Antibiotic resistant bacteria are an immediate threat to public health, animal husbandry and agriculture. Toxin-antitoxin (TA) systems in multidrug-resistant bacteria are promising approaches to combat them, but further research is needed to fully understand their distribution, functioning, regulation and evolution. TA systems are genetic modules consisting of a toxin and its antitoxin, which are proteins or rnas, and are involved in various cellular processes, including cell death and stress response. Eight different types, with type I and II being the most investigated, are known.
This project aims to computationally identify and characterize all TAS in all bacteria, archaea and phages from NCBI to gain more insights by using sequence similarity searches and hidden markov models and in the case of ncRNA component, we use RNAalifold1, Infernal2 and R-scape3. In contrast to a simple search, which is limited to direct sequence and structural similarity, our iterative approach allows us to identify more evolutionary divergence TA systems and enhances both sensitivity and accuracy. Furthermore, we intend to employ a single-plasmid-based approach to experimental verify novel TA systems of interest.
Through sequence alignment and secondary structure prediction, we identify conserved motifs and structural elements within these RNA components. Additionally, we explore potential RNA-RNA and protein-RNA interactions. Our findings provide insights into the structural and functional characteristics of these systems on a global level, paving the way for further experimental validation. We'll also expand the RFAM database to aid future research.
A deeper understanding of TA systems is an essential part of the search for alternatives to antibiotic treatment and future applications in medicine, animal husbandry, and agriculture. A comprehensive search shall reveal their whereabouts and appearance.
1 Bernhart, Stephan H., et al. https://doi.org/10.1186/1471-2105-9-474
2 Nawrocki, Eric P., and Sean R. Eddy. https://doi.org/10.1093/bioinformatics/btt509
3 Rivas, Elena, Jody Clements, and Sean R. Eddy. https://doi.org/10.1038/nmeth.4066