In silico identification of sugarcane

• Published in: PloS one Vol. 17; no. 1; p. e0261807
• Main Authors: Ashraf, Muhammad Aleem, Feng, Xiaoyan, Hu, Xiaowen, Ashraf, Fakiha, Shen, Linbo, Iqbal, Muhammad Shahzad, Zhang, Shuzhen
• Format: Journal Article
• Language: English
• Published: Public Library of Science 20.01.2022
• Subjects: Diseases and pests; Genetic aspects; Identification and classification; MicroRNA; Open reading frames; Sugarcane; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0261807

Sugarcane bacilliform virus (SCBV) is considered one of the most economically damaging pathogens for sugarcane production worldwide. Three open reading frames (ORFs) are characterized in the circular, ds-DNA genome of the SCBV; these encode for a hypothetical protein (ORF1), a DNA binding protein (ORF2), and a polyprotein (ORF3). A comprehensive evaluation of sugarcane (Saccharum officinarum L.) miRNAs for the silencing of the SCBV genome using in silico algorithms were carried out in the present study using mature sugarcane miRNAs. miRNAs of sugarcane are retrieved from the miRBase database and assessed in terms of hybridization with the SCBV genome. A total of 14 potential candidate miRNAs from sugarcane were screened out by all used algorithms used for the silencing of SCBV. The consensus of three algorithms predicted the hybridization site of sof-miR159e at common locus 5534. miRNA-mRNA interactions were estimated by computing the free-energy of the miRNA-mRNA duplex using the RNAcofold algorithm. A regulatory network of predicted candidate miRNAs of sugarcane with SCBV-ORFs, generated using Circos-is used to identify novel targets. The predicted data provide useful information for the development of SCBV-resistant sugarcane plants.