Kantitatif RT-PCR (RT-qPCR) ile MikroRNA (miRNA) Ekspresyon Profillemesi

Abstract

Gen ekspresyonunun post-transkripsiyonel düzenleyicisi olarak bilinen miRNA’lar ökaryotik canlılarda çeşitli fizyolojik ve patolojik süreçlerde rol oynamaktadır. miRNA'ların rollerinin ortaya konulması ile birlikte miRNA’lar üzerine yapılan çalışmaların sayısı da gün geçtikçe artmaktadır. Yapılan çalışmalar ile birlikte miRNA'ların bütün hücre ve doku tiplerinde eksprese olduğu ortaya konmuştur. miRNA ekspresyon profilinin ortaya konması için çeşitli yöntemler öneril-mesine rağmen, yüksek duyarlılığı ve özgüllüğü nedeniyle RT-qPCR altın standart olarak kabul edilmektedir. Bu derle-mede; RT-qPCR ile miRNA ekspresyon profillemesi sürecindeki adımlar detaylı olarak özetlenmiş ve konu ile ilgili lite-ratür bilgisine yer verilmiştir.

Keywords

• Ekspresyon
• miRNA
• normalizasyon
• RT-qPCR

MicroRNA (miRNA) Expression Profiling by Quantitative RT-PCR (RT-qPCR)

Abstract

miRNAs, known as post-transcriptional regulators of gene expression, play a crucial role in various physio-logical and pathological processes in eukaryotic organisms. As the roles of miRNAs have been revealed, the number of studies on miRNAs is increasing day by day. Studies have shown that miRNAs are expressed in all cell and tissue types. Although various methods have been proposed to demonstrate miRNA expression profile, RT-qPCR is consid-ered as a gold standard technique due to its high sensitivity and specificity. In this review; the steps in the miRNA ex-pression profiling process by RT-qPCR were summarized in detail and relevant literature knowledge was given.

Keywords

• Expression
• miRNA
• normalization
• RT-qPCR

References

1. Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. Elife 2015; 12: 4.
2. Andersen CL, Jensen JL, Orntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normaliza-tion, applied to bladder and colon cancer data sets. Cancer Res 2004; 64: 5245-50.
3. Androvic P, Valihrach L, Elling J, Sjoback R, Kubista M. Two-tailed RT-qPCR: a novel method for highly accurate miRNA quantification. Nucleic Acids Res 2017; 45: 144.
4. Arroyo JD, Chevillet JR, Kroh EM, Ruf IK, Pritchard CC, Gibson DF, Mitchell PS, Bennett CF, Pogo-sova-Agadjanyan EL, Stirewalt DL, Tait JF, Tewari M. Argonaute2 complexes carry a popu-lation of circulating microRNAs independent of vesicles in human plasma. Proc Natl Acad Sci USA 2011; 108(12): 5003-08.
5. Backes C, Haas J, Leidinger P, Frese K, Grossmann T, Ruprecht K, Meder B, Meese E, Keller A. miF-Rame: analysis and visualization of miRNA sequencing data in neurological disorders. J Transl Med 2015; 13: 224.
6. Becker C, Hammerle-Fickinger A, Riedmaier I, Pfaffl MW. mRNA and microRNA quality control for RT-qPCR analysis. Methods 2010; 50: 237-43.
7. Benes V, Castoldi M. Expression profiling of microR-NA using real-time quantitative PCR, how to use it and what is available. Methods 2010; 50: 244-9.
8. Blondal T, Jensby Nielsen S, Baker A, Andreasen D, Mouritzen P, Wrang Teilum M, Dahlsveen IK Assessing sample and miRNA profile quality in serum and plasma or other biofluids. Methods 2013; 59: 1-6.

