Back

Phage resistance impacts antibiotic susceptibility and virulence in Staphylococcus aureus

Bowring, J. Z.; Mikkelsen, F. C.; Haider, R. M.; Lehmann, E.; Frisch, T.; Kjos, M.; van Sorge, N. M.; Ingmer, H.

2025-09-02 microbiology
10.1101/2025.09.02.673687 bioRxiv
Show abstract

As antibiotic resistance continues to rise worldwide, phage therapy offers a promising solution by harnessing viruses that infect and kill bacteria. However, phage-resistance may develop, compromising therapy. Phage-resistance has primarily been linked to changes in major phage receptors that for the human pathogen Staphylococcus aureus are the cell-wall linked wall teichoic acids (WTA). To identify additional factors contributing to phage-resistance, S. aureus was evolved under selective pressure from lytic phages, including candidates from therapeutic phage cocktails, to obtain resistant clones. A third of the phage-resistant clones acquired mutations associated with cell-wall changes previously linked with phage resistance, namely in femA, involved in peptidoglycan crossbridge formation, and tagO, encoding the initiator of the WTA biosynthesis. The remaining clones had mutations in pathways not previously associated with phage-resistance, including nucleoside catabolism (deoC1), polyamine import (potAB) and DNA/RNA replication (cshA, ligA), suggesting that phage-resistance may be associated with phage-driven host takeover. Antibiotic susceptibility and virulence were diversely affected by the mutations; mutations in tagO increased {beta}-lactams sensitivity and attenuated virulence, whereas mutations in femA/ligA or cshA increased staphylococcal virulence in Galleria mellonella. This work demonstrates the involvement of several novel bacterial pathways in phage resistance, highlighting the complexity behind phage-bacterial interactions.

Matching journals

The top 5 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.