Antibiotic export by efflux pumps affects growth of neighboring bacteria

Wen, Xian‐Huan; Langevin, Ariel M.; Dunlop, Mary J.

doi:10.1038/s41598-018-33275-4

Cited by 23 publications

(19 citation statements)

References 37 publications

(37 reference statements)

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“…This observation might be due to the active efflux pump that can be specific for ciprofloxacin as substrate or the relatively low MIC 0.1 values against ASST. This is in good agreement with the efflux-mediated resistance to fluoroquinolones in many bacterial populations [29,32]. Cephalothin showed a comparably high inoculum effect against ARST (Figure 5b).…”

Section: Discussionsupporting

confidence: 84%

Effects of Incubation Time and Inoculation Level on the Stabilities of Bacteriostatic and Bactericidal Antibiotics against Salmonella Typhimurium

2021

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This study was designed to evaluate the stability of chloramphenicol, erythromycin, tetracycline, cephalothin, ciprofloxacin, and tobramycin against antibiotic-sensitive Salmonella Typhimurium (ASST) and antibiotic-resistant S. Typhimurium (ARST) during the broth microdilution assay. The antimicrobial activity in association with antibiotic stability was measured by using antibiotic susceptibility, time-delayed inoculation, time-extended incubation, and inoculum effect assays. The loss of the antimicrobial activity of cephalothin against ASST exposed to 1 MIC was observed for the 10 h delayed inoculation. The antimicrobial activities of tetracycline and ciprofloxacin against ASST and ARST exposed to ½ MIC were significantly decreased after the 10 h delayed inoculation. All antibiotics used in this study, except for ciprofloxacin, showed the considerable losses of antimicrobial activities against ASST and ARST after 40 h of incubation at 37 °C when compared to the 20 h of incubation during AST. Compared to the standard inoculum level (6 log CFU/mL), the MIC0.1 values of bactericidal antibiotics, ciprofloxacin and tobramycin against ASST were increased by more than 4-fold at the high inoculum level of 9 log CFU/mL. This would provide practical information for better understanding the clinical efficacy of the currently used antibiotics by considering the antibiotic stability during incubation time at different inoculum levels.

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Section: Discussionsupporting

confidence: 84%

Effects of Incubation Time and Inoculation Level on the Stabilities of Bacteriostatic and Bactericidal Antibiotics against Salmonella Typhimurium

2021

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“…Moreover, the same experiment repeated with a strain of E . coli that expresses the non‐social AcrAB‐TolC export pump as a mechanism of antibiotic resistance (Wen et al ., ) revealed that MIC was independent of initial cell density (Fig. S1).…”

Section: Resultsmentioning

confidence: 87%

The public and private benefit of an impure public good determines the sensitivity of bacteria to population collapse in a snowdrift game

Smith

Doiron

Muzquiz

et al. 2019

Environmental Microbiology

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Summary When cooperation is critical for survival, cheating can lead to population collapse. One mechanism of cooperation that permits the coexistence of cooperators and cheaters is an impure public good, whose public benefits are shared, but with a private benefit retained by the cooperator. It has yet to be determined how the contributions of the public and private benefit affect population survival. Using simulations and experiments with β‐lactamase‐expressing bacteria, we found that for a given amount of public and private benefit, the population was most sensitive to collapse when initiated from an intermediate fraction of cooperators due to the near‐concurrent collapse of the cooperator and cheater populations. We found that increasing the ratio of public to private benefit increased sensitivity to collapse. A low ratio allowed cooperators to survive on their private benefit after the public benefit could not rescue the cheaters. A high ratio allowed the cheaters to survive to high concentrations of ampicillin due to the high public benefit. However, small increases in ampicillin caused a rapid decline in the entire population as the private benefit was insufficient to allow self‐rescue of the cooperators. Our findings have implications in the persistence of populations that rely on cooperation for survival.

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“…Two-dimensional configurations, ranging from microcolonies growing on agarose pads to microfluidic growth chambers, can be used to measure spatial dynamics of cell-to-cell interactions. Examples include quorum sensing [25] and the effect of efflux pumps on neighboring cells in the presence of antibiotics [26]. Segmenting and tracking cells in two dimensions are more challenging than for cells constrained within the mother machine.…”

Section: Introductionmentioning

confidence: 99%

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

O’Connor

Alnahhas²,

Lugagne³

et al. 2022

PLoS Comput Biol

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Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze images of single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats as inputs and the introduction of a Google Colab notebook so users can try the software without installing the code on their local machine. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.

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Antibiotic export by efflux pumps affects growth of neighboring bacteria

Cited by 23 publications

References 37 publications

Effects of Incubation Time and Inoculation Level on the Stabilities of Bacteriostatic and Bactericidal Antibiotics against Salmonella Typhimurium

Effects of Incubation Time and Inoculation Level on the Stabilities of Bacteriostatic and Bactericidal Antibiotics against Salmonella Typhimurium

The public and private benefit of an impure public good determines the sensitivity of bacteria to population collapse in a snowdrift game

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

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