Resistance Gene Association and Inference Network (ReGAIN): A Bioinformatics Pipeline for Assessing Probabilistic Co-Occurrence Between Resistance Genes in Bacterial Pathogens.
bioRxiv
; 2024 Mar 01.
Article
em En
| MEDLINE
| ID: mdl-38464005
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.
Texto completo:
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Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos