Holistic understanding of trimethoprim resistance in using an integrative approach of genome-wide association study, resistance reconstruction, and machine learning.
Journal:
mBio
PMID:
39120145
Abstract
UNLABELLED: Antimicrobial resistance (AMR) is a public health threat worldwide. Next-generation sequencing (NGS) has opened unprecedented opportunities to accelerate AMR mechanism discovery and diagnostics. Here, we present an integrative approach to investigate trimethoprim (TMP) resistance in the key pathogen . We explored a collection of 662 . genomes by conducting a genome-wide association study (GWAS), followed by functional validation using resistance reconstruction experiments, combined with machine learning (ML) approaches to predict TMP minimum inhibitory concentration (MIC). Our study showed that multiple additive mutations in the and loci are responsible for TMP non-susceptibility in and can be used as key features to build ML models for digital MIC prediction, reaching an average accuracy within ±1 twofold dilution factor of 86.3%. Our roadmap of analysis-wet-lab validation-diagnostic tool building could be adapted to explore AMR in other combinations of bacteria-antibiotic.