Introduction
The evolution of antimicrobial resistance in Staphylococcus aureus (S. aureus) involves complex genotype–phenotype interactions of multiple genetic determinants acting independently.
Objective
In this study, we aimed to accurately predict antimicrobial resistance from genomic sequences and uncover the complex genetic interactions underlying its mechanisms, focusing on interpretability and mechanistic insights.
Methods
We propose a framework that elucidates antibiotic resistance by linking the genomic context with underlying gene interactions, combining a reference-agnostic gene-context 22-mer (gkmer) representation, a two-step random forest pipeline (RF1 screening/mapping; RF2 gene-level modelling), co-information-based synergy networks, and protein-structure mapping.
Results
Using genomic sequences from 4569 S. aureus strains, we successfully predicted resistance to 12 antibiotics and examined the genetic interaction networks for penicillin, ciprofloxacin, and erythromycin in detail. On the held-out 20% test set, RF2 achieved an area under the receiver operating characteristic curve (AUC) of 0.83–1.00 (median 0.90) and an F1 score of 0.58–0.99 (median 0.76); comparable performance was observed for RF1. External validation on an independent dataset including 1011 S. aureus isolates revealed strong generalization for ciprofloxacin, erythromycin, and penicillin (area under the receiver operating characteristic curve > 0.95; F1 > 0.97), while highlighting reduced performance for clindamycin and tetracycline, and failure for gentamicin and trimethoprim, thereby delineating the scope and limits of the model and its applicability. Network analysis revealed distinct structural patterns of gene cooperation, highlighting key hubs and community structures consistent with known resistance pathways.
Conclusions
Our findings indicate that antibiotic resistance in S. aureus involves distinct genetic network architectures depending on the antibiotic, highlighting the diverse regulatory pathways bacteria employ to acquire resistance. This discovery-oriented framework prioritizes interpretable determinants and generates testable mechanistic hypotheses, with prospective standardized evaluations aimed at assessing its translatability to clinical settings.
扫码关注我们
求助内容:
应助结果提醒方式:
