Tek Basamaklı Ters Transkripsiyon Kantitatif PZR Yönteminin miRNA Ekspresyon Analizleri için Optimizasyonu

Year 2021, , 113 - 119, 25.08.2021

Seda Süsgün , İlker Karacan Emrah Yücesan

https://doi.org/10.26650/experimed.2021.948146

Abstract

Amaç: Bu çalışmada mikroRNA (miRNA) hedeflerinin spesifik ola-rak belirlenmesi ve ekspresyon ölçümünün yapılmasına yönelik tek basamaklı ters transkripsiyon kantitatif polimeraz zincir reaksiyonu (RT-qPZR) yönteminin, seçilen iki farklı miRNA (hsa-miR-145-5p ve hsa-miR-146a-5p) için araştırılması ve sürecin optimizasyonu amaçlanmıştır.

Gereç ve Yöntem: RNA eldesi HEK293T hücre hattından yapılmıştır. Çalışmada seçilen her iki miRNA hedefi için uygun primerler tasarlanmış, tek basamaklı RT-qPZR yöntemi ile optimizasyon işlemi gerçekleştirilmiştir. Hedef amplikonların spesifiklik doğrulamaları agaroz jel elektroforezi ve konvansiyonel dizileme yöntemi ile gerçekleştirilmiştir.

Bulgular: Tek basamakta gerçekleştirilen RT-qPZR çalışmasının her iki primer için de yüksek spesifiklikte ve hassasiyette sonuç verdiği qPZR erime eğrisi analizi ve agaroz jel görüntüleme sistemi ile gös-terilmiştir. qPZR sırasındaki bağlanma sıcaklıklarından en düşük Ct değerinin 54°C’de elde edildiği görülmüştür. Ayrıca konvansiyonel Sanger dizileme sonucunda yalnızca ilgili miRNA dizilerinin hedef-lendiği ve spesifik olmayan herhangi bir çoğaltma işleminin olmadığı gösterilmiştir.

Sonuç: Sunulan çalışmada deneysel tasarım her iki miRNA hedefi için de optimize edilerek spesifik olarak yalnızca hedef miRNA mo-lekülünün tespitinin yapılabildiği gösterilmiştir. Bu yaklaşım, ilerleyen çalışmalarda miRNA tespit ve ekspresyon analizlerinde güvenilir olarak kullanılabilecektir. Sunulan yaklaşım, düşük maliyetli, zamandan ve iş gücünden tasarruf sağlayan bir alternatif olması sebebiyle benzer tüm çalışmalarda değerlendirilebilir.

Keywords

miRNA, RT-qPZR, miR-145, miR-146a

Supporting Institution

Bezmialem Vakıf Üniversitesi Bilimsel Araştırma Projeleri Birimi

Project Number

20200906

Optimization of One Step Reverse Transcription Quantitative PCR Method for miRNA Expression Analyses

Abstract

Objective: We aimed to investigate and optimize the one step re-verse transcription quantitative polymerase chain reaction (RT-qP-CR) method for specific detection and quantitation of two selected microRNA (miRNA)s, namely hsa-miR-145-5p and hsa-miR-146a-5p.

Material and Method: RNA was extracted from HEK293T cell line. Primers were designed and experimentally optimized to be compatible with with one step RT-qPCR method for two selected miRNAs. Targeted amplicons were visualized with agarose gel electrophoresis and sequenced using the Sanger method for specificity verification.

Results: High specificity of one step RT-qPCR amplification was demonstrated using melt curve and agarose gel electrophoresis analyses for both miRNA targets. It was shown that the earliest cycle threshold (Ct) values were obtained at the annealing tem-perature of 54°C. Also, target specificity was confirmed by con-ventional Sanger sequencing.

Conclusion: In this study, one-step RT-qPCR design was optimized for both miRNA targets and target specificity was verified. Our study showed this approach to be a good candidate for miRNA detection and quantitation as a cost-effective alternative method. Furthermore, the approach is highly suitable for research projects as it is both low-cost and fast, involving less hands-on time.

