Next generation sequencing for profiling expression of miRNAs: technical progress and applications in drug development

Jie Liu, Steven F. Jennings, Weida Tong, Huixiao Hong
DOI: 10.4236/jbise.2011.410083                

Abstract

miRNAs are non-coding RNAs that play a regulatory role in expression of genes and are associated with diseases. Quantitatively measuring expression levels of miRNAs can help understanding the mechanisms of human diseases and discovering new drug targets. There are three major methods that have been used to measure the expression levels of miRNAs: real-time reverse transcription PCR (qRT-PCR), microarray, and the newly introduced next-generation sequencing (NGS). NGS is not only suitable for profiling of known miRNAs that qRT-PCR and microarray can do too but also able to detect unknown miRNAs that the other two methods are incapable. Profiling of miRNAs by NGS has been progressed rapidly and is a promising field for applications in drug development. This paper will review the technical advancement of NGS for profiling miRNAs, including comparative analyses between different platforms and software packages for analyzing NGS data. Examples and future perspectives of applications of NGS profiling miRNAs in drug development will be discussed.

Keywords

MiRNAs; Next-Generation Sequencing; Expression; Data Analysis; Drug Development

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Lee, R.C., Feinbaum, R.L. and Ambros, V. (1993) The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell, 75, 843- 854. doi:10.1016/0092-8674(93)90529-Y
[2]	Bartel, D.P. (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 116, 281-297. doi:10.1016/S0092-8674(04)00045-5
[3]	Bartel, D.P. (2009) MicroRNAs: target recognition and regulatory functions. Cell, 136, 215-233. doi:10.1016/j.cell.2009.01.002
[4]	Griffiths-Jones, S., Saini, H.K., van Dongen, S. and En- right, A.J. (2008) miRBase: Tools for microRNA genom- ics. Nucleic Acids Research, 36, D154-D158.
[5]	Sheng, Y., Engstrom, P.G. and Lenhard, B. (2007) Mam- malian microRNA prediction through a support vector machine model of sequence and structure. PLoS One, 2, e946. doi:10.1371/journal.pone.0000946
[6]	van den Berg, A., Mols, J. and Han, J. (2008) RISC- target interaction: Cleavage and translational suppression. Biochimica Biophysica Acta, 1779, 668-677.
[7]	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.
[8]	Bueno, M.J., de Castro, I.P. and Malumbres, M. (2008) Control of cell proliferation pathways by microRNAs. Cell Cycle, 7, 3143-3148. doi:10.1101/gr.082701.108
[9]	He, L. and Hannon, G.J. (2004) MicroRNAs: small RNAs with a big role in gene regulation. Nature Reviews Genetics, 5, 522-531. doi:10.1038/nrg1379
[10]	Jovanovic, M. and Hengartner, M.O. (2006) microRNAs and apoptosis: RNAs to die for. Oncogene, 25, 6176- 6187. doi:10.1038/sj.onc.1209912
[11]	Krutzfeldt, L. and Stoffel, M. (2006) MicroRNAs: A new class of regulatory genes affecting metabolism. Cell Matabolism, 4, 9-12. doi:10.1016/j.cmet.2006.05.009
[12]	Stefani, G. and Slack, F.J. (2008) Small noncoding RNAs in animal development. Nature Reviews Molar Cell Biology, 9, 219-230. doi:10.1038/nrm2347
[13]	He, X., Eberhart, J.K. and Postlethwait J.H. (2009) Mi- croRNAs and micro-managing the skeleton in diease, development, and evolution. Journal of Cellular and Molecular Medicine, 13, 606-618. doi:10.1111/j.1582-4934.2009.00696.x
[14]	Schulte, J.H., Marschall. T., Martin, M., et al. (2010) Deep sequencing reveals differential expression of mi- croRNAs in favorable versus unfavorable neuroblastoma. Nucleic Acids Research, 38, 5919-5928. doi:10.1093/nar/gkq342
