., Kim, J., Kim, J.H., Kim, S.Y., Cho, S., Park, S.G., Park, B.C., Kim, S., and Kim, J.H. (2020). The tumor suppressor, p53, negatively regulates non-canonical NF-κB signaling through miRNA-induced silencing of NF-κB-inducing kinase. Mol. Cells 43, 23-33.
  56. Jens, M. and Rajewsky, N. (2015). Competition between target sites of regulators shapes post-transcriptional gene regulation. Nat. Rev. Genet. 16, 113-126. https://doi.org/10.1038/nrg3853
  57. Ji, Y., Zhou, Z., Liu, H., and Davuluri, R.V. (2021). DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNAlanguage in genome. Bioinformatics 37, 2112-2120. https://doi.org/10.1093/bioinformatics/btab083
  58. Jing, Q., Huang, S., Guth, S., Zarubin, T., Motoyama, A., Chen, J., Di Padova, F., Lin, S.C., Gram, H., and Han, J. (2005). Involvement of microRNA in AUrich element-mediated mRNA instability. Cell 120, 623-634. https://doi.org/10.1016/j.cell.2004.12.038
  59. Karreth, F.A., Reschke, M., Ruocco, A., Ng, C., Chapuy, B., Leopold, V., Sjoberg, M., Keane, T.M., Verma, A., Ala, U., et al. (2015). The BRAF pseudogene functions as a competitive endogenous RNA and induces lymphoma in vivo. Cell 161, 319-332. https://doi.org/10.1016/j.cell.2015.02.043
  60. Kawahara, Y., Zinshteyn, B., Sethupathy, P., Iizasa, H., Hatzigeorgiou, A.G., and Nishikura, K. (2007). Redirection of silencing targets by adenosine-toinosine editing of miRNAs. Science 315, 1137-1140. https://doi.org/10.1126/science.1138050
  61. Kedde, M., van Kouwenhove, M., Zwart, W., Oude Vrielink, J.A., Elkon, R., and Agami, R. (2010). A Pumilio-induced RNA structure switch in p27-3' UTR controls miR-221 and miR-222 accessibility. Nat. Cell Biol. 12, 1014-1020. https://doi.org/10.1038/ncb2105
  62. Keene, J.D., Komisarow, J.M., and Friedersdorf, M.B. (2006). RIP-Chip: the isolation and identification of mRNAs, microRNAs and protein components of ribonucleoprotein complexes from cell extracts. Nat. Protoc. 1, 302-307. https://doi.org/10.1038/nprot.2006.47
  63. Kertesz, M., Iovino, N., Unnerstall, U., Gaul, U., and Segal, E. (2007). The role of site accessibility in microRNA target recognition. Nat. Genet. 39, 1278-1284. https://doi.org/10.1038/ng2135
  64. Kim, D., Kim, J., and Baek, D. (2014). Global and local competition between exogenously introduced microRNAs and endogenously expressed microRNAs. Mol. Cells 37, 412-417. https://doi.org/10.14348/molcells.2014.0100
  65. Kim, D., Sung, Y.M., Park, J., Kim, S., Kim, J., Park, J., Ha, H., Bae, J.Y., Kim, S., and Baek, D. (2016). General rules for functional microRNA targeting. Nat. Genet. 48, 1517-1526. https://doi.org/10.1038/ng.3694
  66. Kim, S., Kim, S., Chang, H.R., Kim, D., Park, J., Son, N., Park, J., Yoon, M., Chae, G., Kim, Y.K., et al. (2021). The regulatory impact of RNA-binding proteins on microRNA targeting. Nat. Commun. 12, 5057.
