Optimizing microRNA quantification in serum samples

Document Type : Research Articles

Authors

• Sedigheh Gharbi ¹
• Fatemh Mirzadeh ¹
• Shahriar Khatrei ²
• Mohammad Reza Soroush ²
• Mahmood Tavallaie ³
• Mohammad Reza Nourani ³
• mehdi Sahmsara ⁴
• Seyyed Javad Mowla ¹

¹ Tarbiat Modares University

² Janbazan Medical and Engineering Research Center

³ Baqiyatallah University of Medical Sciences

⁴ Nat. Inst. of Genetic Eng. and Biotech

https://doi.org/10.22067/jcmr.v6i2.31710

Abstract

MicroRNAs constitute a group of small non-coding RNAs that negatively regulate gene expression. Aside from their contribution to biological and pathological pathways, altered expression of microRNAs is reported in bio-fluid samples, such as serum. To employ serum's microRNAs as potential biomarkers, it is crucial to develop an efficient method for microRNA quantification, avoiding pre-analytical and analytical variations which could affect the accuracy of data analysis. Here, we optimized a real-time PCR quantification procedure for microRNA detection in serum samples. Serum's total RNA was extracted using two different RNA isolation methods, one based on phenol-chloroform and the other based on silica column. To investigate a potential PCR inhibitory effect, different RNA amounts were subjected to reverse transcription. Moreover to assess the enzymatic efficiency, synthetic exogenous microRNAs was spiked into the mixture. Moreover, to find a reliable internal control gene for normalizing the microRNA quantification, the amounts of 8 candidate non-coding RNAs including SNORD38B, SNORD49A, U6, 5S rRNA, miR-423-5p, miR-191, miR-16 and miR-103 were assessed on serum samples. Altogether, our data demonstrated that the silica-based method was more efficient for microRNA recovery. Furthermore, increasing the input volume of the extracted RNA would dramatically increase inhibitors' amounts which could end up in a larger Cq values. Therefore, the best input volume of RNA turned out to be 1.5 microliter/reaction. Among the 8 aforementioned internal controls, U6, SNORD38B and SNORD49A showed low levels of expression, and were undetectable in some samples. Amongst the others, 5s rRNA, had the biggest standard deviation which could significantly affect data analysis. MiR-103 with the least variation appeared to be the best normalizer gene.

Keywords

• MicroRNA recovery
• Real-time PCR quantification
• Normalizer gene

20.1001.1.20089147.2014.6.2.2.8

References

Ajit S. K. (2012) Circulating microRNAs as biomarkers, therapeutic targets, and signaling molecules. Sensors (Basel) 12:3359-3369.

Bartel D. P. (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116:281-297.

Brase J., Wuttig D., Kuner R. and Sultmann H. (2010) Serum microRNAs as non-invasive biomarkers for cancer. Molecular Cancer 9: 306

Chen X., Liang H., Zhang J., Zen K. and Zhang C. Y. (2012) Secreted microRNAs: a new form of intercellular communication. Trends in cell biology 22:125-132.

Eldh M., Lotvall J., Malmhall C. and Ekstrom K. (2012) Importance of RNA isolation methods for analysis of exosomal RNA: evaluation of different methods. Molecular immunology 50:278-286.

Etheridge A., Lee I., Hood L., Galas D. and Wang K. (2011) Extracellular microRNA: a new source of biomarkers. Mutation research 717:85-90.

Fan K. L., Zhang H. F., Shen J., Zhang Q. and Li X. L. (2013) Circulating microRNAs levels in Chinese heart failure patients caused by dilated cardiomyopathy. Indian heart journal 65:12-16.

Friedman R. C., Farh K. K., Burge C. B. and Bartel D. P. (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome research 19:92-105.

Gilad S., Meiri E., Yogev Y., Benjamin S., Lebanony D., Yerushalmi N., Benjamin H., Kushnir M., Cholakh H., Melamed N., Bentwich Z., Hod M., Goren Y. and Chajut A. (2008) Serum microRNAs are promising novel biomarkers. PloS one 3:e3148.

Kloosterman W. P. and Plasterk R. H. (2006) The diverse functions of microRNAs in animal development and disease. Developmental cell 11:441-450.

Kosaka N. and Ochiya T. (2011) Unraveling the Mystery of Cancer by Secretory microRNA: Horizontal microRNA Transfer between Living Cells. Frontiers in genetics 2:97.

Kroh E. M., Parkin R. K., Mitchell P. S. and Tewari M. (2010) Analysis of circulating microRNA biomarkers in plasma and serum using quantitative reverse transcription-PCR (qRT-PCR). Methods 50:298-301.

McDonald J. S., Milosevic D., Reddi H. V., Grebe S. K. and Algeciras-Schimnich A. (2011) Analysis of circulating microRNA: preanalytical and analytical challenges. Clinical chemistry 57:833-840.

Mittelbrunn M. and Sanchez-Madrid F. (2012) Intercellular communication: diverse structures for exchange of genetic information. Nature reviews. Molecular cell biology 13:328-335.

Mraz M., Malinova K., Mayer J. and Pospisilova S. (2009) MicroRNA isolation and stability in stored RNA samples. Biochemical and biophysical research communications 390:1-4.

Taylor D. D. and Gercel-Taylor C. (2008) MicroRNA signatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer. Gynecologic oncology 110:13-21.

Wang K., Zhang S., Weber J., Baxter D. and Galas D. J. (2010) Export of microRNAs and microRNA-protective protein by mammalian cells. Nucleic acids research 38:7248-7259.