High-throughput sequencing reveals small RNAs involved in ASGV infection

Visser, Marike ; Maree, Hans J. ; Rees, D. Jasper G. ; Burger, Johan T. (2014-07)

CITATION: Visser, M., Maree, H. J., Rees, D. J. G. & Burger, J. T. 2014. High-throughput sequencing reveals small RNAs involved in ASGV infection. BMC Genomics, 15(1):568, doi:10.1186/1471-2164-15-568.

The original publication is available at http://www.biomedcentral.com/1471-2164/15/568

Publication of this article was funded by the Stellenbosch University Open Access Fund.

Article

Background Plant small RNAs (sRNAs) associated with virulent virus infections have been reported by previous studies, while the involvement of sRNAs in latent virus infection remains largely uncharacterised. Apple trees show a high degree of resistance and tolerance to viral infections. We analysed two sRNA deep sequencing datasets, prepared from different RNA size fractions, to identify sRNAs involved in Apple stem grooving virus (ASGV) infection. Results sRNA analysis revealed virus-derived siRNAs (vsiRNAs) originating from two ASGV genetic variants. A vsiRNA profile for one of the ASGV variants was also generated showing an increase in siRNA production towards the 3′ end of the virus genome. Virus-derived sRNAs longer than those previously analysed were also observed in the sequencing data. Additionally, tRNA-derived sRNAs were identified and characterised. These sRNAs covered a broad size-range and originated from both ends of the mature tRNAs as well as from their central regions. Several tRNA-derived sRNAs showed differential regulation due to ASGV infection. No changes in microRNA, natural-antisense transcript siRNA, phased-siRNA and repeat-associated siRNA levels were observed. Conclusions This study is the first report on the apple sRNA-response to virus infection. The results revealed the vsiRNAs profile of an ASGV variant, as well as the alteration of the tRNA-derived sRNA profile in response to latent virus infection. It also highlights the importance of library preparation in the interpretation of high-throughput sequencing data.

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