Analysis of a Library of Escherichia coli Transporter Knockout Strains to Identify Transport Pathways of Antibiotics

Abstract

Antibiotic resistance is a major global healthcare issue. Antibiotic compounds cross the bacterial cell membrane via membrane transporters, and a major mechanism of antibiotic resistance is through modification of the membrane transporters to increase the efflux or reduce the influx of antibiotics. Targeting these transporters is a potential avenue to combat antibiotic resistance. In this study, we used an automated screening pipeline to evaluate the growth of a library of 447 Escherichia coli transporter knockout strains exposed to sub-inhibitory concentrations of 18 diverse antimicrobials. We found numerous knockout strains that showed more resistant or sensitive phenotypes to specific antimicrobials, suggestive of transport pathways. We highlight several specific drug-transporter interactions that we identified and provide the full dataset, which will be a useful resource in further research on antimicrobial transport pathways. Overall, we determined that transporters are involved in modulating the efficacy of almost all the antimicrobial compounds tested and can, thus, play a major role in the development of antimicrobial resistance.

Keywords: Escherichia coli; antibiotics; transporters.

Figure 1

Histograms illustrating distributions of (A) growth rate and (B) area under the curve (AUC) for transporter library growth in LB in the absence of antibiotics. Scatter plots showing correlation between replicates for (C) growth rates and (D) AUC for transporter library growth in LB alone.

Figure 2

Transporter knockout library ordered by the mean area under the curve (AUC). Data shown for growth in LB (A) and subinhibitory concentrations of antibiotics/antimicrobials indicated (B–D). Y-genes are shown in blue, and annotated transporters are shown in red, while the arrow indicates the position of the WT strain.

Figure 3

Box and whisker plot showing distribution of mean area under the curve (AUC) for growth of the transporter library in LB and antimicrobial compounds.

Figure 4

Growth curves for WT (black) and ∆acrB (red) in LB (A), azithromycin, (B) D-cycloserine (C) and chloramphenicol (D). Shaded areas represent the standard deviation for the WT growth curve.

Figure 5

Growth curves for WT (black) and knockout strains (red) in 400 mg/L ornidazole. ∆nimT (A), ∆argO (B), ∆narU (C) and ∆ygaH (D) are shown. Shaded areas represent the standard deviation for the WT growth curve.

Figure 6

Growth curves for WT (black) and knockout strains (red) in 400 mg/L azithromycin. ∆betT (A), ∆tyrP(B), ∆yhdW (C) and ∆ydfJ (D) are shown. Shaded areas represent the standard deviation for the WT growth curve.

Figure 7

Growth features of paraquat. (A) Concentration inhibition data for growth of WT E. coli in the indicated concentration of paraquat. Shaded areas represent the standard deviation, n = 3. Growth curves for WT (black) and knockout strains (red) in 32 mg/L paraquat. ∆cusB (B) and ∆aroP (C) are shown.

Figure 8

(A). Heatmap showing correlations between area under the curve values for transporters in all antibiotics. Zoom inset highlights a cluster of highly correlated transporter knockout strains. (B) Histogram of p-values for all correlations showing the peak around 0, indicating likely true positive results. (C) Scatter plot indicating high degree of correlation between ∆sapB and ∆ydjE.