Abstract
The rampant rise of multidrug resistant (MDR) bacterial pathogens poses a severe health threat, necessitating innovative tools to unravel the complex genetic underpinnings of antimicrobial resistance. Despite significant strides in developing genomic tools for detecting resistance genes, a gap remains in analyzing organism-specific patterns of resistance gene co-occurrence. Addressing this deficiency, we developed the Resistance Gene Association and Inference Network (ReGAIN), a novel web-based and command line genomic platform that uses Bayesian network structure learning to identify and map resistance gene networks in bacterial pathogens. ReGAIN not only detects resistance genes using well-established methods, but also elucidates their complex interplay, critical for understanding MDR phenotypes. Focusing on ESKAPE pathogens, ReGAIN yielded a queryable database for investigating resistance gene co-occurrence, enriching resistome analyses, and providing new insights into the dynamics of antimicrobial resistance. Furthermore, the versatility of ReGAIN extends beyond antibiotic resistance genes to include assessment of co-occurrence patterns among heavy metal resistance and virulence determinants, providing a comprehensive overview of key gene relationships impacting both disease progression and treatment outcomes.