Present-Day Computer-Aided Primer Designing Tools for Non-Coding RNA

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Resumo

MicroRNA (miRNA) is a class of non-coding RNA that play the pivotal role in post-transcriptional regulation of expression of genes involved in the control of fundamental cellular processes. Their high diagnostic yield and predictive value in various human diseases predetermined the need for highly specific design of primers for qualitative analysis of the expression of a variety of microRNA. Designing the primers for qualitative assessment of microRNA expression is a challenge in terms of methodology due to short matrix and high homology between the family members. Quite often conventional tools, initially aimed at longer targets, are not efficient enough when working with shorter sequences of mature microRNA. That is why, specialized platforms have been created that are adapted to unique structural and functional properties of microRNA. These platforms ensure exact design of primers that can be reproduced. Current review considers present-day computer-aided software tools specially developed to design primers for short non-coding RNA with emphasis on their functional characteristics and ability to design primers for most commonly used method of qualitative assessment of microRNA expression – Stem-loop RT-PCR.

Sobre autores

M. Yanishevskaya

Southern Urals Federal Research and Clinical Center for Medical Biophysics of the FMBA; Chelyabinsk State University

Email: yanishevskaya@urcrm.ru
Chelyabinsk, Russia; Chelyabinsk, Russia

E. Blinova

Southern Urals Federal Research and Clinical Center for Medical Biophysics of the FMBA; Chelyabinsk State University

