Prediction and identification of novel sRNAs involved in Agrobacterium strains by integrated genome wide and transcriptome based methods

Abstract

sRNAs are a class of gene regulators in bacteria, playing a central role in its response to environmental changes. Bioinformatic prediction facilitates the identification of sRNAs expressed at different conditions. We propose a novel method of prediction of sRNAs from the genome of Agrobacterium based on PWM matrix of conditional sigma factors. sRNAs predicted from the genome are integrated with the virulence specific transcriptome data to identify putative sRNAs that are over expressed during Agrobacterial virulence induction. A total of 384 sRNAs are predicted from transcriptome data analysis of Agrobacterium fabrum and 100–500 sRNAs from the genome of different Agrobacterial strains. In order to refine our study, a final set of 10 novel sRNAs with best features across different replicons targeting virulence genes are experimentally identified using semi-quantitative PCR. Since Ti plasmid plays major role in virulence, out of 10 sRNAs across the replicons, 4 novel sRNAs differentially expressed under virulence induced and non-induced conditions are predicted to be present in the Ti plasmid T-DNA region flanking virulence related genes like agrocinopine synthase, indole 3-lactate synthase, mannopine synthase and tryptophan monooxygenase. Further, validating the function of these sRNAs in conferring virulence would be relevant to explore its role in Agrobacterium-mediated plant transformation.