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. 2024 Jun 30;8(5):735-747.
doi: 10.1093/evlett/qrae030. eCollection 2024 Sep.

Antibiotic resistance alters the ability of Pseudomonas aeruginosa to invade bacteria from the respiratory microbiome

Selina Lindon  1 , Sarah Shah  1 , Danna R Gifford  2 , Cédric Lood  1 , Maria A Gomis Font  3 , Divjot Kaur  1 , Antonio Oliver  3 , R Craig MacLean  1 , Rachel M Wheatley  1   4
Affiliations

Antibiotic resistance alters the ability of Pseudomonas aeruginosa to invade bacteria from the respiratory microbiome

Selina Lindon et al. Evol Lett. .

Abstract

The emergence and spread of antibiotic resistance in bacterial pathogens is a global health threat. One important unanswered question is how antibiotic resistance influences the ability of a pathogen to invade the host-associated microbiome. Here we investigate how antibiotic resistance impacts the ability of a bacterial pathogen to invade bacteria from the microbiome, using the opportunistic bacterial pathogen Pseudomonas aeruginosa and the respiratory microbiome as our model system. We measure the ability of P. aeruginosa spontaneous antibiotic-resistant mutants to invade pre-established cultures of commensal respiratory microbes in an assay that allows us to link specific resistance mutations with changes in invasion ability. While commensal respiratory microbes tend to provide some degree of resistance to P. aeruginosa invasion, antibiotic resistance is a double-edged sword that can either help or hinder the ability of P. aeruginosa to invade. The directionality of this help or hindrance depends on both P. aeruginosa genotype and respiratory microbe identity. Specific resistance mutations in genes involved in multidrug efflux pump regulation are shown to facilitate the invasion of P. aeruginosa into Staphylococcus lugdunensis, yet impair invasion into Rothia mucilaginosa and Staphylococcus epidermidis. Streptococcus species provide the strongest resistance to P. aeruginosa invasion, and this is maintained regardless of antibiotic resistance genotype. Our study demonstrates how the cost of mutations that provide enhanced antibiotic resistance in P. aeruginosa can crucially depend on community context. We suggest that attempts to manipulate the microbiome should focus on promoting the growth of commensals that can increase the fitness costs associated with antibiotic resistance and provide robust inhibition of both wildtype and antibiotic-resistant pathogen strains.

Keywords: Pseudomonas aeruginosa; antibiotic resistance; microbial interactions; microbiome.

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Figures

Figure 1.
Figure 1.
Schematic for selection of P. aeruginosa antibiotic-resistant mutants, subsequent strain characterization (via genome sequencing and resistance phenotyping), and use of strains in invasion assays.
Figure 2.
Figure 2.
Growth rate assays of the ceftazidime-, ciprofloxacin-, and meropenem-resistant strains compared to PAO1-GFP in tryptic soy broth in the absence of respiratory microbes. Growth was measured as relative fluorescence units (RFU) in the GFP channel over a period of 24 hr, and data were plotted as mean (three biological replicates with two technical replicates for each resistance phenotype) and standard error of the mean (shaded area) using the Growthcurver package in R (Sprouffske & Wagner, 2016).
Figure 3.
Figure 3.
Schematic of invasion assay method (A) and invasion of P. aeruginosa into respiratory microbiome strains in 24 hr (B, C, D, F, G, and H) and 36 hr (E). Each bar chart shows invasion ability measured as percentage RFU compared to the media control at that same timepoint (y-axis), for invasion into pre-established cultures of: (B) R. mucilaginosa, (C) S. epidermidis, (D) S. lugdunensis (24 hr), (E) S. lugdunensis (36 hr), (F) S. gordonii, (G) S. salivarius, and (H) S. oralis. Values obtained in S. gordonii, S. salivarius, and S. oralis were considered negligible and below the limits of the assay (below 2%). Differences in invasion ability between the P. aeruginosa antibiotic-resistant strains and PAO1-GFP were assessed using a one-way ANOVA followed by a post hoc Dunnett’s test, and significance (Dunnett’s test: p < 0.05) is indicated by an asterisk (*). Bar charts show the mean of six biological replicates ± standard error of the mean.
Figure 4.
Figure 4.
(A) Schematic of S. lugdunensis spent media assay. (B) Growth of PAO1-GFP and clinical P. aeruginosa isolates in S. lugdunensis spent media, measured as percentage OD595 in S. lugdunensis spent media compared to the media control at 24 hr. For each clinical P. aeruginosa genotype, three biological replicates of three isolates were measured and the values are plotted as mean ± standard error of the mean. For PAO1-GFP, three biological replicates were measured. One tailed unpaired t-tests were carried out to compare growth in S. lugdunensis spent media between PAO1-GFP and the two clinical WT strains (ST782-WT, ST17-WT) and to compare between each resistant mutant to its WT background. Significance as p < 0.05 is indicated by an asterisk (*).
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