  67. Kleaveland, B., Shi, C.Y., Stefano, J., and Bartel, D.P. (2018). A network of noncoding regulatory RNAs acts in the mammalian brain. Cell 174, 350-362.e17. https://doi.org/10.1016/j.cell.2018.05.022
  68. Konno, M., Koseki, J., Asai, A., Yamagata, A., Shimamura, T., Motooka, D., Okuzaki, D., Kawamoto, K., Mizushima, T., Eguchi, H., et al. (2019). Distinct methylation levels of mature microRNAs in gastrointestinal cancers. Nat. Commun. 10, 3888.
  69. Krek, A., Grun, D., Poy, M.N., Wolf, R., Rosenberg, L., Epstein, E.J., MacMenamin, P., da Piedade, I., Gunsalus, K.C., Stoffel, M., et al. (2005). Combinatorial microRNA target predictions. Nat. Genet. 37, 495-500. https://doi.org/10.1038/ng1536
  70. Krichevsky, A.M., Sonntag, K.C., Isacson, O., and Kosik, K.S. (2006). Specific microRNAs modulate embryonic stem cell-derived neurogenesis. Stem Cells 24, 857-864. https://doi.org/10.1634/stemcells.2005-0441
  71. Kruger, J. and Rehmsmeier, M. (2006). RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res. 34(Web Server issue), W451-W454. https://doi.org/10.1093/nar/gkl243
  72. Kume, H., Hino, K., Galipon, J., and Ui-Tei, K. (2014). A-to-I editing in the miRNA seed region regulates target mRNA selection and silencing efficiency. Nucleic Acids Res. 42, 10050-10060. https://doi.org/10.1093/nar/gku662
  73. Lambert, N., Robertson, A., Jangi, M., McGeary, S., Sharp, P.A., and Burge, C.B. (2014). RNA Bind-n-Seq: quantitative assessment of the sequence and structural binding specificity of RNA binding proteins. Mol. Cell 54, 887-900. https://doi.org/10.1016/j.molcel.2014.04.016
  74. Lee, S., Song, J., Kim, S., Kim, J., Hong, Y., Kim, Y., Kim, D., Baek, D., and Ahn, K. (2013). Selective degradation of host MicroRNAs by an intergenic HCMV noncoding RNA accelerates virus production. Cell Host Microbe 13, 678-690. https://doi.org/10.1016/j.chom.2013.05.007
  75. Legendre, M., Ritchie, W., Lopez, F., and Gautheret, D. (2006). Differential repression of alternative transcripts: a screen for miRNA targets. PLoS Comput. Biol. 2, e43.
  76. Leger, A., Amaral, P.P., Pandolfini, L., Capitanchik, C., Capraro, F., Miano, V., Migliori, V., Toolan-Kerr, P., Sideri, T., Enright, A.J., et al. (2021). RNA modifications detection by comparative Nanopore direct RNA sequencing. Nat. Commun. 12, 7198.
  77. Lewis, B.P., Burge, C.B., and Bartel, D.P. (2005). Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15-20. https://doi.org/10.1016/j.cell.2004.12.035
  78. Li, Q., Song, X.W., Zou, J., Wang, G.K., Kremneva, E., Li, X.Q., Zhu, N., Lappalainen, P., Yuan, W.J., Qin, Y.W., et al. (2010). Attenuation of microRNA-1 derepresses the cytoskeleton regulatory protein twinfilin-1 to provoke cardiac hypertrophy (vol 123, pg 2444, 2010). J. Cell Sci. 123, 2680.
