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      In silico MCMV Silencing Concludes Potential Host-Derived miRNAs in Maize

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          Abstract

          Maize Chlorotic Mottle Virus (MCMV) is a deleterious pathogen which causes Maize Lethal Necrosis Disease (MLND) that results in substantial yield loss of Maize crop worldwide. The positive-sense RNA genome of MCMV (4.4 kb) encodes six proteins: P32 (32 kDa protein), RNA dependent RNA polymerases (P50 and P111), P31 (31 kDa protein), P7 (7 kDa protein), coat protein (25 kDa). P31, P7 and coat protein are encoded from sgRNA1, located at the 3′end of the genome and sgRNA2 is located at the extremity of the 3′genome end. The objective of this study is to locate the possible attachment sites of Zea mays derived miRNAs in the genome of MCMV using four diverse miRNA target prediction algorithms. In total, 321 mature miRNAs were retrieved from miRBase (miRNA database) and were tested for hybridization of MCMV genome. These algorithms considered the parameters of seed pairing, minimum free energy, target site accessibility, multiple target sites, pattern recognition and folding energy for attachment. Out of 321 miRNAs only 10 maize miRNAs are predicted for silencing of MCMV genome. The results of this study can hence act as the first step towards the development of MCMV resistant transgenic Maize plants through expression of the selected miRNAs.

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          Most cited references37

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          High-Throughput Sequencing of Arabidopsis microRNAs: Evidence for Frequent Birth and Death of MIRNA Genes

          In plants, microRNAs (miRNAs) comprise one of two classes of small RNAs that function primarily as negative regulators at the posttranscriptional level. Several MIRNA genes in the plant kingdom are ancient, with conservation extending between angiosperms and the mosses, whereas many others are more recently evolved. Here, we use deep sequencing and computational methods to identify, profile and analyze non-conserved MIRNA genes in Arabidopsis thaliana. 48 non-conserved MIRNA families, nearly all of which were represented by single genes, were identified. Sequence similarity analyses of miRNA precursor foldback arms revealed evidence for recent evolutionary origin of 16 MIRNA loci through inverted duplication events from protein-coding gene sequences. Interestingly, these recently evolved MIRNA genes have taken distinct paths. Whereas some non-conserved miRNAs interact with and regulate target transcripts from gene families that donated parental sequences, others have drifted to the point of non-interaction with parental gene family transcripts. Some young MIRNA loci clearly originated from one gene family but form miRNAs that target transcripts in another family. We suggest that MIRNA genes are undergoing relatively frequent birth and death, with only a subset being stabilized by integration into regulatory networks.
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            Practical Aspects of microRNA Target Prediction

            microRNAs (miRNAs) are endogenous non-coding RNAs that control gene expression at the posttranscriptional level. These small regulatory molecules play a key role in the majority of biological processes and their expression is also tightly regulated. Both the deregulation of genes controlled by miRNAs and the altered miRNA expression have been linked to many disorders, including cancer, cardiovascular, metabolic and neurodegenerative diseases. Therefore, it is of particular interest to reliably predict potential miRNA targets which might be involved in these diseases. However, interactions between miRNAs and their targets are complex and very often there are numerous putative miRNA recognition sites in mRNAs. Many miRNA targets have been computationally predicted but only a limited number of these were experimentally validated. Although a variety of miRNA target prediction algorithms are available, results of their application are often inconsistent. Hence, finding a functional miRNA target is still a challenging task. In this review, currently available and frequently used computational tools for miRNA target prediction, i.e., PicTar, TargetScan, DIANA-microT, miRanda, rna22 and PITA are outlined and various practical aspects of miRNA target analysis are extensively discussed. Moreover, the performance of three algorithms (PicTar, TargetScan and DIANA-microT) is both demonstrated and evaluated by performing an in-depth analysis of miRNA interactions with mRNAs derived from genes triggering hereditary neurological disorders known as trinucleotide repeat expansion diseases (TREDs), such as Huntington’s disease (HD), a number of spinocerebellar ataxias (SCAs), and myotonic dystrophy type 1 (DM1).
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              The diversity of RNA silencing pathways in plants.

              RNA silencing was discovered in plants as a mechanism whereby invading nucleic acids, such as transgenes and viruses, are silenced through the action of small (20-26 nt) homologous RNA molecules. Our understanding of small RNA biology has significantly improved in recent years, and it is now clear that there are several cellular silencing pathways in addition to those involved in defense. Endogenous silencing pathways have important roles in gene regulation at the transcriptional, RNA stability and translational levels. They share a common core of small RNA generator and effector proteins with multiple paralogs in plant genomes, some of which have acquired highly specialized functions. Here, we review recent developments in the plant RNA silencing field that have identified components of specific silencing pathways and have shed light on the mechanisms and biological roles of RNA silencing in plants.
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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                28 March 2017
                2017
                : 8
                : 372
                Affiliations
                [1] 1Center of Excellence in Molecular Biology, University of the Punjab Lahore, Pakistan
                [2] 2Institute of Biochemistry and Biotechnology, University of the Punjab Lahore, Pakistan
                Author notes

                Edited by: Ekaterina Shelest, Leibniz-Institut für Naturstoff-Forschung und Infektionsbiologie - Hans Knöll Institut, Germany

                Reviewed by: Michael Poidinger, Singapore Immunology Network (A*STAR), Singapore; Rosalba Giugno, University of Verona, Italy

                *Correspondence: Muhammad Shahzad Iqbal shzad@ 123456live.com

                This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2017.00372
                5368279
                c1234b45-17d9-41c5-8a36-1fa43c634387
                Copyright © 2017 Iqbal, Jabbar, Sharif, Ali, Husnain, Henry and Nasir.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 11 January 2017
                : 03 March 2017
                Page count
                Figures: 7, Tables: 1, Equations: 0, References: 48, Pages: 9, Words: 5115
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                maize chlorotic mottle virus (mcmv),miranda,tapirhybrid,targetfinder,rna22,r language,mirna,target prediction

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