Keywords

miRNA, RT-qPCR, miR-145, miR-146a

Project Number

20200906

References

• 1. Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993; 75(5): 843-54. [CrossRef ]
• 2. Vishnoi A, Rani S. MiRNA Biogenesis and Regulation of Diseases: An Overview. Methods Mol Biol. 2017; 1509: 1-10. [CrossRef ]
• 3. Ha M, Kim VN. Regulation of microRNA biogenesis. Nat Rev Mol Cell Biol. 2014; 15(8): 509-24. [CrossRef ]
• 4. Michlewski G, Caceres JF. Post-transcriptional control of miRNA biogenesis. RNA. 2019; 25(1): 1-16. [CrossRef ]
• 5. Kozomara A, Birgaoanu M, Griffiths-Jones S. miRBase: from mic-roRNA sequences to function. Nucleic Acids Res. 2019; 47(D1): D155-D62. [CrossRef ]
• 6. Alles J, Fehlmann T, Fischer U, Backes C, Galata V, Minet M, et al. An estimate of the total number of true human miRNAs. Nucleic Acids Res. 2019; 47(7): 3353-64. [CrossRef ]
• 7. Hydbring P, Badalian-Very G. Clinical applications of microRNAs. F1000Res. 2013; 2: 136. [CrossRef ]
• 8. Yan J, Zhang N, Qi C, Liu X, Shangguan D. One-step real time RT-PCR for detection of microRNAs. Talanta. 2013; 110: 190-5. [CrossRef ]
• 9. Androvic P, Valihrach L, Elling J, Sjoback R, Kubista M. Two-tailed RT-qPCR: a novel method for highly accurate miRNA quantificati-on. Nucleic Acids Res. 2017; 45(15): e144. [CrossRef ]
• 10. Tian T, Wang J, Zhou X. A review: microRNA detection methods. Org Biomol Chem. 2015; 13(8): 2226-38. [CrossRef ]
• 11. Xie S, Zhu Q, Qu W, Xu Z, Liu X, Li X, et al. sRNAPrimerDB: compre-hensive primer design and search web service for small non-co-ding RNAs. Bioinformatics. 2019; 35(9): 1566-72. [CrossRef ]12. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, et al. MicroRNA expression profiles classify human cancers. Nature. 2005 ;435(7043): 834-8. [CrossRef ]
• 13. Huang Z, Shi J, Gao Y, Cui C, Zhang S, Li J, et al. HMDD v3.0: a data-base for experimentally supported human microRNA-disease as-sociations. Nucleic Acids Res. 2019; 47(D1): D1013-D7. [CrossRef ]
• 14. Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, Nguyen JT, et al. Real-time quantification of microRNAs by stem-loop RT-PCR. Nuc-leic Acids Res. 2005; 33(20): e179. [CrossRef ]
• 15. Saliminejad K, Khorram Khorshid HR, Soleymani Fard S, Ghaffari SH. An overview of microRNAs: Biology, functions, therapeutics, and analysis methods. J Cell Physiol. 2019; 234(5): 5451-65. [CrossRef ]
• 16. Robinson S, Follo M, Haenel D, Mauler M, Stallmann D, Tewari M, et al. Droplet digital PCR as a novel detection method for quantif-ying microRNAs in acute myocardial infarction. Int J Cardiol. 2018; 257: 247-54. [CrossRef ]
• 17. Li J, Yao B, Huang H, Wang Z, Sun C, Fan Y, et al. Real-time poly-merase chain reaction microRNA detection based on enzymatic stem-loop probes ligation. Anal Chem. 2009; 81(13): 5446-51. [CrossRef ]
• 18. Jin J, Vaud S, Zhelkovsky AM, Posfai J, McReynolds LA. Sensitive and specific miRNA detection method using SplintR Ligase. Nuc-leic Acids Res. 2016; 44(13): e116. [CrossRef ]
• 19. Mei Q, Li X, Meng Y, Wu Z, Guo M, Zhao Y, et al. A facile and specific assay for quantifying microRNA by an optimized RT-qPCR approa-ch. PLoS One. 2012; 7(10): e46890. [CrossRef ]
• 20. Benes V, Collier P, Kordes C, Stolte J, Rausch T, Muckentaler MU, et al. Identification of cytokine-induced modulation of microRNA expression and secretion as measured by a novel microRNA speci-fic qPCR assay. Sci Rep. 2015; 5: 11590. [CrossRef ]
• 21. Munafo DB, Robb GB. Optimization of enzymatic reaction conditi-ons for generating representative pools of cDNA from small RNA. RNA. 2010; 16(12): 2537-52. [CrossRef ]
• 22. Raymond CK, Roberts BS, Garrett-Engele P, Lim LP, Johnson JM. Simple, quantitative primer-extension PCR assay for direct mo-nitoring of microRNAs and short-interfering RNAs. RNA. 2005; 11(11): 1737-44. [CrossRef ]
• 23. Sharbati-Tehrani S, Kutz-Lohroff B, Bergbauer R, Scholven J, Eins-panier R. miR-Q: a novel quantitative RT-PCR approach for the expression profiling of small RNA molecules such as miRNAs in a complex sample. BMC Mol Biol. 2008; 9:34. [CrossRef ]
• 24. Benes V, Castoldi M. Expression profiling of microRNA using re-al-time quantitative PCR, how to use it and what is available. Met-hods. 2010; 50(4): 244-9. [CrossRef ]
• 25. Rai P, Kumar BK, Deekshit VK, Karunasagar I, Karunasagar I. Dete-ction technologies and recent developments in the diagnosis of COVID-19 infection. Appl Microbiol Biotechnol. 2021; 105(2): 441-55. [CrossRef ]
• 26. Wang J, Cai K, Zhang R, He X, Shen X, Liu J, et al. Novel One-Step Single-Tube Nested Quantitative Real-Time PCR Assay for High-ly Sensitive Detection of SARS-CoV-2. Anal Chem. 2020; 92(13): 9399-404. [CrossRef]