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. 2022 Apr 25:13:845173.
doi: 10.3389/fmicb.2022.845173. eCollection 2022.

Enhanced Biosynthesis of Fatty Acids Contributes to Ciprofloxacin Resistance in Pseudomonas aeruginosa

Yu-Bin Su  1   2 , Xi-Kang Tang  1 , Ling-Ping Zhu  1 , Ke-Xin Yang  1 , Li Pan  1 , Hui Li  1 , Zhuang-Gui Chen  1
Affiliations

Enhanced Biosynthesis of Fatty Acids Contributes to Ciprofloxacin Resistance in Pseudomonas aeruginosa

Yu-Bin Su et al. Front Microbiol. .

Abstract

Antibiotic-resistant Pseudomonas aeruginosa is insensitive to antibiotics and difficult to deal with. An understanding of the resistance mechanisms is required for the control of the pathogen. In this study, gas chromatography-mass spectrometer (GC-MS)-based metabolomics was performed to identify differential metabolomes in ciprofloxacin (CIP)-resistant P. aeruginosa strains that originated from P. aeruginosa ATCC 27853 and had minimum inhibitory concentrations (MICs) that were 16-, 64-, and 128-fold (PA-R16CIP, PA-R64CIP, and PA-R128CIP, respectively) higher than the original value, compared to CIP-sensitive P. aeruginosa (PA-S). Upregulation of fatty acid biosynthesis forms a characteristic feature of the CIP-resistant metabolomes and fatty acid metabolome, which was supported by elevated gene expression and enzymatic activity in the metabolic pathway. The fatty acid synthase inhibitor triclosan potentiates CIP to kill PA-R128CIP and clinically multidrug-resistant P. aeruginosa strains. The potentiated killing was companied with reduced gene expression and enzymatic activity and the returned abundance of fatty acids in the metabolic pathway. Consistently, membrane permeability was reduced in the PA-R and clinically multidrug-resistant P. aeruginosa strains, which were reverted by triclosan. Triclosan also stimulated the uptake of CIP. These findings highlight the importance of the elevated biosynthesis of fatty acids in the CIP resistance of P. aeruginosa and provide a target pathway for combating CIP-resistant P. aeruginosa.

