Evaluating the predictive power of combined gene expression dynamics from single cells on antibiotic survival.

Heteroresistance and persistence are examples of mechanisms that can allow otherwise drug-susceptible bacteria to survive and resume growth after antibiotic exposure. These temporary forms of antibiotic tolerance can be caused by the upregulation of stress response genes or a decrease in cell growth rate. However, it is not clear how the expression of multiple genes contributes to tolerance phenotypes. Using fluorescent reporters for stress-related genes, we conducted real-time measurements of expression prior to, during, and after antibiotic exposure. We first identified relationships between growth rate and reporter levels based on auto- and cross-correlation analysis, revealing consistent patterns where changes in growth rate were anticorrelated with fluorescence following a delay. We then used pairs of stress gene reporters and time-lapse fluorescence microscopy to measure the growth rate and reporter levels in cells that survived or died following antibiotic exposure. Using these data, we asked whether combined information about reporter expression and growth rate could improve our ability to predict whether a cell would survive or die following antibiotic exposure. We developed a Bayesian inference model to predict how the combination of dual reporter expression levels and growth rate impacts ciprofloxacin survival in