COMPUTATIONAL PREDICTION OF MICRO RNAs IN HCV-3a GENOME

Hepatitis C is a life-threatening chronic liver disorder, caused by Hepatitis C Virus (HCV), responsible for widespread morbidity and mortality around the globe. Recent studies revealed that micro RNAs (miRNAs), a class of small non-coding regulatory RNAs, have key roles in tumorigenesis, apoptosis, cell cycle and immune function. It was previously assumed that the cytoplasmic RNA viruses do not encode miRNAs due to their nucleus inaccessibility. In this study, we investigated miRNAs encoding potential of HCV-3a through in-silico approaches. Initial searches through VMir software extracted 19 miRNAs precursors (pre-miRNAs) from viral genome. MiPred designated 14 hairpin sequences as real and remaining five as pseudo pre-miRNAs. After analyzing for secondary structures and thermal stability (Minimum Free Energy <= -25 kcal/mol), through RNAfold algorithm, the number of predicted pre-miRNAs was reduced to 13. Finally, 11 mature miRNAs were identified in HCV-3a genome through Bayes-SVM-MiRNA online web server v1.0. Blast analysis further revealed that two miRNA sequences are highly conserved in other HCV types and subtypes. We envision that this report will provide a future experimental framework for gaining further insight into human-HCV interaction and will contribute to develop new antiviral therapies for controlling the deadly hepatitis C disease.