Identification of antimicrobial resistance genes in Escherichia coli through network diffusion

Abstract 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.

• Publication year

  2025

• Venue

  Journal of Antimicrobial Chemotherapy

• Publication date

  2025-11-11

• Fields of study

  Biology, Medicine

• Identifiers
  DOI 10.1093/jac/dkaf404PMID 41217903PMCID 12802890
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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