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

The ability in managing reactive oxygen species affects Escherichia coli persistence to ampicillin after nutrient shifts

Bacterial persistence profoundly impacts biofilms, infections, and antibiotic effectiveness. Persister formation can be substantially promoted by nutrient shift, which commonly exists in natural environments. However, mechanisms that promote persister formation remain poorly understood. Here, we investigated the persistence frequency of Escherichia coli after switching from various carbon sources to fatty acid and observed drastically different survival rates. While more than 99.9% of cells died during a 24-hour ampicillin (AMP) treatment after the glycerol to oleic acid (GLY → OA + AMP) shift, a surprising 56% of cells survived the same antibiotic treatment after the glucose to oleic acid (GLU → OOA + AMP) shift. Using a combination of single-cell imaging and time-lapse microscopy, we discovered that the induction of high levels of reactive oxygen species (ROS) by AMP is the primary mechanism of cell killing after switching from gluconeogenic carbons to OA + AMP. Moreover, the timing of the ROS burst is highly correlated (R2 = 0.91) with the start of the rapid killing phase in the time-kill curves for all gluconeogenic carbons. However, ROS did not accumulate to lethal levels after the GLU → OA + AMP shift. We also found that the overexpression of the oxidative stress regulator and ROS detoxification enzymes strongly affects the amounts of ROS and the persistence frequency following the nutritional shift. These findings elucidate the different persister frequencies resulting from various nutrient shifts and underscore the pivotal role of ROS. Our study provides insights into bacterial persistence mechanisms, holding promise for targeted therapeutic interventions combating bacterial resistance effectively.

IMPORTANCE

This research delves into the intriguing realm of bacterial persistence and its profound implications for biofilms, infections, and antibiotic efficacy. The study focuses on Escherichia coli and how the switch from different carbon sources to fatty acids influences the formation of persister-resilient bacterial cells resistant to antibiotics. The findings reveal a striking variation in survival rates, with a significant number of cells surviving ampicillin treatment after transitioning from glucose to oleic acid. The key revelation is the role of reactive oxygen species (ROS) in cell killing, particularly after switching from gluconeogenic carbons. The timing of ROS bursts aligns with the rapid killing phase, highlighting the critical impact of oxidative stress regulation on persistence frequency. This research provides valuable insights into bacterial persistence mechanisms, offering potential avenues for targeted therapeutic interventions to combat bacterial resistance effectively.

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.