  79. Liao, S., Sun, H., and Xu, C. (2018). YTH domain: a family of N6- methyladenosine (m6A) readers. Genomics Proteomics Bioinformatics 16, 99-107. https://doi.org/10.1016/j.gpb.2018.04.002
  80. Libri, V., Helwak, A., Miesen, P., Santhakumar, D., Borger, J.G., Kudla, G., Grey, F., Tollervey, D., and Buck, A.H. (2012). Murine cytomegalovirus encodes a miR-27 inhibitor disguised as a target. Proc. Natl. Acad. Sci. U. S. A. 109, 279-284. https://doi.org/10.1073/pnas.1114204109
  81. Lim, L.P., Lau, N.C., Garrett-Engele, P., Grimson, A., Schelter, J.M., Castle, J., Bartel, D.P., Linsley, P.S., and Johnson, J.M. (2005). Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433, 769-773. https://doi.org/10.1038/nature03315
  82. Lin, X., Yang, B., Liu, W., Tan, X., Wu, F., Hu, P., Jiang, T., Bao, Z., Yuan, J., Qiang, B., et al. (2016). Interplay between PCBP2 and miRNA modulates ARHGDIA expression and function in glioma migration and invasion. Oncotarget 7, 19483-19498. https://doi.org/10.18632/oncotarget.6869
  83. Liu, J., Yue, Y., Han, D., Wang, X., Fu, Y., Zhang, L., Jia, G., Yu, M., Lu, Z., Deng, X., et al. (2014). A METTL3-METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation. Nat. Chem. Biol. 10, 93-95. https://doi.org/10.1038/nchembio.1432
  84. Loughrey, D., Watters, K.E., Settle, A.H., and Lucks, J.B. (2014). SHAPE-Seq 2.0: systematic optimization and extension of high-throughput chemical probing of RNA secondary structure with next generation sequencing. Nucleic Acids Res. 42, e165.
  85. Luciano, D.J., Mirsky, H., Vendetti, N.J., and Maas, S. (2004). RNA editing of a miRNA precursor. RNA 10, 1174-1177. https://doi.org/10.1261/rna.7350304
  86. Lucks, J.B., Mortimer, S.A., Trapnell, C., Luo, S., Aviran, S., Schroth, G.P., Pachter, L., Doudna, J.A., and Arkin, A.P. (2011). Multiplexed RNA structure characterization with selective 2'-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). Proc. Natl. Acad. Sci. U. S. A. 108, 11063-11068. https://doi.org/10.1073/pnas.1106501108
  87. Majoros, W.H. and Ohler, U. (2007). Spatial preferences of microRNA targets in 3' untranslated regions. BMC Genomics 8, 152.
  88. McGeary, S.E., Bisaria, N., Pham, T.M., Wang, P.Y., and Bartel, D.P. (2022). MicroRNA 3'-compensatory pairing occurs through two binding modes, with affinity shaped by nucleotide identity and position. Elife 11, e69803.
  89. McGeary, S.E., Lin, K.S., Shi, C.Y., Pham, T.M., Bisaria, N., Kelley, G.M., and Bartel, D.P. (2019). The biochemical basis of microRNA targeting efficacy. Science 366, eaav1741.
  90. Mehta, A. and Baltimore, D. (2016). MicroRNAs as regulatory elements in immune system logic. Nat. Rev. Immunol. 16, 279-294. https://doi.org/10.1038/nri.2016.40
  91. Memczak, S., Jens, M., Elefsinioti, A., Torti, F., Krueger, J., Rybak, A., Maier, L., Mackowiak, S.D., Gregersen, L.H., Munschauer, M., et al. (2013). Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 495, 333-338. https://doi.org/10.1038/nature11928
  92. Michaels, M.L., Cruz, C., Grollman, A.P., and Miller, J.H. (1992). Evidence that MutY and MutM combine to prevent mutations by an oxidatively damaged form of guanine in DNA. Proc. Natl. Acad. Sci. U. S. A. 89, 7022-7025. https://doi.org/10.1073/pnas.89.15.7022
  93. Min, S., Lee, B., and Yoon, S. (2022). TargetNet: functional microRNA target prediction with deep neural networks. Bioinformatics 38, 671-677. https://doi.org/10.1093/bioinformatics/btab733
