Abstract
The microRNAs (miRNAs) are small non-coding RNAs which play an important role in gene regulation and are involved in several biological functions. Studies have shown that there are several hundreds of them across (human) genome. And one miRNA may be involved in several genes and several miRNA may target a gene. In this regard it is interesting to know whether these several known miRNAs show structural and functional similarities. Do they fall into recognisable groups with respect to their structure and function and does the length of miRNA follow evolutionary principles and are highly conserved?. This study with the help of statistical tools explores characterising, identification of (human) miRNA based on their structure and function, network analysis of their relationship and target genes and conservation of their length and sequence structure across species.
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References
Abelson JF, Kwan KY, O’Roak BJ, Baek DY, Stillman AA, Morgan TM, Mathews CA, Pauls DL, Rasin MR, Gunel M, Davis NR, Ercan-Sencicek AG, Guez DH, Spertus JA, Leckman JF, Dure LS, Kurlan R, Singer HS, Gilbert DL, Farhi A, Louvi A, Lifton RP, Sestan N, State MW (2005) Sequence variants in SLITRK1 are associated with Tourett’s syndrome. Science 310(5746):317–320
Agostini F, Dapas B, Farra R, Grassi M, Racchi G, Klingel K, Kandolf R, Heidenreich O, Mercatanti A, Rainaldi G et al (2006) Potential applications of small interfering RNAs in the cardiovascular field. Drugs Future 31(6):513–525
Ambros V (2004) The functions of animal microRNAs. Nature 431:350–355
Ambros V, Bartel B, Bartel DP, Burge CB et al (2003) A uniform system for microRNA annotation. RNA 9:277–279
Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism and function. Cell 116(2): 281–297
Basu S, Burma DP, Chaudhuri P (2003) Words in DNA sequences: some case studies based on their frequency statistics. J Math Biol 46:479–503
Bouamar H, Jiang D, Wang L, Lin AP, Ortega M, Aguiar RC (2015) MicroRNA 155 control of p53 activity is context dependent and mediated by Aicda and Socs1. Mol Cell Biol 35(8): 1329–1340
Brennecke J, Hipfner DR, Stark A, Russel RB, Cohen SM (2003) Bantam encodes a developmentally regulated microRNA that controls cell proliferation and regulates the proapoptotic gene hid in Drosophila. Cell 113:25–36
Brennecke J, Stark A, Russel RB, Cohen SM (2005) Principles of micro-RNA-target recognition. PLoS Biol 3:e85
Brody E, Abelson J (1985) The “spliceosome” yeast pre-messenger RNA associates with a 40S complex in a splicing-dependent reaction. Science 228(4702):963–967
Chaudhuri P, Das S (2001) Statistical analysis of large DNA sequences using distribution of DNA words. Curr Sci 80:1161–1166
Chaudhuri P, Das S (2002) SWORDS: a statistical tool for analyzing large DNA sequences. J Biosci 27:1–6
Crick FHC (1958) On protein synthesis. Symp Soc Exp Biol 12:138–163
Crick FHC (1968) The origin of the genetic code. J Mol Biol 38:367–379
Crick FHC (1970) Central dogma of molecular biology. Nature 227:561–563
Crick FHC (1988) What made pursuit. Basic Books, New York
Denli AM, Tops BB, Plasterk RH, Ketting RF, Hannon GJ (2004) Processing of primary microRNAs by the microprocessor complex. Nature 432(7014):231–240
Doolittle WF (1978) Genes in pieces: were they ever together? Nature 272:581–582
Doolittle WF, Fraser P, Gerstein MB, Graveley BR (2013) Sixty years of genome biology. Genome Biol 14(4):113–119
Dusl M, Senderek J, Müller JS, Vogel JG, Pertl A, Stucka R, Lochmüller H, David R, Abicht A (2015) A 3′-UTR mutation creates a microRNA target site in the GFPT1 gene of patients with congenital myasthenic syndrome. Hum Mol Genet 24(8). doi:10.1093/hmg/ddv090
Fantini B (2006) History of central dogma of molecular biology and its epistemological status today, Geneva, February 22–23, 2007. Hist Philos Life Sci 28:487–609
Lian F, Cui Y, Zhou C, Gao K, Wu L (2015) Identification of a plasma four-microRNA panel as potential noninvasive biomarker for osteosarcoma. PLoS One 10(3):e0121499. doi:10.1371/journal.pone.0121499
Franklin RE, Gosling RG (1953) Molecular configuration in sodium thymonucleate. Nature 171(4356):740–741
Gilbert W (1978) Why genes in pieces? Nature 271(5645):501
Gregory RI, Yan KP, Amuthan G, Chendrimada T, Doratotaj B, Cooch N, Shiekhattar R (2004) The microprocessor complex mediates the genesis of microRNAs. Nature 432(7014):235–240
