Stochastic gene expression in bacteria: effects of regulatory RNAs on population profiles
Zusammenfassung der Projektergebnisse
Bacteria have evolved sophisticated survival strategies and it is an exciting challenge to investigate the mechanisms behind. Several of these survival strategies involve phenotypic diversity which is generated by stochastic processes and depends on the underlying regulatory circuits. The question of how bacteria exploit regulatory RNAs to make decisions between phenotypes is central to a general understanding of these universal regulators. We investigated the TisB/IstR-1 toxin-antitoxin system of Escherichia coli to appreciate the role of the RNA antitoxin IstR-1 in TisB-dependent persister formation. Persisters are phenotypic variants that have become transiently drug-tolerant by arresting their growth. We could show that IstR-1 sets a threshold for TisB-dependent depolarization of the inner membrane, resulting in two sub-populations: polarized and depolarized cells. Furthermore, our data indicate that an inhibitory 5' UTR structure in the tisB mRNA serves as an emergency device to avoid spurious depolarization. Investigation of the persister sub-population further revealed that both regulatory RNA elements have an influence on both persister level and persistence time. This is an intriguing example of how bacteria can exploit regulatory RNAs to control phenotypic switching. The toxin TisB is induced under DNA-damaging conditions as part of the SOS response. We investigated early and late time-points of the response to DNA damage by RNA-seq which allowed us to determine complex expression patterns. In parallel, we established a new method for normalization of RNA-seq data to determine reliable changes in gene expression. The normalization scheme is based on finding invariant genes and applying them for a non-linear correction of read counts. The method is implemented as MOOSE2. Finally, we applied RNA-seq together with MOOSE2 to perform an in-depth target search for the two homologous sRNAs OmrA and OmrB. The data will be used to thoroughly define their regulons and to identify differential targets if existing. Some of the new targets might be involved in switching between phenotypes, such as transition from a motile to a sessile lifestyle.
