[Tracking transfers of resistance-carrying bacteria between animals, humans and the environment]. / Tracking des transferts des bactéries porteuses de résistances entre animal, homme et environnement.

The fight against antibiotic resistance must incorporate the "One Health" concept to be effective. This means having a holistic approach embracing the different ecosystems, human, animal, and environment. Transfers of resistance genes may exist between these three domains and different stresses related to the exposome may influence these transfers. Various targeted or pan-genomic molecular biology techniques can be used to better characterise the dissemination of bacterial clones and to identify exchanges of genes and mobile genetic elements between ecosystems.

La lutte contre la résistance aux antibiotiques doit intégrer le concept «  Une seule santé  ¼ pour être efficace. Ceci consiste à avoir une approche holistique embrassant les différents écosystèmes, homme, animal et environnement. Des transferts de gènes de résistance peuvent exister entre ces trois domaines et différents stress liés à l'exposome peuvent influencer ces transferts. Différentes techniques de biologie moléculaire ciblées ou pan-génomiques peuvent être mises en œuvre pour mieux caractériser les circulations de clones bactériens mais aussi pour identifier les échanges de gènes et d'éléments génétiques mobiles entre écosystèmes.

Characterization of Pseudomonas aeruginosa resistance to ceftolozane-tazobactam due to ampC and/or ampD mutations observed during treatment using semi-mechanistic PKPD modeling.

A double ampC (AmpCG183D) and ampD (AmpDH157Y) genes mutations have been identified by whole genome sequencing in a Pseudomonas aeruginosa (PaS) that became resistant (PaR) in a patient treated by ceftolozane/tazobactam (C/T). To precisely characterize the respective contributions of these mutations on the decreased susceptibility to C/T and on the parallel increased susceptibility to imipenem (IMI), mutants were generated by homologous recombination in PAO1 reference strain (PAO1- AmpCG183D, PAO1-AmpDH157Y, PAO1-AmpCG183D/AmpDH157Y) and in PaR (PaR-AmpCPaS/AmpDPaS). Sequential time-kill curve experiments were conducted on all strains and analyzed by semi-mechanistic PKPD modeling. A PKPD model with adaptation successfully described the data, allowing discrimination between initial and time-related (adaptive resistance) effects of mutations. With PAO1 and mutant-derived strains, initial EC50 values increased by 1.4, 4.1, and 29-fold after AmpCG183D , AmpDH157Y and AmpCG183D/AmpDH157Y mutations, respectively. EC50 values were increased by 320, 12.4, and 55-fold at the end of the 2 nd experiment. EC50 of PAO1-AmpCG183D/AmpDH157Y was higher than that of single mutants at any time of the experiments. Within the PaR clinical background, reversal of AmpCG183D, and AmpDH157Y mutations led to an important decrease of EC50 value, from 80.5 mg/L to 6.77 mg/L for PaR and PaR-AmpCPaS/AmpDPaS, respectively. The effect of mutations on IMI susceptibility mainly showed that the AmpCG183D mutation prevented the emergence of adaptive resistance. The model successfully described the separate and combined effect of AmpCG183D and AmpDH157Y mutations against C/T and IMI, allowing discrimination and quantification of the initial and time-related effects of mutations. This method could be reproduced in clinical strains to decipher complex resistance mechanisms.