[15]	Fasanaro, P., Greco, S., Ivan, M., Capogrossi, M.C. and Martelli, F. (2010) microRNA: Emerging therapeutic tar- gets in acute ischemic diseases. Pharmacology Therapeutics, 125, 92-104. doi:10.1016/j.pharmthera.2009.10.003
[16]	Trang, P., Weidhaas, J.B. and Slack, F.J. (2008) Mi- croRNAs as potential cancer therapeutics. Oncogene, 27, S52-S57. doi:10.1038/onc.2009.353
[17]	Jiang, Q., Wang, Y., Hao, Y., et al. (2009) miR2Disease: A manually curated database for microRNA deregulation in human disease. Nucleic Acid Research, 37, D98-D104. doi:10.1093/nar/gkn714
[18]	Hennessy, E. and O’Driscoll, L. (2008) Molecular medi- cine of microRNAs: Structure, function, and implications for diabetes. Expert Reviews in Molecular Medicine, 10, e24. doi:10.1017/S1462399408000781
[19]	van Rooij, E., Sutherland, L.B., Liu, N., et al. (2006) A signature pattern of stress-responsive microRNAs that can evoke cardiac hypertrophy and heart failure. Proceedings of the National Academy of Sciences, 103, 18255-18260. doi:10.1073/pnas.0608791103
[20]	Barbato, C., Giorge, C., Catalanotto, C. and Cogoni C. (2008) Thinking about RNA? MicroRNAs in the brain. Mammalian Genome, 19, 541-551. doi:10.1007/s00335-008-9129-6
[21]	Beveridge, N.J., Gardiner, E., Carroll, A.P., et al. (2009) Schizophrenia is associated with an increase in cortical microRNA biogenesis. Molecular Psychiatry, 15, 1176- 1189. doi:10.1038/mp.2009.84
[22]	Medina, P.P. and Slack, F.J. (2008) microRNAs and can- cer: An overview. Cell Cycle, 7, 2485-2492. doi:10.4161/cc.7.16.6453
[23]	Nana-Sinkam, S.P. and Croce, C.M. (2011) MicroRNAs as therapeutic targets in cancer. Translational Research, 157, 216-225. doi:10.1016/j.trsl.2011.01.013
[24]	Chen, C.Z. (2005) MicroRNAs as oncogenes and tumor suppressors. The New England Journal of Medicine, 353, 1768-1771. doi:10.1056/NEJMp058190
[25]	Lagos-Quintana, M., Rauhut, R., Lendeckel, W. and Tuschl, T. (2001) Identification of novel genes coding for small expressed RNAs. Science, 294, 853-858. doi:10.1126/science.1064921
[26]	Chen, C., Ridzon, D.A., Broomer, A.J., et al. (2005) Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Research, 33, e179. doi:10.1093/nar/gni178
[27]	Shi, R. and Chiang, V.L. (2005) Facile means for quanti- fying microRNA expression by real-time PCR. Biotech- niques, 39, 519-525. doi:10.2144/000112010
[28]	Yin, J.Q., Zhao, R.C. and Morris, K.V. (2008) Profiling microRNA expression with microarrays. Trends Bio- technol, 26, 70-76. doi:10.1016/j.tibtech.2007.11.007
[29]	Li, W. and Ruan, K. (2009) MicroRNA detection by mi- croarray. Analytical Bioanalytical Chemistry, 394, 1117- 1124. doi:10.1007/s00216-008-2570-2
[30]	Hafner, M., Landgraf, P., Ludwig, J., et al. (2008) Identi- fication of microRNAs and other small regulatory RNAs using cDNA library sequencing. Methods, 44, 3-12. doi:10.1016/j.ymeth.2007.09.009
[31]	Roush, S. and Slack, F.J. (2008) The let-7 family of mi- croRNAs. Trends in Cell Biology, 18, 505-516. doi:10.1016/j.tcb.2008.07.007
[32]	Buermans, H.P., Ariyurek, Y., van Ommen, G., et al. (2010) New methods for next generation sequencing based microRNA expression profiling. BMC Genomics, 11, 716. doi:10.1186/1471-2164-11-716
[33]	Metzker, M.L. (2010) Sequencing technologies—the next generation. Nature Reviews Genetics, 11, 31-46. doi:10.1038/nrg2626
[34]	‘t Hoen, P.A, Ariyurek, Y., Thygesen, H.H., et al. (2008) Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab por- tability over five microarray platforms. Nucleic Acids Research, 36, e141. doi:10.1093/nar/gkn705
[35]	Sanger, f., Air, g.m., Barrell, b.g., et al. (1977) Nucleo- tide sequence of bacteriophage X174 D. Nature, 265, 687-695.
