Biocomputational identification and validation of novel microRNAs predicted from bubaline whole genome shotgun...

MicroRNAs (miRNAs) are small (19-25 base long), non-coding RNAs that regulate post-transcriptional gene expression by cleaving targeted mRNAs in several eukaryotes. The miRNAs play vital roles in multiple biological and metabolic processes, including developmental timing, signal transduction, cell maintenance and differentiation, diseases and cancers. Experimental identification of microRNAs is expensive and lab-intensive. Alternatively, computational approaches for predicting putative miRNAs from genomic or exomic sequences rely on features of miRNAs viz. secondary structures, sequence conservation, minimum free energy index (MFEI) etc. To date, not a single miRNA has been identified in bubaline (Bubalus bubalis), which is an economically important livestock. The present study aims at predicting the putative miRNAs of buffalo using comparative computational approach from buffalo whole genome shotgun sequencing data (INSDC: AWWX00000000.1). The sequences were blasted against the known mammalian miRNA. The obtained miRNAs were then passed through a series of filtration criteria to obtain the set of predicted (putative and novel) bubaline miRNA. Eight miRNAs were selected based on lowest E-value and validated by real time PCR (SYBR green chemistry) using RNU6 as endogenous control. The results from different trails of real time PCR shows that out of selected 8 miRNAs, only 2 (hsa-miR-1277-5p; bta-miR-2285b) are not expressed in bubaline PBMCs. The potential target genes based on their sequence complementarities were then predicted using miRanda. This work is the first report on prediction of bubaline miRNA from whole genome sequencing data followed by experimental validation. The finding could pave the way to future studies in economically important traits in buffalo.