  94. Muljo, S.A., Ansel, K.M., Kanellopoulou, C., Livingston, D.M., Rao, A., and Rajewsky, K. (2005). Aberrant T cell differentiation in the absence of Dicer. J. Exp. Med. 202, 261-269. https://doi.org/10.1084/jem.20050678
  95. Muller, S., Glass, M., Singh, A.K., Haase, J., Bley, N., Fuchs, T., Lederer, M., Dahl, A., Huang, H., Chen, J., et al. (2019). IGF2BP1 promotes SRFdependent transcription in cancer in a m6A- and miRNA-dependent manner. Nucleic Acids Res. 47, 375-390. https://doi.org/10.1093/nar/gky1012
  96. Nachtigall, P.G. and Bovolenta, L.A. (2022). Computational detection of microRNA targets. Methods Mol. Biol. 2257, 187-209. https://doi.org/10.1007/978-1-0716-1170-8_10
  97. Nam, J.W., Rissland, O.S., Koppstein, D., Abreu-Goodger, C., Jan, C.H., Agarwal, V., Yildirim, M.A., Rodriguez, A., and Bartel, D.P. (2014). Global analyses of the effect of different cellular contexts on microRNA targeting. Mol. Cell 53, 1031-1043. https://doi.org/10.1016/j.molcel.2014.02.013
  98. Nielsen, C.B., Shomron, N., Sandberg, R., Hornstein, E., Kitzman, J., and Burge, C.B. (2007). Determinants of targeting by endogenous and exogenous microRNAs and siRNAs. RNA 13, 1894-1910. https://doi.org/10.1261/rna.768207
  99. Nishikura, K. (2016). A-to-I editing of coding and non-coding RNAs by ADARs. Nat. Rev. Mol. Cell Biol. 17, 83-96. https://doi.org/10.1038/nrm.2015.4
  100. O'Connell, R.M., Rao, D.S., Chaudhuri, A.A., and Baltimore, D. (2010). Physiological and pathological roles for microRNAs in the immune system. Nat. Rev. Immunol. 10, 111-122. https://doi.org/10.1038/nri2708
  101. O'Donnell, K.A., Wentzel, E.A., Zeller, K.I., Dang, C.V., and Mendell, J.T. (2005). c-Myc-regulated microRNAs modulate E2F1 expression. Nature 435, 839-843. https://doi.org/10.1038/nature03677
  102. Orom, U.A. and Lund, A.H. (2010). Experimental identification of microRNA targets. Gene 451, 1-5. https://doi.org/10.1016/j.gene.2009.11.008
  103. Park, J., Seo, J.W., Ahn, N., Park, S., Hwang, J., and Nam, J.W. (2019). UPF1/SMG7-dependent microRNA-mediated gene regulation. Nat. Commun. 10, 4181.
  104. Peng, Y. and Croce, C.M. (2016). The role of MicroRNAs in human cancer. Signal Transduct. Target. Ther. 1, 15004.
  105. Pettersen, E.F., Goddard, T.D., Huang, C.C., Meng, E.C., Couch, G.S., Croll, T.I., Morris, J.H., and Ferrin, T.E. (2021). UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70-82. https://doi.org/10.1002/pro.3943
  106. Plaisier, C.L., Pan, M., and Baliga, N.S. (2012). A miRNA-regulatory network explains how dysregulated miRNAs perturb oncogenic processes across diverse cancers. Genome Res. 22, 2302-2314. https://doi.org/10.1101/gr.133991.111
  107. Poliseno, L., Salmena, L., Zhang, J., Carver, B., Haveman, W.J., and Pandolfi, P.P. (2010). A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature 465, 1033-1038. https://doi.org/10.1038/nature09144
  108. Pollard, K.S., Hubisz, M.J., Rosenbloom, K.R., and Siepel, A. (2010). Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110-121. https://doi.org/10.1101/gr.097857.109
  109. Pu, M., Chen, J., Tao, Z., Miao, L., Qi, X., Wang, Y., and Ren, J. (2019). Regulatory network of miRNA on its target: coordination between transcriptional and post-transcriptional regulation of gene expression. Cell. Mol. Life Sci. 76, 441-451. https://doi.org/10.1007/s00018-018-2940-7
  110. Qian, B., Wang, P., Zhang, D., and Wu, L. (2021). m6A modification promotes miR-133a repression during cardiac development and hypertrophy via IGF2BP2. Cell Death Discov. 7, 157.