Griffiths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR (2003) Rfam: an RNA family database. Nucleic Acids Res 31:439–441
Griffiths-Jones S et al (2005) miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 34(Suppl 1):D140–D144
Gu W, Wang X, Zhail C, Xie X, Zhou T (2012) Selection on synonymous sites for increased accessibility around miRNA binding sites in plants. Mol Biol Evol 29:3037–3044
Han J, Han J, Lee Y, Yeom K, Nam J, Heo I, Rhee J, Sohn SY, Cho Y, Zhang BT, Kim VN (2004) The Drosha-DGCR8 complex in primary microRNA processing. Genes Dev 18(24):3016–3027
Huang Y, Gu X (2007) A bootstrap based analysis pipeline for efficient classification of phylogenetically related animal miRNAs. BMC Genomics 8:66. doi:10.1186/1471-2164-8-66
Jeffreys AJ, Flavel RA (1977) The rabbit beta-globin gene contains a large large insert in the coding sequence. Cell 12(4):1097–1108
Jiang Q et al (2009) miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 37(Database issue):D98–D104
John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS (2004) Human microRNA targets. PLoS Biol 2:e363. doi:10.1371/journal.pbio.0020363, pmid:15502875
Kamanu TKK, Radovanovic A, Archer JAC, Bajic VB (2013) Exploration of miRNA families for hypotheses generation. Nat Sci Rep 3:2940. doi:10.1038/srep02940
Kefas B, Comeau L, Floyd DH, Seleverstov O, Godlewski J, Schmittgen T et al (2009) The neuronal microRNA miR-326 acts in a feedback loop with notch and has therapeutic potential against brain tumors. J Neurosci 29:15161–15168. doi:10.1523/JNEUROSCI.4966-09.2009, pmid:19955368
Kefas B, Comeau L, Erdle N, Montgomery E, Amos S, Purow B (2010) Pyruvate kinase M2 is a target of the tumor-suppressive microRNA-326 and regulates the survival of glioma cells. Neuro Oncol 12:1102–1112. doi:10.1093/neuonc/noq080, pmid:20667897
Kruger J, Rehmsmeier M (2006) RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res 34:W451–W454. doi:10.1093/nar/gkl243, pmid:16845047
Kumar S (2007) MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol Biol Evol 24:1596–1599
Lau NC, Lim LP, Weinstein EG, Bartel DP (2001) An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 294(5543):858–862
Lee RC, Ambros V (2001) An extensive class of small RNAs in Caenorhabditis elegans. Science 294:862–864
Lee RC, Feinbaum RL, Ambros V (1993) The C elegans heterochronic gene lin-4 encodes small RNAs with artisense complementarity to lin-14. Cell 75:843–854
Lee Y et al (2004) MicroRNA genes are transcribed by RNA polymerase II. EMBO J 23: 4051–4060
Lorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, Sabbioni S, Magri E, Pedriali M, Fabbri M, Campiglio M, Ménard S, Palazzo JP, Rosenberg A, Musiani P, Volinia S, Nenci I, Calin GA, Querzoli P, Negrini M, Croce CM (2005) MicroRNA gene expression deregulation in human breast cancer. Cancer Res 65(16):7065–7070
Ma L, Teruya-Feldstein J, Weinberg RA (2007) Tumour invasion and metastasis initiated bu microRNA-10b in breast cancer. Nature 449(7163):682–688
Mathews DH, Sabina J, Zuker M, Turner DH (1999) Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J Mol Biol 288:911–940
miRBase (2010) Sanger miRBase sequence database. http://microrna.sanger.ac.uk/sequences/
Mendell JT (2008) myRiad roles for the miR-17-92 cluster in development and disease. Cell 133(2):217–222
miRBase (2010) Sanger miRBase sequence database. microrna.sanger.ac.uk/sequences/
Morange M (2006) The protein side of the central dogma:permanence and change. Hist Philos Life Sci 28:513–524
Morange M (2008) What history tells us XIII. Fifty years of central dogma. J Biosci 33(2):171–175
Mulder C, Arrand JR, Delius H, Keller W, Pettersson U, Roberts RJ, Sharp PA (1975) Cleavage maps of DNA from adenovirus types 2 and 5 by restriction endonucleases EcoRI and HpaI. Cold Spring Harb Symp Quant Biol 39(Pt 1):397–400
Olsen PH, Ambros V (1999) The lin-4 regulatory RNA controls developmental timing in C. elegans by blocking LIN-14 protein synthesis after the initiation of translation. Dev Biol 2:671–680
Piriyapongsa J, Mariño-Ramírez L, Jordan IK (2007) Origin and evolution of human microRNAs from transposable elements. Genetics 176:1323–1337
Prabhakar S, Noonan JP, Pääbo S, Rubin EM (2007) Accelerated evolution of conserved noncoding sequences in humans. Science 314(5800):786
Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, Horvitz HR, Ruvkun G (2000) The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 403:901–906
