Using Genomic Tools to Predict Antimicrobial Resistance and Markers in Clinical Bacterial Samples

Abstract

AbstractAntimicrobial resistance (AMR) poses a critical threat to hospital infections particularly in the context of hospital-acquired infections (HAIs). This study leverages genomic tools to predict AMR and identify resistance markers in clinical bacterial samples associated with HAIs. Using comprehensive genomic and phenotypic analyses, we evaluated the genetic profiles of Pseudomonas aeruginosa and Staphylococcus aureus to uncover resistance mechanisms. Our results demonstrate that genomic tools, such as CARD-RGI and the Solu platform, can accurately identify resistance genes and predict AMR phenotypes in nosocomial pathogens. These findings underscore the potential of integrating genomic approaches into clinical practice to enhance the management of resistant infections in hospital settings and inform the development of novel antimicrobial strategies.ImportanceThis study investigates the impact of prophages on antibiotic resistance in two clinically significant bacteria, Pseudomonas aeruginosa and Staphylococcus aureus. Understanding how prophages influence resistance mechanisms in these pathogens is crucial, as Pseudomonas aeruginosa is known for its role in chronic infections in cystic fibrosis patients, while Staphylococcus aureus, including MRSA strains, is a leading cause of hospital-acquired infections. By exploring the relationship between prophage presence and resistance, this research provides insights that could inform the development of more effective treatment strategies and enhance our ability to combat antibiotic-resistant infections, ultimately improving patient outcomes and public health.