The Journal of antimicrobial chemotherapy · 2026
Identification of antimicrobial resistance genes in Escherichia coli through network diffusion.
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Mansouri Anis, Durazzi Francesco, Ihsan Muhammad Ahmed, Griffin Sholeem, Manfreda Gerardo, Valdramidis Vasilis P, Pasquali Frédérique, Remondini Daniel
Аннотация
OBJECTIVES: Antimicrobial resistance (AMR) is an escalating global health concern, driven by multifactorial biological processes not yet fully understood. This study employed network diffusion analysis to dissect the molecular mechanisms driving AMR in Escherichia coli, aiming to identify novel potential drug targets for therapeutic development.
METHODS: A systems biology approach was used to identify genes and biological pathways associated with AMR, by mapping known AMR-related genes from the Comprehensive Antibiotic Resistance Database (CARD) and PointFinder database into the E. coli protein interactome. Through a network diffusion algorithm, several network modules were identified, i.e. genes and pathways, in part already known to be involved in AMR mechanisms. We selected gene candidates for performing an in vitro susceptibility validation test, consisting of 13 knockout mutants against nine different antibiotics.
RESULTS: Compared with the WT E. coli BW25113, the AMR of some mutants showed significant shifts of biological relevance: ΔuhpB (S/I) and ΔmdaB (S/R) against ampicillin, ΔrpmG (I/S) and ΔrplA (I/S) against ciprofloxacin, and ΔrplA (I/S) against streptomycin (S, susceptible; I, intermediate; R, resistant). In other cases, only a significant change in inhibition disc diameter was observed, probably deserving further studies.
CONCLUSIONS: Network diffusion is an effective tool to infer relevant biological insights related to AMR from microbial biological networks. Our results contribute to a better understanding and characterization of AMR in E. coli. Furthermore, the in vitro validated genes could be considered as new putative drug targets.
METHODS: A systems biology approach was used to identify genes and biological pathways associated with AMR, by mapping known AMR-related genes from the Comprehensive Antibiotic Resistance Database (CARD) and PointFinder database into the E. coli protein interactome. Through a network diffusion algorithm, several network modules were identified, i.e. genes and pathways, in part already known to be involved in AMR mechanisms. We selected gene candidates for performing an in vitro susceptibility validation test, consisting of 13 knockout mutants against nine different antibiotics.
RESULTS: Compared with the WT E. coli BW25113, the AMR of some mutants showed significant shifts of biological relevance: ΔuhpB (S/I) and ΔmdaB (S/R) against ampicillin, ΔrpmG (I/S) and ΔrplA (I/S) against ciprofloxacin, and ΔrplA (I/S) against streptomycin (S, susceptible; I, intermediate; R, resistant). In other cases, only a significant change in inhibition disc diameter was observed, probably deserving further studies.
CONCLUSIONS: Network diffusion is an effective tool to infer relevant biological insights related to AMR from microbial biological networks. Our results contribute to a better understanding and characterization of AMR in E. coli. Furthermore, the in vitro validated genes could be considered as new putative drug targets.