→ Poster doi:10.7490/f1000research.1112721.1
- Devika Subramanian, Data mining and Text mining laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, Tamil Nadu, India
- Jeyakumar Natarajan, Data mining and Text mining laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, Tamil Nadu, India
The widespread emergence of antibiotic-resistant Staphylococcus aureus
is a major bottleneck in the development of novel treatments as the mechanisms triggering this phenomenon is largely unknown. The role of small-RNAs in regulating bacterial response to environmental stresses including antibiotic exposures is now recognized. However, the functions of a majority of them are still unknown. Here, a collection of RNA-seq expression profiles were analyzed to reconstruct a model of vancomycin-resistome interactions which was then used to predict the functions of sRNA241, a small-noncoding RNA which was consistently downregulated upon antibiotic exposures. The state-of-the-art tools Bowtie, Stringtie (ran from galaxy webserver) and Ballgown were used to align, assemble and identify the differentially expressed mRNAs that could be responsible for the development of resistance mechanisms. Based on this, an mRNA repertoire encompassing the major resistome components were identified and used to reconstruct a vancomycin-resistome network with 308 nodes and 2477 interactions. Clustering and enrichment analyses of the network indicate that a variety of gene clusters representing various metabolic pathways and defense mechanisms mediate the resistance to vancomycin. Subsequently, the resistome network was used to examine the functions of sRNA241. Predicted targets of the sRNA were refined by opposite expression pairing and a functional subnetwork of the resistome consisting of the specific sRNA-mRNA interactions were identified. Enrichment analysis of the subnetwork indicates the regulation of different metabolic pathways including quinone/menaquinone biosynthetic pathways by sRNA241. Thus the resistome network model is a good platform to expand the knowledge on cellular interactions behind antibiotic resistance.