[36]	Sanger, F., Nicklen, S., Coulson, A.R., et al. (1977) DNA sequencing with chain-terminating inhibitors. Proceedings of the National Academy of Sciences, 74, 5463- 5467. doi:10.1073/pnas.74.12.5463
[37]	Margulies, M., Egholm, M., Altman, W.E., et al. (2005) Genome sequencing in microfabricated high-density pi- colitre reactors. Nature, 437, 376-380.
[38]	Mocali, S. and Benedetti, A. (2010) Exploring research frontiers in microbiology: the challenge of metagenomics in soil microbiology. Research in Microbiology, 161, 497-505. doi:10.1016/j.resmic.2010.04.010
[39]	Langmead, B., Trapnell, C., Pop, M. and Salzberg, S.L. (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology, 10, R25. doi:10.1186/gb-2009-10-3-r25
[40]	Li, H. and Durbin, R. (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinfor- matics, 25, 1754-1760. doi:10.1093/bioinformatics/btp324
[41]	Li, H.; Ruan, J. and Durbin, R. (2008) Mapping short DNA sequencing reads and calling variants using map- ping quality scores. Genome Research, 18, 1851-1858. doi:10.1101/gr.078212.108
[42]	Jiang, H. and Wong, W.H. (2008) SeqMap: mapping massive amount of oligonucleotides to the genome. Bio- informatics, 24, 2395-2396. doi:10.1093/bioinformatics/btn429
[43]	Li, R., Li, Y., Kristiansen, K. and Wang, J. (2008) SOAP: short oligonucleotide alignment program. Bioinformatics, 24, 713-714. doi:10.1093/bioinformatics/btn025
[44]	Trapnell, C., Pachter, L. and Salzberg, S.L. (2009) To- pHat: discovering splice junctions with RNA-Seq. Bio- informatics, 25, 1105-1111. doi:10.1093/bioinformatics/btp120
[45]	Yang, J.H., Shao, P., Zhou, H., Chen, Y.Q. and Qu, L.H. (2010) deepBase: a database for deeply annotating and mining deep sequencing data. Nucleic Acids Research, 38, D123-D130. doi:10.1093/nar/gkp943
[46]	Betel, D., Wilson, M., Gabow, A., Marks, D.S. and Sander, C. (2008) The microRNA.org resource: targets and expression. Nucleic Acids Research, 36, D149-D153. doi:10.1093/nar/gkm995
[47]	Alexiou, P., Vergoulis, T., Gleditzsch, M., et al. (2010) miRGen 2.0: a database of microRNA genomic informa- tion and regulation. Nucleic Acids Research, 38, D137- D141. doi:10.1093/nar/gkp888
[48]	Hsu, S.D., Chu, C.H. and Tsou, A.P. (2008) miRNAMap 2.0: genomic maps of microRNAs in metazoan genomes. Nucleic Acids Research, 36, D165-D169. doi:10.1093/nar/gkm1012
[49]	Zhang, Z., Yu, J., Li, D., et al. (2010) PMRD: plant mi- croRNA database. Nucleic Acids Res, 38, D806-D813. doi:10.1093/nar/gkp818
[50]	John, B., Enright, A.J., Aravin, A., et al. (2004) Human MicroRNA targets. PLoS Biology, 2, e363. doi:10.1371/journal.pbio.0020363
[51]	Lewis, B.P., Shih, I.H., Jones-Rhoades, M.W., et al. (2003) Prediction of mammalian microRNA targets. Cell, 115, 787-798. doi:10.1016/S0092-8674(03)01018-3
[52]	Kruger, J. and Rehmsmeier, M. (2006) RNAhybrid: mi- croRNA target prediction easy, fast and flexible. Nucleic Acids Research, 34, W451-W454. doi:10.1093/nar/gkl243
[53]	Hackenberg, M., Sturm, M., Langenberger, D., Fal- cón-Pérez, J.M. and Aransay, A.M. (2009) miRanalyzer: a microRNA detection and analysis tool for next-genera- tion sequencing experiments. Nucleic Acids Research, 37, W68-W76.