  111. Quang, D. and Xie, X. (2016). DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res. 44, e107.
  112. Reczko, M., Maragkakis, M., Alexiou, P., Grosse, I., and Hatzigeorgiou,  A.G. (2012). Functional microRNA targets in protein coding sequences. Bioinformatics 28, 771-776. https://doi.org/10.1093/bioinformatics/bts043
  113. Rodriguez, A., Vigorito, E., Clare, S., Warren, M.V., Couttet, P., Soond, D.R., van Dongen, S., Grocock, R.J., Das, P.P., Miska, E.A., et al. (2007). Requirement of bic/microRNA-155 for normal immune function. Science 316, 608-611. https://doi.org/10.1126/science.1139253
  114. Roundtree, I.A., Evans, M.E., Pan, T., and He, C. (2017). Dynamic RNA modifications in gene expression regulation. Cell 169, 1187-1200. https://doi.org/10.1016/j.cell.2017.05.045
  115. Rouskin, S., Zubradt, M., Washietl, S., Kellis, M., and Weissman, J.S. (2014). Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo. Nature 505, 701-705. https://doi.org/10.1038/nature12894
  116. Saetrom, P., Heale, B.S., Snove, O., Jr., Aagaard, L., Alluin, J., and Rossi, J.J. (2007). Distance constraints between microRNA target sites dictate efficacy and cooperativity. Nucleic Acids Res. 35, 2333-2342. https://doi.org/10.1093/nar/gkm133
  117. Salmena, L., Poliseno, L., Tay, Y., Kats, L., and Pandolfi, P.P. (2011). A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell 146, 353-358. https://doi.org/10.1016/j.cell.2011.07.014
  118. Sandberg, R., Neilson, J.R., Sarma, A., Sharp, P.A., and Burge, C.B. (2008). Proliferating cells express mRNAs with shortened 3' untranslated regions and fewer microRNA target sites. Science 320, 1643-1647. https://doi.org/10.1126/science.1155390
  119. Schirle, N.T., Sheu-Gruttadauria, J., and MacRae, I.J. (2014). Structural basis for microRNA targeting. Science 346, 608-613. https://doi.org/10.1126/science.1258040
  120. Selbach, M., Schwanhausser, B., Thierfelder, N., Fang, Z., Khanin, R., and Rajewsky, N. (2008). Widespread changes in protein synthesis induced by microRNAs. Nature 455, 58-63. https://doi.org/10.1038/nature07228
  121. Seok, H., Lee, H., Lee, S., Ahn, S.H., Lee, H.S., Kim, G.D., Peak, J., Park, J., Cho, Y.K., Jeong, Y., et al. (2020). Position-specific oxidation of miR-1 encodes cardiac hypertrophy. Nature 584, 279-285. https://doi.org/10.1038/s41586-020-2586-0
  122. Shi, C.Y., Kingston, E.R., Kleaveland, B., Lin, D.H., Stubna, M.W., and Bartel, D.P. (2020). The ZSWIM8 ubiquitin ligase mediates target-directed microRNA degradation. Science 370, eabc9359.
  123. Squires, J.E., Patel, H.R., Nousch, M., Sibbritt, T., Humphreys, D.T., Parker, B.J., Suter, C.M., and Preiss, T. (2012). Widespread occurrence of 5-methylcytosine in human coding and non-coding RNA. Nucleic Acids Res. 40, 5023-5033. https://doi.org/10.1093/nar/gks144
  124. Stark, A., Brennecke, J., Bushati, N., Russell, R.B., and Cohen, S.M. (2005). Animal MicroRNAs confer robustness to gene expression and have a significant impact on 3'UTR evolution. Cell 123, 1133-1146. https://doi.org/10.1016/j.cell.2005.11.023
  125. Talukder, A., Zhang, W., Li, X., and Hu, H. (2022). A deep learning method for miRNA/isomiR target detection. Sci. Rep. 12, 10618.