Keywords: Pseudomonas aeruginosa; antibiotic resistance; biosynthesis of fatty acids; ciprofloxacin; membrane permeability; metabolomics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Minimum inhibitory concentration (MIC) and metabolic profiles of PA-S and three PA-R strains. (A) MIC of seven P. aeruginosa strains to ciprofloxacin. (B) Correlation coefficient of two technical replications. The Pearson correlation coefficient between technical replicates varies between 0.996 and 0.999. (C) Heat map of the differential abundance of metabolites (row). Blue and yellow indicated a decrease and increase of the metabolites scaled to mean and standard deviation of row metabolite level, respectively (refer to color scale). (D) Scores plot of OPLS-DA model between PA-S and PA-R. Each dot represented the technical replicate analysis of samples in the plot. (E) Percentage of metabolites in every category. Sixty-five metabolites were searched against KEGG for their categories.
Figure 2
Figure 2
Analysis of differential abundance metabolites of P. aeruginosa. (A) Differential metabolic profiles in PA-R16CIP, PA-R64CIP, and PA-R128CIP were compared with PA-S16, PA-S64, and PA-S128 mixed together. (B) Z-score plots. PA-R16CIP, PA-R64CIP, and PA-R128CIP were compared with PA-S16, PA-S64, and PA-S128, respectively. (C) Venn diagram of the total differential metabolites between PA-R16CIP, PA-R64CIP, and PA-R128CIP with their control group. (D) The number of differential abundant metabolites from PA-R16CIP, PA-R64CIP, and PA-R128CIP compared with their control group in every category.
Figure 3
Figure 3
Analysis of pathway enrichment in ciprofloxacin-resistant P. aeruginosa. (A) Pathway enrichment of differential abundant metabolites. (B) Integrative analysis of differential abundant metabolites in enriched pathways. Blue and yellow represented a decrease and increase in the abundance of metabolites, respectively. (C) MetaMapp visualization of metabolomic data highlighting the differential metabolic regulation from PA-R16CIP, PA-R64CIP, and PA-R128CIP was compared with PA-S16, PA-S64, and PA-S128, respectively. Yellow, increased abundance; Blue, decreased abundance; Black, no difference; Weak black, not detected.
Figure 4
Figure 4
Analysis of potential biomarkers of ciprofloxacin-resistant P. aeruginosa. (A) Principal component analysis of P. aeruginosa. (B) S-plot generated from OPLS-DA. Triangle represents individual metabolite, where potential biomarkers are highlighted with red, which was greater or equal to 0.05 and 0.5 for absolute value of covariance p and correlation p (corr), respectively. (C) Scatter plot of key metabolite abundance. Results are displayed as mean ± SEM, and significant differences are identified (*p < 0.05, **p < 0.01) as determined by two-tailed Student's t-test.
Figure 5
Figure 5
Analysis of fatty acid biosynthesis and degradation in AP-RCIP. (A) qRT-PCR for the expression of genes encoding fatty acid biosynthesis in PA-S0, PA-R16CIP, PA-R64CIP, and PA-R128CIP. (B) Outline for expression of genes in fatty acid metabolism pathway. Red: increase, green: decrease. (C) qRT-PCR for the expression of genes encoding fatty acid degradation in PA-R16CIP, PA-R64CIP, and PA-R128CIP. (D) Activity of acetyl-coenzyme-A (CoA) carboxylase (ACC) in PA-R16CIP, PA-R64CIP, and PA-R128CIP. (E) Profile of fatty acid metabolome in PA-R128CIP. (F) Z-score plot of differential fatty acids in PA-R128CIP. (G) Number of differential saturated and unsaturated fatty acids in PA-R128CIP. (H) S-plot generated from OPLS-DA based on the differential fatty acids in data (F). Triangle represents individual metabolite, where potential biomarkers are highlighted with red, which is greater or equal to 0.05 and 0.5 for the absolute value of covariance p and correlation p (corr), respectively. Results are displayed as mean ± SEM and at least three biological repeats are performed. Significant differences are identified *p < 0.05, **p < 0.01.
Figure 6
Figure 6
Viability of PA-R128CIP and clinically multidrug-resistant P. aeruginosa in the presence of antibiotics or/and triclosan. (A,B) Percent survival of PA-R128CIP in the indicated concentrations of aminooxazole or triclosan plus ciprofloxacin (0.25 μg/ml). (C) Percent survival of PA-R128CIP in the indicated concentrations of ciprofloxacin plus triclosan (1 μg/ml). (D) Percent survival of PA-R128CIP in the indicated incubation periods and in the presence of ciprofloxacin (0.25 μg/ml) plus triclosan (1 μg/ml). (E) MIC of clinical P. aeruginosa. (F) Percent survival of clinically multidrug-resistant P. aeruginosa in the presence of ciprofloxacin (0.025 μg/ml) or/and triclosan (1μg/ml). (G–I) Percent survival of PA-R128CIP (G), A2 (H), and B2 (I) in the presence of different antibiotics or/and triclosan (1 μg/ml). Results are displayed as mean ± SEM and three biological repeats are performed. Significant differences are identified *p < 0.05, **p < 0.01.
Figure 7
Figure 7
The effect of triclosan on fatty acid metabolism. (A) Z-score plot of differential fatty acids in the presence of triclosan (1 μg/ml) in PA-R128CIP. (B) Number of differential saturated and unsaturated fatty acids between PA-R128CIP and PA-R128CIP plus triclosan (1 μg/ml). (C) S-plot generated from OPLS-DA based on the differential fatty acids in data (A). Triangle represents individual metabolite, where potential biomarkers are highlighted with red, which is greater or equal to 0.05 and 0.5 for absolute value of covariance p and correlation p (corr), respectively. (D) Comparison for differential abundance of fatty acids in data (C). Results are displayed as mean ± SEM and three biological repeats are performed. Significant differences are identified *p < 0.05, **p < 0.01.
Figure 8
Figure 8
Membrane permeability among P. aeruginosa and survival rate of lysate against E. coli. (A) Green fluorescence signal intensity and single parameter histogram among P. aeruginosa. (B,C) Green fluorescence signal intensity in the presence of triclosan and single parameter histogram. The left shift of fluorescence peak diagram indicates the decrease of membrane permeability, the right shift of peak diagram indicates the increase of membrane permeability, and the MFI indicates the fluorescence signal intensity. (D) Effects of P. aeruginosa lysate on E. coli K12BW25113 survival. Results are shown as mean ± SEM and at least three biological repeats are performed. Significant differences are identified *p < 0.05, **p < 0.01.
Figure 9
Figure 9
Model showing the proposed metabolic regulation for ciprofloxacin resistance.
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