Dataset Information

Mobilization of a plant transposon by expression of the transposon-encoded anti-silencing factor.

ABSTRACT: Transposable elements (TEs) have a major impact on genome evolution, but they are potentially deleterious, and most of them are silenced by epigenetic mechanisms, such as DNA methylation. Here, we report the characterization of a TE encoding an activity to counteract epigenetic silencing by the host. In Arabidopsis thaliana, we identified a mobile copy of the Mutator-like element (MULE) with degenerated terminal inverted repeats (TIRs). This TE, named Hiun (Hi), is silent in wild-type plants, but it transposes when DNA methylation is abolished. When a Hi transgene was introduced into the wild-type background, it induced excision of the endogenous Hi copy, suggesting that Hi is the autonomously mobile copy. In addition, the transgene induced loss of DNA methylation and transcriptional activation of the endogenous Hi. Most importantly, the trans-activation of Hi depends on a Hi-encoded protein different from the conserved transposase. Proteins related to this anti-silencing factor, which we named VANC, are widespread in the non-TIR MULEs and may have contributed to the recent success of these TEs in natural Arabidopsis populations.

SUBMITTER: Fu Y

PROVIDER: S-EPMC3773815 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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Mobilization of a plant transposon by expression of the transposon-encoded anti-silencing factor.

Fu Yu Y Kawabe Akira A Etcheverry Mathilde M Ito Tasuku T Toyoda Atsushi A Fujiyama Asao A Colot Vincent V Tarutani Yoshiaki Y Kakutani Tetsuji T

The EMBO journal 20130730 17

Transposable elements (TEs) have a major impact on genome evolution, but they are potentially deleterious, and most of them are silenced by epigenetic mechanisms, such as DNA methylation. Here, we report the characterization of a TE encoding an activity to counteract epigenetic silencing by the host. In Arabidopsis thaliana, we identified a mobile copy of the Mutator-like element (MULE) with degenerated terminal inverted repeats (TIRs). This TE, named Hiun (Hi), is silent in wild-type plants, bu ...[more]

PMID: 23900287

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Project description:Viral encoded RNA silencing suppressor proteins interfere with the host RNA silencing machinery, facilitating viral infection by evading host immunity. In plant hosts, the viral proteins have several basic science implications and biotechnology applications. However in silico identification of these proteins is limited by their high sequence diversity. In this study we developed supervised learning based classification models for plant viral RNA silencing suppressor proteins in plant viruses. We developed four classifiers based on supervised learning algorithms: J48, Random Forest, LibSVM and Naïve Bayes algorithms, with enriched model learning by correlation based feature selection. Structural and physicochemical features calculated for experimentally verified primary protein sequences were used to train the classifiers. The training features include amino acid composition; auto correlation coefficients; composition, transition, and distribution of various physicochemical properties; and pseudo amino acid composition. Performance analysis of predictive models based on 10 fold cross-validation and independent data testing revealed that the Random Forest based model was the best and achieved 86.11% overall accuracy and 86.22% balanced accuracy with a remarkably high area under the Receivers Operating Characteristic curve of 0.95 to predict viral RNA silencing suppressor proteins. The prediction models for plant viral RNA silencing suppressors can potentially aid identification of novel viral RNA silencing suppressors, which will provide valuable insights into the mechanism of RNA silencing and could be further explored as potential targets for designing novel antiviral therapeutics. Also, the key subset of identified optimal features may help in determining compositional patterns in the viral proteins which are important determinants for RNA silencing suppressor activities. The best prediction model developed in the study is available as a freely accessible web server pVsupPred at http://bioinfo.icgeb.res.in/pvsup/.

Dataset Information

Mobilization of a plant transposon by expression of the transposon-encoded anti-silencing factor.

Publications

Mobilization of a plant transposon by expression of the transposon-encoded anti-silencing factor.

Similar Datasets

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