  126. Tan, C.L., Plotkin, J.L., Veno, M.T., von Schimmelmann, M., Feinberg, P., Mann, S., Handler, A., Kjems, J., Surmeier, D.J., O'Carroll, D., et al. (2013). MicroRNA-128 governs neuronal excitability and motor behavior in mice. Science 342, 1254-1258. https://doi.org/10.1126/science.1244193
  127. Tasdelen, A. and Sen, B. (2021). A hybrid CNN-LSTM model for premiRNA classification. Sci. Rep. 11, 14125.
  128. Thadani, R. and Tammi, M.T. (2006). MicroTar: predicting microRNA targets from RNA duplexes. BMC Bioinformatics 7 Suppl 5, S20.
  129. Thai, T.H., Calado, D.P., Casola, S., Ansel, K.M., Xiao, C., Xue, Y., Murphy, A., Frendewey, D., Valenzuela, D., Kutok, J.L., et al. (2007). Regulation of the germinal center response by microRNA-155. Science 316, 604-608. https://doi.org/10.1126/science.1141229
  130. Thomson, D.W., Bracken, C.P., and Goodall, G.J. (2011). Experimental strategies for microRNA target identification. Nucleic Acids Res. 39, 6845-6853. https://doi.org/10.1093/nar/gkr330
  131. Thomson, D.W. and Dinger, M.E. (2016). Endogenous microRNA sponges: evidence and controversy. Nat. Rev. Genet. 17, 272-283. https://doi.org/10.1038/nrg.2016.20
  132. Tian, B., Hu, J., Zhang, H., and Lutz, C.S. (2005). A large-scale analysis of mRNA polyadenylation of human and mouse genes. Nucleic Acids Res. 33, 201-212. https://doi.org/10.1093/nar/gki158
  133. Townshend, R.J.L., Eismann, S., Watkins, A.M., Rangan, R., Karelina, M., Das, R., and Dror, R.O. (2021). Geometric deep learning of RNA structure. Science 373, 1047-1051. https://doi.org/10.1126/science.abe5650
  134. Tuschl, T., Zamore, P.D., Lehmann, R., Bartel, D.P., and Sharp, P.A. (1999). Targeted mRNA degradation by double-stranded RNA in vitro. Genes Dev. 13, 3191-3197. https://doi.org/10.1101/gad.13.24.3191
  135. Ulitsky, I., Shkumatava, A., Jan, C.H., Sive, H., and Bartel, D.P. (2011). Conserved function of lincRNAs in vertebrate embryonic development despite rapid sequence evolution. Cell 147, 1537-1550. https://doi.org/10.1016/j.cell.2011.11.055
  136. Van Nostrand, E.L., Pratt, G.A., Shishkin, A.A., Gelboin-Burkhart, C., Fang, M.Y., Sundararaman, B., Blue, S.M., Nguyen, T.B., Surka, C., Elkins, K., et al. (2016). Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods 13, 508-514. https://doi.org/10.1038/nmeth.3810
  137. Van Nostrand, E.L., Pratt, G.A., Yee, B.A., Wheeler, E.C., Blue, S.M., Mueller, J., Park, S.S., Garcia, K.E., Gelboin-Burkhart, C., Nguyen, T.B., et al. (2020). Principles of RNA processing from analysis of enhanced CLIP maps for 150 RNA binding proteins. Genome Biol. 21, 90.
