Improved RNA stability estimation indicates that transcriptional interference is frequent in diverse bacteria

We used stochastic simulations and experimental data from E. coli, K. aerogenes, Synechococcus PCC 7002 and Synechocystis PCC 6803 to provide evidence that transcriptional interference via the collision mechanism is likely a prevalent mechanism for bacterial gene regulation. Rifampicin time-series data can be used to globally monitor and quantify collision between sense and antisense transcription-complexes. Our findings also highlight that transcriptional events, such as differential RNA decay, partial termination, and internal transcriptional start sites often deviate from gene annotations. Consequently, within a single gene annotation, there exist transcript segments with varying half-lives and transcriptional properties. To address these complexities, we introduce 'rifi', an R-package that analyzes transcriptomic data from rifampicin time series. 'rifi' employs a dynamic programming-based segmentation approach to identify individual transcripts, enabling accurate assessment of RNA stability and detection of diverse transcriptional events. RNA time-series data using rifampicin (an inhibitor of transcription initiation) give insight into transcriptional interference, e.g. by sense/asRNA complex collision, and other features of RNA. The R-package rifi analyses these complex data.

Automated summary

We provide evidence that transcriptional interference via the collision mechanism is a prevalent mechanism for bacterial gene regulation, based on stochastic simulations and experimental data. Rifampicin time-series data can be used to globally monitor and quantify collision between sense and antisense transcription-complexes. Our findings highlight the need to consider transcript segments with varying half-lives and transcriptional properties within a single gene annotation, and we introduce the 'rifi' R-package for accurately assessing RNA stability and detecting diverse transcriptional events.