[54]	Hackenberg, M., Rodríguez-Ezpeleta, N. and Aransay, A.M. (2011) miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Research, 39, W132-W138. doi:10.1093/nar/gkr247
[55]	Moxon, S., Schwach, F. and Dalmay, T. (2008) A toolkit for analysing large-scale plant small RNA datasets. Bioinformatics, 24, 2252-2253. doi:10.1093/bioinformatics/btn428
[56]	Friedl?nder, M.R., Chen, W., Adamidi, C., et al. (2008) Discovering microRNAs from deep sequencing data us- ing miRDeep. Nature Biotechnology, 26, 407-415. doi:10.1038/nbt1394
[57]	Wang, W.C., Lin, F.M. and Chang, W.C. (2009) miREx- press: analyzing high-throughput sequencing data for profiling microRNA expression. BMC Bioinformatics, 10, 328. doi:10.1186/1471-2105-10-328
[58]	Breiman, L. (2001) Random forests. Machine Learning, 45, 5-32. doi:10.1023/A:1010933404324
[59]	Prüfer, K., et al. (2008) PatMaN: rapid alignment of short sequences to large databases. Bioinformatics, 24, 1530- 1531. doi:10.1093/bioinformatics/btn223
[60]	DiMasi, J.A., Hansen, R.W. and Grabowski, H.G. (2003) The Price of Innovation: New Estimates of Drug Devel- opmentCosts. Journal of Health Economics, 22, 151-185. doi:10.1016/S0167-6296(02)00126-1
[61]	Adams, C.P. and Brantner, V.V. (2006) Estimating The Cost Of New Drug Development: Is It Really $802 Mil- lion? Health Affairs, 25, 420-428. doi:10.1377/hlthaff.25.2.420
[62]	Dreyer, J.L. (2010) New insights into the roles of mi- croRNAs in drug addiction and neuroplasticity. Genome Medicine, 2, 92. doi:10.1186/gm213
[63]	Ueda, T., Volinia, S, Okumura, H., et al. (2010) Relation between microRNA expression and progression and prognosis of gastric cancer: a microRNA expression analysis. Lancet Oncol, 11, 136-146. doi:10.1016/S1470-2045(09)70343-2
[64]	Kumar, M.S., Lu, J., Mercer, K.L., et al. (2007) Impaired microRNA processing enhances cellular transformation and tumorigenesis. Nature Genetics, 39, 673-677. doi:10.1038/ng2003
[65]	Mitchell, P.S., Parkin, R.K., Kroh, E.M., et al. (2008) Circulating microRNAs as stable blood-based markers for cancer detection. Proceedings of the National Academy of Sciences, 105, 10513-10518. doi:10.1073/pnas.0804549105
[66]	Long, J.M. and Lahiri, D.K. (2011) MicroRNA-101 downregulates Alzheimer’s amyloid-β precursor protein levels in human cell cultures and is differentially ex- pressed. Biochem and Biophysical Research Communations, 404, 889-895. doi:10.1016/j.bbrc.2010.12.053
[67]	Jopling, C.L., Yi, M., Lancaster, A.M., et al. (2005) Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA. Science, 309, 1577-1581. doi:10.1126/science.1113329
[68]	Villanueva, R.A., Jangra, R.K., Yi, M., et al. (2010) miR- 122 does not modulate the elongation phase of hepatitis C virus RNA synthesis in isolated replicase complexes. Antiviral Research, 88, 119-123. doi:10.1016/j.antiviral.2010.07.004
[69]	Su, Z., Ning, B., Fang, H., et al. (2011) Next-generation sequencing and its applications in molecular diagnostics. Expert Review of Molecular Diagnonstics, 11, 333-343.
[70]	Rogers, G.B. and Bruce, K.D. (2010) Next-generation sequencing in the analysis of human microbiota:essential considerations for clinical application. Molecular Diagnosis & Therapy, 14, 343-350.