9. Bollati V, Dioni L. Methods for Analyzing miRNA Expression. Academic Press 2019; 379-405.
10. Bravo V, Rosero S, Ricordi C, Pastori RL. Instability of miRNA and cDNAs derivatives in RNA prepa-rations. Biochem Biophys Res Commun 2007; 353: 1052-5.
11. Castoldi M, Spasia MV, Altamura S, Elman J, Lindow M, Kiss J, Stolte J, Sparla R, D’Alessandro LA, Klingmullers U, Fleming RE, Longerich T, Gro-nes HJ, Benes V, Kauppinen S, Hentze MW, Muckenthaler MU. The liver-specific microRNA miR-122 controls systematic iron homeostasis in mice. JIC 2011; 121: 1386-96.
12. Chakraborty M, Chatterjee A, Krithika S, Vasulu TS. A Statistical Analysis of MicroRNA: Classifica-tion, Identification and Conservation Based on Structure and Function. Dasgupta R. ed. In: Growth Curve and Structural Equation Modeling. Switzerland: Springer Proceedings in Mathema-tics & Statistics, 2015; pp. 223-58.
13. Cheng HH, Yi HS, Kim Y, Kroh EM, Chien JW, Eaton KD, Goodman MT, Trait JF, Teewari, Pritchard CC. Plasma processing conditions substantially influence circulating microRNA biomarker levels. PloS ONE 2013; 8(6): 64795.
14. Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, Huang WC, Sun TH, Tu SJ, Lee WH, Chiew MY, Tai CS, Wei TY, Tsai TR, Huang HT, Wang CY, Wu HY, Ho SY, Chen PR, Chuang CH, Hsieh PJ, Wu YS, Chen WL, Li MJ, Wu YC, Huang XY, Ng FL, Buddhakosai W, Huang PC, Lan KC, Huang CY, Weng SL, Cheng YN, Liang C, Hsu WL, Huang HD. miRTarBase update 2018: A resource for experimentally validated microRNA-target interactions. Nucleic Acids Res 2018; 46: 296-302.
15. Dellett M, Simpson DA. Considerations for optimiza-tion of microRNA PCR assays for molecular di-agnosis. Expert Rev Mol Diagnost 2016; 16:407-14.
16. Donadeux FX, Schauer SN. Differential miRNA expression between equine ovulatory and ano-vulatory follicles. Domest Anim Endocrinol 2013, 45: 122-5.
17. Faraldi M, Gomarasca M, Sansoni V, Perego S, Banfi G, Lombardi G. Normalization strategies diffe-rently affect circulating miRNA profile associated with the training status. Nature 2019; 9: 1584.
18. Fleischhacker SN, Bauersachs S, Wehner A, Hart-mann K, Weber K. Differential expression of cir-culating microRNAs in diabetic and healthy lean cats. Vet J 2013; 197: 688-93.
19. Grasedieck S, Schöler N, Bommer M, Niess JH, Tu-mani H, Roughi A, Bloehdorn J, Liebisch P, Mertens D, Döhner H, Buske C, Langer C, Kuchen-bauer F. Impact of serum storage conditions on microRNA stability. Leukemia 2012; 26: 2414-6.
20. Guo L, Chen F. A challenge for miRNA: multiple isomiRs in miRNAomics. Gene 2014; 544: 1-7.
21. Guo X, Zheng Y. Expression profiling of circulating miRNAs in mouse serum in response to Echino-coccus multilocularis infection. Parasitology 2017; 144: 1079–87.
22. Huang T, Yang J, Liu G, Jin W, Liu Z, Zhao S, Yao M. Quantification of mature microRNAs using pincer probes and real-time PCR amplification. PLoS ONE 2015; 10: 0120160.
23. Jin J, Vaud S, Zhelkovsky AM, Posfai J, Mcreynolds LA. Sensitive and specific miRNA detection met-hod using SplintR ligase. Nucleic Acids Res 2016; 44: 116.
24. Kang ST, Hsies YS, Feng CT, Chen YT, Yang PE, Chen WM. miPrimer: An empirical-based qPCR primer design method for small noncoding mic-roRNA. RNA 2018; 24: 304-12.
