Computational Biology and Machine Learning Approaches Identify Rubber Tree (Hevea brasiliensis Muell. Arg.) Genome Encoded MicroRNAs Targeting Rubber Tree Virus 1

文献类型： 外文期刊

第一作者： Ashraf, Muhammad Aleem

作者： Ashraf, Muhammad Aleem;Zou, Zhi;Ashraf, Muhammad Aleem;Tariq, Hafiza Kashaf;Hu, Xiao-Wen;Khan, Jallat

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关键词： rubber tree capillovirus 1; microRNAs; plant-virus interaction; RNAi; computational algorithms; gene silencing; minimum free energy

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.838； 五年影响因子：2.921 ）

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年卷期： 2022 年 12 卷 24 期

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收录情况： SCI

摘要： Tapping panel dryness (TPD), a complex physiological syndrome associated with the rubber tree (Hevea brasiliensis Muell. Arg.), causes cessation of latex drainage upon tapping and thus threatens rubber production. Rubber tree virus 1 (RTV1) is a novel positive-sense single-stranded RNA virus from the Betaflexiviridae (genus Capillovirus), which has been established to cause TPD. MicroRNAs (miRNAs) play an important role in the interplay between viruses and host cells. In this study, we identified the rubber tree genome-encoded miRNAs and their therapeutic targets against RTV1. We applied computational algorithms to predict target binding sites of rubber tree miRNAs potentially targeting RTV1 RNA genome. Mature rubber-tree miRNAs are retrieved from the miRBase database and are used for hybridization of the RTV1 genome. A total of eleven common rubber-tree miRNAs were identified based on consensus genomic positions. The consensus of four algorithms predicted the hybridization sites of the hbr-miR396a and hbr-miR398 at common genomic loci (6676 and 1840), respectively. A miRNA-regulatory network of rubber tree was constructed with the RTV1- ORFs using Circos, is illustrated to analyze therapeutic targets. Overall, this study provides the first computational evidence of the reliable miRNA-mRNA interaction between specific rubber tree miRNAs and RTV1 genomic RNA transcript. Therefore, the predicted data offer valuable evidence for the development of RTV1-resistant rubber tree in the future. Our work suggests that similar computational host miRNA prediction strategies are warranted for identification of the miRNA targets in the other viral genomes.

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