  138. Vasudevan, S. and Steitz, J.A. (2007). AU-rich-element-mediated upregulation of translation by FXR1 and Argonaute 2. Cell 128, 1105-1118. https://doi.org/10.1016/j.cell.2007.01.038
  139. Wagner, R.W., Smith, J.E., Cooperman, B.S., and Nishikura, K. (1989). A double-stranded RNA unwinding activity introduces structural alterations by means of adenosine to inosine conversions in mammalian cells and Xenopus eggs. Proc. Natl. Acad. Sci. U. S. A. 86, 2647-2651. https://doi.org/10.1073/pnas.86.8.2647
  140. Wang, D., Zhang, Z., Jiang, Y., Mao, Z., Wang, D., Lin, H., and Xu, D. (2021). DM3Loc: multi-label mRNA subcellular localization prediction and analysis based on multi-head self-attention mechanism. Nucleic Acids Res. 49, e46.
  141. Wang, J.X., Gao, J., Ding, S.L., Wang, K., Jiao, J.Q., Wang, Y., Sun, T., Zhou, L.Y., Long, B., Zhang, X.J., et al. (2015). Oxidative modification of miR-184 enables it to target Bcl-xL and Bcl-w. Mol. Cell 59, 50-61. https://doi.org/10.1016/j.molcel.2015.05.003
  142. Wang, Q., Hui, H., Guo, Z., Zhang, W., Hu, Y., He, T., Tai, Y., Peng, P., and Wang, L. (2013). ADAR1 regulates ARHGAP26 gene expression through RNA editing by disrupting miR-30b-3p and miR-573 binding. RNA 19, 1525-1536. https://doi.org/10.1261/rna.041533.113
  143. Wen, J., Parker, B.J., Jacobsen, A., and Krogh, A. (2011). MicroRNA transfection and AGO-bound CLIP-seq data sets reveal distinct determinants of miRNA action. RNA 17, 820-834. https://doi.org/10.1261/rna.2387911
  144. Wienholds, E., Kloosterman, W.P., Miska, E., Alvarez-Saavedra, E., Berezikov, E., de Bruijn, E., Horvitz, H.R., Kauppinen, S., and Plasterk, R.H. (2005). MicroRNA expression in zebrafish embryonic development. Science 309, 310-311. https://doi.org/10.1126/science.1114519
  145. Wu, J., Bao, J., Kim, M., Yuan, S., Tang, C., Zheng, H., Mastick, G.S., Xu, C., and Yan, W. (2014). Two miRNA clusters, miR-34b/c and miR-449, are essential for normal brain development, motile ciliogenesis, and spermatogenesis. Proc. Natl. Acad. Sci. U. S. A. 111, E2851-E2857. https://doi.org/10.1073/pnas.1407777111
  146. Xin, M., Small, E.M., Sutherland, L.B., Qi, X., McAnally, J., Plato, C.F., Richardson, J.A., Bassel-Duby, R., and Olson, E.N. (2009). MicroRNAs miR143 and miR-145 modulate cytoskeletal dynamics and responsiveness of smooth muscle cells to injury. Genes Dev. 23, 2166-2178. https://doi.org/10.1101/gad.1842409
  147. Xue, Y., Ouyang, K., Huang, J., Zhou, Y., Ouyang, H., Li, H., Wang, G., Wu, Q., Wei, C., Bi, Y., et al. (2013). Direct conversion of fibroblasts to neurons by reprogramming PTB-regulated microRNA circuits. Cell 152, 82-96. https://doi.org/10.1016/j.cell.2012.11.045
  148. Yang, W., Chendrimada, T.P., Wang, Q., Higuchi, M., Seeburg, P.H., Shiekhattar, R., and Nishikura, K. (2006). Modulation of microRNA processing and expression through RNA editing by ADAR deaminases. Nat. Struct. Mol. Biol. 13, 13-21. https://doi.org/10.1038/nsmb1041
  149. Zeng, H., Edwards, M.D., Liu, G., and Gifford, D.K. (2016). Convolutional neural network architectures for predicting DNA-protein binding. Bioinformatics 32, i121-i127. https://doi.org/10.1093/bioinformatics/btw255