25. Kirschner MB, Kirschner MB, Edelman JJB, Kao ACH, Valley MP, Zandwijk NV, Reid G. The im-pact of hemolysis on cell-free microRNA biomar-kers. Front Genet 2013; 4: 94.
26. Kozomara A, Birgaoanu M, Jones-Griffiths S. miRBa-se: from microRNA sequences to function. Nuc-leic Asids Res 2019; 47: 155-62.
27. Li Z, Liu H, Jin X, Lo L, Liu J. Expression profiles of microRNAs from lactating and non-lactating bovi-ne mammary glands and identification of miRNA related to lactation. BMC Genomics 2012; 13: 731.
28. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-delta delta c(t)) method. Methods 2001; 25: 402-8.
29. Mendell JT, Olson EN. MicroRNAs in stress signaling and human disease. Cell 2012; 148: 1172-87.
30. Mraz M, Malinova K, Mayer J, Pospisilova S. MicroR-NA isolation and stability in stored RNA samples. Biochem Biophys Res Commun 2009; 390: 1-4.
31. Nolan T, Hands RE, Bustin SA. Quantification of mRNA using real-time RT-PCR. Nat Protoc 2006; 1: 1559-82.
32. Nolan T, Huggett J, Sanchez E. Good practice guide for the application of quantitative PCR (qPCR), LGC 2013; 1-99.
33. Schwarzenbach H, Da Silva AM, Calin G, Pantel K. Data normalization strategies for microRNA qu-antification. Clin Chem 2015; 61: 1333-42.
34. Sidekli Ö, Korkmaz Ağaoğlu Ö. Gebelik Sürecinde Rol Oynayan mikroRNA (miRNA)’lar. Lalahan Hay Araşt Enst Derg 2019; 59(1): 36-48.
35. Singh B, Mal G, Gautam S, mukesh M. Big from small: MicroRNA in Relation to veterinary scien-ces. Advances in Animal Biotechnology 2019; 447-53.
36. Stenfeldt C, Arzt J, Smoliga G, LaRocco M, Gutkoska J, Lawrence P. Proof-of- concept study: profile of circulating microRNAs in bovine serum harves-ted during acute and persistent FMDV infection. Virol J 2017; 14: 71.
37. Su L, Zhao S, Zhu M, Yu M. Differential expression of microRNAs in porcine placentas on Days 30 and 90 of gestation. Reprod Fertil Dev 2010; 22(8): 1175-82.
38. Szafranska AE, Davison TS, Shingara J, Doleshal M, Riggenbach JA, Morrison CD, Jewell S, Labou-rier E. Accurate molecular characterization of formalin-fixed, paraffin-embedded tissues by microRNA expression profiling. J Mol Dliagn 2008; 10: 415-23.
39. Tellinghuisen J, Spiess AN. Comparing real-time quantitative polymerase chain reaction analysis methods for precision, linearity, and accuracy of estimating amplification efficiency. Anal Biochem 2016; 449: 76-82.
40. Tsang J, Ebert MS, Van Oudenaarden A. Genome-wide dissection of microRNA functions and co-targeting networks using gene set signatures. Mol Cell 2010; 38: 140-53.
41. Uhl WE, Suter S, Krimer P, Schliekelman P, Tomp-kins SM, Suter S. Identification of altered Mic-roRNA expression in canine lymphoid cell lines and cases of B- and T-Cell lymphomas. Genes Chromosomes Cancer 2011; 950-67.
42. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 2002; 3(7).
43. Weber JA, Baxter DH, Zhang S, Huang KH, Lee MJ, Galas DJ, Wang K. The microRNA spectrum in 12 body fluids. Clin Chem 2010; 56: 1733-41.
44. Xie S, Zhu Q, Qu W, Xu Z, Liu X, Li X, Li S, Ma W, Miao Y, Zhang L, Du X, Dong W, Li H, Zhao C, Wang Y, Fang Y, Zhao S. sRNAPrimerDB: Comprehensive primer design and search web service for small non-coding RNAs. Bioinformatics 2019; 35: 1566-72.
45. Zheng CY, Zou X, Lin HJ, Zhou BC, Zhang ML, Luo CH, Fu SX. miRNA-185 regulates the VEGFA signaling pathway in dairy cows with retained fetal membranes. Therio 2018; 110: 116-21.