Prediction of miRNA in HIV1 genome and its target

Authors: Manish Kumar Gupta Kavita Agarwal Nutan Prakash Dev Bukhsh Singh Krishna Misra

Publish Date: 2012/09/06

Volume: 1, Issue: 4, Pages: 141-151

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Abstract

MicroRNAs miRNA are a class of noncoding RNA which inhibits the expression of a particular gene by the process of nucleotidesequencespecific posttranscriptional gene silencing method miRNAs are ~21 nt long noncoding RNAs that are derived from larger hairpin RNA precursors The short length of the miRNA sequences and relatively low conservation of premiRNA sequences restrict the conventional sequencealignmentbased methods of finding only relatively close homologs On the other hand it has been reported that miRNA genes are more conserved in the secondary structure of their precursor rather than in primary sequences Therefore secondary structural features should be fully exploited in the homologue search for new miRNA genes In this study an approach for identification and prediction of miRNA in viruses through artificial neural networks ANN has been proposed This idea uses both sequential and structural features of premiRNA to train the ANN for identification of miRNA in new viral genomes The designed ANN was found with an accuracy of 9368  for the training dataset and 5555  for the validation dataset In case of HIV this trained ANN identifies premiRNA which does not show sufficient homology to known premiRNA sequences but are highly conserved in their structure Finally single miRNA of length 19 mer has been predicted targeting four genes namely NDUFS7 WNT3A SUFU and FOXK1 a strict threshold at score 19 The results indicate that this method can be used for identifying novel miRNAs in other viral genomes with considerable successMicroRNAs miRNA are small regulatory RNAs ~21 nucleotides in length processed from short stem–loop precursors that are encoded in genomes of metazoans and viruses The genes of miRNA transcribe into primary miRNA which process to form premiRNA having length ~70 with stem loop structure with help of RNaseIII enzyme Drsoha in the animal cells Later Dicer which is a class of RNaseIII enzyme Grishok et al 2001 Hutvagner et al 2001 Ketting et al 2001 incises the premiRNAs to release the ~21 nucleotides mature miRNAs Lee et al 2002 2003 At the end RNAinduced silencing complexes RISC Hammond et al 2000 are produced to regulate the expression of target genes via complementary base pair interactions The precursor lengths of miRNA are more variable and their structures are more complex in plants but the maturation process of miRNA is comparable with that of Animals miRNAs can bind to the 3′ UTRs of messenger RNAs mRNAs and interfere with their translation thus contributing a significant posttranscriptional regulatory step in gene expression They have been shown to play an important role at the level of development apoptosis and establishment of cell lineage in various organisms He and Hannon 2004 Bartel 2004 Human immunodeficiency virus1 HIV1 infects human cells and incorporates its DNA into the host genome HIV1 infection stimulates expression of particular sets of cellular miRNAs including miR29a HIV1 mRNA is transcribed exported from the nucleus and translated into viral proteins Cellular RISC having specific miRNAs such as miR29a target HIV1 mRNA and sequestering the ribonucleoprotein RNP complex in P bodies Depending upon cellular stimuli or viral pathogenesis signs HIV1 mRNA could be stored in P bodies and released for subsequent translation of viral proteins Alternatively viral mRNA could be degraded in P bodies Nathans et al 2009Although the exact mechanism by which miRNA mediates regulation is not completely understood several experimental observations have been made which generalize the rules of miRNAtranscript binding Most important among them are the incomplete complementarity of pairing and the initial continuous base pairing referred to as the seed with base 2–8 of the 5′ end of the miRNA pairing completely with the 3′ UTR of the transcript Other general features like optimum minimum free energy MFE of the bound complex and conservation in related sequences also favor miRNA function HIV1 has been identified as an etiological agent responsible for acquired immune deficiency syndrome AIDS a fatal condition that arises by the invasion of the virus on various cells of the human immune system Arendt and Littman 2001 Emerman and Malim 1998 It has been reported that only copious miRNA genes can be easily detected by Polymerase Chain Reaction or northern blot due to limitations of the techniques For finding those lowexpression or tissuespecific miRNA genes computational prediction provides an efficient strategy Bartel 2004 Several miRNAs have been accounted in the herpesvirus family Pfeffer et al 2005 A computational approach has already been developed for predicting miRNA Lim et al 2003a b Lai et al 2003 Thomassen et al 2006 in animals considering both sequence and structure alignment Wang et al 2005Artificial neural networks are a form of artificial intelligence that can learn to predict through modeling answers to particular questions in complex data The models produced by ANNs have been shown to have the ability to predict well for unseen data and have the ability to cope with complexity and nonlinearity within the dataset these features of ANNs mean they have the potential to identify and model patterns in this type of data to address a particular question Anderson and McNeill 1995 Khan et al 2001 In this study we have developed an approach for computational identification of miRNA which utilizes the knowledge from homologybased search methods and machine learning approaches along with knowledgebased feature selection methods Van Hulse et al 2012 Liou and Huang 2012 Zillner and Sonntag 2012A significant number of miRNAs have been reported experimentally but majority of gene target are still unknown So Insilico target prediction tools remain the only way for a rapid identification of miRNA target Kiriakidou et al 2004 Alexiou et al 2009

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