Stochastic gene expression drives state switching and phenotypic heterogeneity in RNA repair

Bacterial populations often exhibit variability in cellular responses to antibiotics which can affect treatment success and survival outcomes. This heterogeneity suggests that genetically identical cells can occupy different states of susceptibility within the same population. Better understanding of these dynamic responses is essential for determining how cells respond to stress and survive antibiotic-induced damage. In this work, we investigate heterogeneity in antibiotic responses driven by the Rtc RNA repair system in E. coli. We develop a stochastic model of Rtc-mediated RNA repair to identify the drivers of heterogeneity at the single-cell level. We observe bimodality in cellular translational capacity and a transient memory in predicted responses to RNA damage, demonstrating how intrinsic stochasticity in Rtc-regulation can lead to phenotypic heterogeneity. Classifying single-cell trajectories, we predict drivers of state switching between the resistant and susceptible states, finding that stochastic expression of the rtc regulator determines entry into a resistant cell state, while dilution drives the system back into a susceptible state. These results indicate that heterogeneity in RNA repair emerges from stochastic switching between states, offering a route to understanding how individual cells survive in the presence of translation-targeting antibiotics.