Improved RNA stability estimation through Bayesian modeling reveals most Salmonella transcripts have subminute half-lives

Abstract

RNA decay is a crucial mechanism for regulating gene expression in response to environmental stresses. In bacteria, RNA-binding proteins (RBPs) are known to be involved in posttranscriptional regulation, but their global impact on RNA half-lives has not been extensively studied. To shed light on the role of the major RBPs ProQ and CspC/E in maintaining RNA stability, we performed RNA sequencing of Salmonella enterica over a time course following treatment with the transcription initiation inhibitor rifampicin (RIF-seq) in the presence and absence of these RBPs. We developed a hierarchical Bayesian model that corrects for confounding factors in rifampicin RNA stability assays and enables us to identify differentially decaying transcripts transcriptome-wide. Our analysis revealed that the median RNA half-life in Salmonella in early stationary phase is less than 1 min, a third of previous estimates. We found that over half of the 500 most long-lived transcripts are bound by at least one major RBP, suggesting a general role for RBPs in shaping the transcriptome. Integrating differential stability estimates with cross-linking and immunoprecipitation followed by RNA sequencing (CLIP-seq) revealed that approximately 30% of transcripts with ProQ binding sites and more than 40% with CspC/E binding sites in coding or 3′ untranslated regions decay differentially in the absence of the respective RBP. Analysis of differentially destabilized transcripts identified a role for ProQ in the oxidative stress response. Our findings provide insights into posttranscriptional regulation by ProQ and CspC/E, and the importance of RBPs in regulating gene expression.

BacSC: A general workflow for bacterial single-cell RNA sequencing data analysis

“…As scDEED is only available in R, the BacSC pipeline includes a Python implementation of the method. For every dataset, we considered all pairwise combinations of parameters: n neighbors : (10,15,20,25,30,35,40,45,50,60,70,80,90,100,150,200,250); min dist : (0.05, 0.1, 0.3, 0.5, 0.7).…”

“…Applying scRNA-seq technologies to bacteria has however proven to be challenging, e.g. due to low overall transcript abundance, the short half-life of bacterial mRNA, and difficulties in cell lysis due to sturdier cell walls [12][13][14][15]. Recently, multiple protocols have been developed that enable scRNA-seq of bacteria on larger scales by tackling these challenges in different ways [13,14,[16][17][18][19].…”