Chelyabinsk, Russia; Chelyabinsk, Russia

Bibliografia

  1. Lee R.C., Feinbaum R.L., Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14 // Cell. 1993. V. 75. № 5. P. 843–854. https://doi.org/10.1016/0092-8674(93)90529-y
  2. Ha M., Kim V.N. Regulation of microRNA biogenesis // Nat. Rev. Mol. Cell Biol. 2014. V. 15. № 8. P. 509–524. https://doi.org/10.1038/nrm3838
  3. Friedman R.C., Farh K.K.H., Burge C.B., Bartel D.P. Most mammalian mRNAs are conserved targets of microRNAs // Genome Res. 2009. V. 19. № 1. P. 92–105. https://doi.org/10.1101/gr.082701.108
  4. Jang J.H., Lee T.J. The role of microRNAs in cell death pathways // Yeungnam Univ. J. Med. 2021. V. 38. № 2. P. 107–117. https://doi.org/10.12701/yujm.2020.00836
  5. Avraham R., Yarden Y. Regulation of signalling by microRNAs // Biochem. Soc. Trans. 2012. V. 40. № 1. P. 26–30. https://doi.org/10.1042/BST20110623
  6. Khameneh S.C., Razi S., Lashanizadegan R. et al. MicroRNA-mediated metabolic regulation of immune cells in cancer: An updated review // Front Immunol. 2024. V. 15. https://doi.org/10.3389/fimmu.2024.1424909
  7. Kunze-Schumacher H., Krueger A. The role of microRNAs in development and function of regulatory T cells – lessons for a better understanding of microRNA biology // Front. Immunol. 2020. V. 11. P. 2185. https://doi.org/10.3389/fimmu.2020.02185
  8. Bao N., Lye K.W., Barton M.K. MicroRNA binding sites in Arabidopsis class III HD-ZIP mRNAs are required for methylation of the template chromosome // Dev. Cell. 2004. V. 7. № 5. P. 653–662. https://doi.org/10.1016/j.devcel.2004.10.003
  9. Searles C.D. MicroRNAs and cardiovascular disease risk // Curr. Cardiol. Rep. 2024. V. 26. № 2. P. 51–60. https://doi.org/10.1007/s11886-023-02014-1
  10. Junn E., Mouradian M.M. MicroRNAs in neurodegenerative diseases and their therapeutic potential // Pharmacol. Ther. 2012. V. 133. № 2. P. 142–150. https://doi.org/10.1016/j.pharmthera.2011.10.002
  11. Chakrabortty A., Patton D.J., Smith B.F., Agarwal P. miRNAs: Potential as biomarkers and therapeutic targets for cancer // Genes (Basel). 2023. V. 14. № 7. P. 1375. https://doi.org/10.3390/genes14071375
  12. Untergasser A., Cutcutache I., Koressaar T. et al. Primer3-new capabilities and interfaces // Nucl. Acids Res. 2012. V. 40. № 15. P. e115. https://doi.org/10.1093/nar/gks596
  13. Ye J., Coulouris G., Zaretskaya I. et al. Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction // BMC Bioinformatics. 2012. V. 13. P. 134. https://doi.org/10.1186/1471-2105-13-134
  14. Li L.C. Designing PCR primer for DNA methylation mapping // Methods Mol. Biol. 2007. V. 402. P. 371–384. https://doi.org/10.1007/978-1-59745-528-2_19
  15. Singh A., Pandey G.K. Primer design using Primer Express® for SYBR Green-based quantitative PCR // Methods Mol. Biol. 2015. V. 1275. P. 153–164. https://doi.org/10.1007/978-1-4939-2365-6_11
  16. Lim L.P., Lau N.C., Weinstein E.G. et al. The microRNAs of Caenorhabditis elegans // Genes Dev. 2003. V. 17. P. 991–1008. https://doi.org/10.1101/gad.1074403
  17. Pena J.T., Sohn-Lee C., Rouhanifard S.H. et al. miRNA in situ hybridization in formaldehyde and EDC-fixed tissues // Nat. Methods. 2009. V. 6. P. 139–141. https://doi.org/10.1038/nmeth.1294
  18. Lagos-Quintana M., Rauhut R., Lendeckel W., Tuschl T. Identification of novel genes coding for small expressed RNAs // Science. 2001. V. 294. P. 853–858. https://doi.org/10.1126/science.1064921
  19. Chen J., Lozach J., Garcia E.W. et al. Highly sensitive and specific microRNA expression profiling using BeadArray technology // Nucl. Acids Res. 2008. V. 36. № 14. P. e87. https://doi.org/10.1093/nar/gkn387
  20. Krichevsky A.M., King K.S., Donahue C.P. et al. A microRNA array reveals extensive regulation of microRNAs during brain development // RNA. 2004. V. 9. № 10. P. 1274–1281. https://doi.org/10.1261/rna.5980303
  21. Balcells I., Cirera S., Busk P.K. Specific and sensitive quantitative RT-PCR of miRNAs with DNA primers // BMC Biotechnol. 2011. V. 11. P. 70. https://doi.org/10.1186/1472-6750-11-70
  22. Chen C., Ridzon D.A., Broomer A.J. et al. Real-time quantification of microRNAs by stem-loop RT-PCR // Nucl. Acids Res. 2005. V. 33. № 20. P. e179. https://doi.org/10.1093/nar/gni178
  23. Shi R., Chiang V.L. Facile means for quantifying microrRNA expression by real-time PCR // BioTechniques. 2005. V. 39. № 4. P. 519–525. https://doi.org/10.2144/000112010
  24. Kramer M.F. Stem-loop RT-qPCR for miRNAs // Curr. Protoc. Mol. Biol. 2011. Chapter 15: Unit15.10. https://doi.org/10.1002/0471142727.mb1510s95
  25. Yang L.H., Wang S.L., Tang L.L. et al. Universal stem-loop primer method for screening and quantification of microRNA // PLoS One. 2014. V. 9. № 12. https://doi.org/10.1371/journal.pone.0115293
  26. Wang Y., Zhou J., Chen Y. et al. Quantification of distinct let-7 microRNA family members by a modified stem-loop RT-qPCR // Mol. Med. Rep. 2018. V. 17. № 3. P. 3690–3696. https://doi.org/10.3892/mmr.2017.8297
  27. Varkonyi-Gasic E., Wu R., Wood M. et al. Protocol: A highly sensitive RT-PCR method for detection and quantification of microRNAs // Plant Methods. 2007. V. 3. P. 12. https://doi.org/10.1186/1746-4811-3-12
  28. Kang S.T., Hsieh Y.S., Feng C.T. et al. miPrimer: An empirical-based qPCR primer design method for small noncoding microRNA // RNA. 2018. V. 24. № 3. P. 304–312. https://doi.org/10.1261/rna.061150.117
  29. Guido N., Starostina E., Leake D., Saaem I. Improved PCR amplification of broad spectrum GC DNA templates // PLoS One. 2016. V. 11. № 6. https://doi.org/10.1371/journal.pone.0156478
  30. Rhim J., Baek W., Seo Y., Kim J.H. From molecular mechanisms to therapeutics: Understanding micro-RNA-21 in cancer // Cells. 2022. V. 11. № 18. P. 2791.https://doi.org/10.3390/cells11182791
  31. He F., Guan W. The role of miR-21 as a biomarker and therapeutic target in cardiovascular disease // Clin. Chim. Acta. 2025. https://doi.org/10.1016/j.cca.2025.120304
  32. Kumarswamy R., Volkmann I., Thum T. Regulation and function of miRNA-21 in health and disease // RNA Biol. 2011. V. 8. № 5. P. 706–713. https://doi.org/10.4161/rna.8.5.16154
  33. Busk P.K. A tool for design of primers for micro-RNA-specific quantitative RT-qPCR // BMC Bioinformatics. 2014. V. 15. P. 29. https://doi.org/10.1186/1471-2105-15-29
  34. Balcells I., Cirera S., Busk P.K. Specific and sensitive quantitative RT-PCR of miRNAs with DNA primers // BMC Biotechnol. 2011. V. 11. P. 70. https://doi.org/10.1186/1472-6750-11-70
  35. Vester B., Wengel J. LNA (locked nucleic acid): High-affinity targeting of complementary RNA and DNA // Biochemistry. 2004. V. 43. № 42. P. 13233–13241. https://doi.org/10.1021/bi0485732
  36. Xie S., Zhu Q., Qu W. et al. sRNAPrimerDB: Comprehensive primer design and search web service for small non-coding RNAs // Bioinformatics. 2019. V. 35. № 9. P. 1566–1572. https://doi.org/10.1093/bioinformatics/bty852
  37. UPL (2017). Universal Probe Library. https://lifescience.roche.com/en_in/brands/universal-probe-library.html.05/06/2017
  38. Czimmerer Z., Hulvely J., Simandi Z. et al. A Versatile method to design stem-loop primer-based quantitative PCR assays for detecting small regulatory RNA molecules // PLoS One. 2013. V. 8. № 1. P. e55168. https://doi.org/10.1371/journal.pone.0055168

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