Rhee J-K, Shin S-Y, Zhang B-T (2013) Construction of microRNA functional families by a mixture model of position weight matrices. Peer J 1:e199. doi:10.7717/peerj.199
Ruvkun G (2001) Molecular biology: glimpses of a tiny RNA world. Science 294:797–799
Sarazin A, Voinnet O (2014) Exploring new models of easiRNA biogenesis. Nat Genet 46(6):530. doi:10.1038/ng.2993
Sharp PA, Sugen B, Sambrook J (1973) Detection of two restriction endonuclease activities
Sinha S, Vasulu TS, Rajat KD (2009) Performance and evaluation of microRNA gene identification tools. J Proteomics Bioinform 2(8):336–343
Smielewska MM (2008) The role of miRNAs and PiRNAs in planarian regeneration. UMI, Ann Arbor
Soifer H et al (2007) MicroRNAs in disease and potential therapeutic applications. Mol Ther 15:2070–2079
Chung SH, Gillies M, Sugiyama Y, Zhu L, Lee S-R, Shen W (2015) Profiling of microRNAs involved in retinal degeneration caused by selective Müller cell ablation. PLoS One 10(3):e0118949
van Rooij E et al (2007) MicroRNAs: powerful new regulators of heart disease and provocative therapeutic targets. J Clin Invest 117:2369–2376
Vergoulis T et al (2012) TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 40:D222–D229
Vergoulis T, Kanellos I, Kostoulas N, Georgakilas G, Sellis T, Hatzigeorgiou A, Dalamagas T (2015) mirPub: a databse for searching microRNA publications. Bioinformatics 31(2):1–3
Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, Visone R, Lorio M, Roldo C, Ferracin M, Prueitt RL, Yanaihara N, Lanza G, Scarpa A, Vecchione A, Negrini M, Harris CC, Croce CM (2006) A microRNA expression signature of human solid tumor cancer gene targets. Proc Natl Acad Sci U S A 103(7):2257–2261
Watson JD (1965) Molecular biology of the gene. W A Benjamin, New York
Watson JD, Crick FH (1953) Molecular structure of nucleic acids: a structure for deoxy rebose nucleic acid. Nature 153:737–738
Wightman B, Ha I, Ruvkun G (1993) Posttransacriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell 75(5):855–862
Wilkins MHF, Stokes AR, Wilson HR (1953) Molecular structure of deoxypentose nucleic acids. Nature 171(4356):738–740
Woese CR (1967) The genetic code: the molecular basis for genetic expression. Harper and Row, New York
Woese CR (2001) Translation: in retrospect and prospect. RNA 7:1055–1067
Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T (2009) miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res 37(Database issue):D105–D110. doi:10.1093/nar/gkn851, Epub 2008 Nov 7
Xie B et al (2013) MirCancer: a microRNA-cancer association database constructed by text mining literature. Bioinformatics 29:638–644
Xu P, Vernooy SY, Guo M, Hay BA (2003) The Drosophila microRNA Mir-14 suppresses cell death and is required for normal fat metabolism. Curr Biol 13(9):790–795
Yu X, Lin J, Zack DJ et al (2008) Analysis of regulatory network topology reveals functionally distinct classes of microRNAs. Nucleic Acids Res 36(20):6494–6503
Yu X, Lin J, Zack DJ et al (2008) Analysis of regulatory network topology reveals functionally distinct classes of microRNAs. Nucleic Acids Res 36(20):6494–503
Zamore PD (2002) Ancient pathways programmed by small RNAs. Science 296:1265–1269
Zeng Y, Wagner EJ, Cullen BR (2002) Both natural and designed microRNAs can inhibit the expression of cognate mRNAs when expressed in human cells. Mol Cell 9:1327–1333
Zhang H-M, Kuang S, Xiong X, Gao T, Liu C, Guo A-Y (2015) Transcription factor and microRNA co-regulatory loops: important regulatory motifs in biological processes and diseases. Brief Bioinform 16:45–58
Zhang Y, Li M, Wang H, Fisher WE, Lin PH, Yao Q et al (2009) Profiling of 95 microRNAs in pancreatic cancer cell lines and surgical specimens by real-time PCR analysis. World J Surg 33:698–709. doi:10.1007/s00268-008-9833-0, pmid:19030927
Zhang Z-L, Bai Z-H, Wang X-B, Bai L, Miao F, Pei H-H (2015) miR-186 and 326 predict the prognosis of pancreatic ductal adenocarcinoma and affect the proliferation and migration of cancer cells. PLoS One 10(3). doi:10.1371/journal.pone.0118814
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Chakraborty, M., Chatterjee, A., Krithika, S., Vasulu, T.S. (2015). A Statistical Analysis of MicroRNA: Classification, Identification and Conservation Based on Structure and Function. In: Dasgupta, R. (eds) Growth Curve and Structural Equation Modeling. Springer Proceedings in Mathematics & Statistics, vol 132. Springer, Cham. https://doi.org/10.1007/978-3-319-17329-0_13
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