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Dryad

The effects of antibiotic combination treatments on Pseudomonas aeruginosa tolerance evolution and coexistence with Stenotrophomonas maltophilia

Cite this dataset

Law, Jack P.; Wood, A. Jamie; Friman, Ville-Petri (2022). The effects of antibiotic combination treatments on Pseudomonas aeruginosa tolerance evolution and coexistence with Stenotrophomonas maltophilia [Dataset]. Dryad. https://doi.org/10.5061/dryad.83bk3j9tn

Abstract

Pseudomonas aeruginosa bacterium is a common pathogen of Cystic Fibrosis (CF) patients due to its ability to evolve resistance to antibiotics during treatments. While P. aeruginosa resistance evolution is well characterised in monocultures, it is less well understood in polymicrobial CF infections. Here, we investigated how exposure to ciprofloxacin, colistin, or tobramycin antibiotics, administered at sub-MIC doses alone and in combination, shaped the tolerance evolution of P. aeruginosa (PAO1 lab and clinical CF LESB58 strains) in the absence and presence of a commonly co-occurring species, Stenotrophomonas maltophilia. Increases in antibiotic tolerances were primarily driven by the presence of that antibiotic in the treatment. We observed a reciprocal cross-tolerance between ciprofloxacin and tobramycin, and when combined these antibiotics selected increased MICs for all antibiotics. Though the presence of S. maltophilia did not affect the tolerance or the MIC evolution, it drove P. aeruginosa into extinction more frequently in the presence of tobramycin due to its relatively greater innate tobramycin tolerance. In contrast, P. aeruginosa dominated and drove S. maltophilia extinct in most other treatments. Together, our findings suggest that besides driving high-level antibiotic tolerance evolution, sub-MIC antibiotic exposure can alter competitive bacterial interactions, leading to target pathogen extinctions in multi-species communities.

Methods

See Methods in paper for collection. MIC values (MIC data.csv) derived from visual inspection of antibiotic growth measurements plotted as a growth curve.

Optical density data has been blank-corrected, and in the case of the antibiotic growth measurements (Antibiotic growth data.csv) averaged over ≤3 technical replicates.

Funding

University of York