Back

Environmental reservoir of resistance genes for the last resort antibiotic Cefiderocol.

GSCHWIND, R.; Bonnet, M.; Abramova, A.; Jarquin Diaz, V. H.; Wenne, M.; Loeber, U.; Godron, N.; Kampouris, I. D.; Tskhay, F.; Naheed, F.; Debroucker, C.; Bui-Hai, M.; El Aiba, I.; Klumper, U.; Berendonk, T. U.; Forslund-Startceva, S. K.; Zahra, R.; Bengtsson-Palme, J.; RUPPE, E.

2025-05-26 microbiology
10.1101/2025.05.26.656069 bioRxiv
Show abstract

Antibiotic resistance poses a global public health threat. Cefiderocol, a recently introduced siderophore cephalosporin, employs a "Trojan Horse" mechanism by exploiting bacterial iron uptake systems for cell entry. Yet, resistant clinical isolates are already observed in clinics and resistance mechanisms are difficult to characterize. Here, we applied functional metagenomics to identify cefiderocol resistance genes. Functional metagenomic DNA libraries from diverse environmental samples collected across several countries were expressed in a cefiderocol-sensitive Escherichia coli host. This yielded four resistant clones with DNA originating from wastewater or freshwater DNA libraries. The identified antibiotic resistance genes (ARGs) causing an increase in cefiderocol minimum inhibitory concentrations encoded for beta-lactamases (VEB-3, OXA-372 homolog and YbxI homolog) and a partial penicillin binding protein homolog. Three of four shared closest homologs in pathogenic bacteria. One ARG was associated with a mobile genetic element and was broadly distributed across all wastewater samples from every country surveyed. This study underscores the critical importance of environmental surveillance for ARGs, particularly for novel agents like cefiderocol with limited understanding of resistance mechanisms.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.