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

Abstract

Heteroresistance can allow otherwise drug-susceptible bacteria to survive and resume growth after antibiotic exposure. This temporary form 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 expression of multiple genes contributes to the tolerance phenotype. By 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 microcopy 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 impact ciprofloxacin survival in Escherichia coli. We found clear evidence of the impact of growth rate and the gadX promoter activity on survival. Unexpectedly, our results also revealed examples where additional information from multiple genes decreased prediction accuracy, highlighting an important and underappreciated effect that can occur when integrating data from multiple simultaneous measurements.