Transient Antibiotic Tolerance Triggered by Nutrient Shifts From Gluconeogenic Carbon Sources to Fatty Acid

Heterogeneous Phenotypic Responses of Antibiotic-Resistant Salmonella Typhimurium to Food Preservative-Related Stresses

This study was designed to evaluate the response of antibiotic-resistant Salmonella Typhimurium to food preservative-related stresses, such as lactic acid and sodium chloride (NaCl). S. Typhimurium cells were exposed to 1 µg/mL of ciprofloxacin (CIP), 0.2% lactic acid (LA), 6% NaCl, CIP followed by LA (CIP-LA), and CIP followed by NaCl (CIP-NaCl). The untreated S. Typhimurium cells were the control (CON). All treatments were as follows: CON, CIP, LA, NaCl, CIP-LA, and CIP-NaCl. The phenotypic heterogeneity was evaluated by measuring the antimicrobial susceptibility, bacterial fluctuation, cell injury, persistence, and cross-resistance. The CIP, CIP-LA, and CIP-NaCl groups were highly resistant to ciprofloxacin, showing MIC values of 0.70, 0.59, and 0.54 µg/mL, respectively, compared to the CON group (0.014 µg/mL). The susceptibility to lactic acid was not changed after exposure to NaCl, while that to NaCl was decreased after exposure to NaCl. The Eagle phenomenon was observed in the CIP, CIP-LA, and CIP-NaCl groups, showing Eagle effect concentrations (EECs) of more than 8 µg/mL. No changes in the MBCs of lactic acid and NaCl were observed in the CIP, LA, and CIP-LA groups, and the EECs of lactic acid and NaCl were not detected in all treatments. The bacterial fluctuation rates of the CIP-LA and CIP-NaCl groups were considerably increased to 33% and 41%, respectively, corresponding to the injured cell proportions of 82% and 89%. CIP-NaCl induced persister cells as high as 2 log cfu/mL. The LA and NaCl treatments decreased the fitness cost. The CIP-NaCl treatment showed positive cross-resistance to erythromycin (ERY) and tetracycline (TET), while the LA and NaCl treatments were collaterally susceptible to chloramphenicol (CHL), ciprofloxacin (CIP), piperacillin (PIP), and TET. The results provide new insight into the fate of antibiotic-resistant S. Typhimurium during food processing and preservation.

The Growth Dependent Design Constraints of Transcription-Factor-Based Metabolite Biosensors

Metabolite biosensors based on metabolite-responsive transcription factors are key synthetic biology components for sensing and precisely controlling cellular metabolism. Biosensors are often designed under laboratory conditions but are deployed in applications where cellular growth rate differs drastically from its initial characterization. Here we asked how growth rate impacts the minimum and maximum biosensor outputs and the dynamic range, which are key metrics of biosensor performance. Using LacI, TetR, and FadR-based biosensors in Escherichia coli as models, we find that the dynamic range of different biosensors have different growth rate dependencies. We developed a kinetic model to explore how tuning biosensor parameters impact the dynamic range growth rate dependence. Our modeling and experimental results revealed that the effects to dynamic range and its growth rate dependence are often coupled, and the metabolite transport mechanisms shape the dynamic range-growth rate response. This work provides a systematic understanding of biosensor performance under different growth rates, which will be useful for predicting biosensor behavior in broad synthetic biology and metabolic engineering applications.