Potential targets for evaluation of sugarcane yellow leaf virus resistance in sugarcane cultivars: in silico sugarcane miRNA and target network prediction. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Ashraf, Muhammad Aleem; Ashraf, Fakiha; Feng, Xiaoyan; Hu, Xiaowen; Shen, Linbo; Khan, Jallat; Zhang, Shuzhen
• Is Part Of: Biotechnology, biotechnological equipment. Volume 35:Issue 1(2021)
• Page Start: 1980
• Page End: 1991
• Publication Date: 2021-01-01
• Subjects: Host–virus interaction -- RNA interference -- Saccharum officinarum -- sugarcane yellow leaf virus -- target prediction; Biotechnology -- Periodicals; Biotechnology -- Periodicals; Biotechnology -- instrumentation -- Periodicals; Periodicals; 660.605
• DOI: 10.1080/13102818.2022.2041483
• Languages: English
• ISSNs: 1310-2818

Abstract

Abstract: The Sugarcane yellow leaf virus (SCYLV) is associated with sugarcane yellow leaf disease (SCYLD) and is considered to be the most economically deleterious emerging pathogen that represents a potential threat and danger to sugarcane cultivation in China. Over the last two decades, high genetic diversity in the SCYLV genotypes was observed worldwide, with a greater chance of YLD incidence for sugarcane injury. SCYLV infection has significantly damaged its economic traits and is responsible for substantial losses in biomass production in sugarcane cultivars. This study aims to identify and analyse sugarcane microRNAs (miRNAs) as therapeutic targets against SCYLV using plant miRNA prediction tools. Mature sugarcane miRNAs are retrieved and are used for hybridisation of the SCYLV. A total of seven common sugarcane miRNAs were selected based on consensus genomic positions. The biologically significant, top ranked ssp-miR528 was consensually predicted to have a potentially unique hybridisation site at nucleotide position 4162 for targeting the ORF5 of the SCYLV genome; this was predicted by all the algorithms used in this study. Then, the miRNA–mRNA regulatory network was generated using the Circos algorithm, which was used to predict novel targets. There are no acceptable commercial SCYLV-resistant sugarcane varieties available at present. Therefore, the predicted biological data offer valuable evidence for the generation of SCYLV-resistant sugarcane plants.