Target prediction of potential candidate miRNAs from Oryza sativa to silence the Pyricularia oryzae genome in rice blast

Abstract

Abstract Rice (Oryza sativa) is a staple food for billions of people across the globe, that feeds nearly three-quarters of the human population on Earth, particularly in Asian countries. Rice yield has been drastically reduced and severely affected by various biotic and abiotic stresses, especially pathogens. Controlling the attack of such pathogens is a matter of immediate concern as yield losses in rice crops could deprive millions of lives of nourishment worldwide. Pyricularia oryzae is one such pathogen that has been considered the major disease of rice because of its worldwide geographic distribution. P. oryzae belongs to the kingdom fungi, that causes rice blast ultimately adversely affecting the yield of the rice crop. Keeping in view this alarming scenario, the present study was designed so that the identifications of genome-encoded miRNAs of Oryza sativa were employed to target and silence the genome of P. oryzae. This study accomplished the computational analysis of algorithms related to miRNA target prediction. Four computational target prediction algorithms i.e., psRNATarget, RNA22, miRanda, and RNAhybrid were utilized in this investigation. The consensus among target prediction algorithms was created to discover six miRNAs from the O. sativa genome with the conservation of the target site fully evaluated on the genome of P. oryzae. The discovery of these novel six miRNAs in Oryza sativa paved a strong way toward the control of this disease in rice. It will open doors for further research in the field of gene silencing in rice. These miRNAs can be designed and employed in the future as experimentation to create constructs regarding the silencing of P. oryzae in rice crops. In the future, this research would be surely helpful for the development of P. oryzae resistant rice varieties.

Keywords

• Pyricularia oryzae
• miRNA
• Oryza sativa
• Target prediction
• Computational