Detection of hidden antibiotic resistance through real-time genomics

Abstract Real-time genomics through nanopore sequencing holds the promise of fast antibiotic resistance prediction directly in the clinical setting. However, concerns about the accuracy of genomics-based resistance predictions persist, particularly when compared to traditional, clinically established diagnostic methods. Here, we leverage the case of a multi-drug resistant Klebsiella pneumoniae infection to demonstrate how real-time genomics can enhance the accuracy of antibiotic resistance profiling in complex infection scenarios. Our results show that unlike established diagnostics, nanopore sequencing data analysis can accurately detect low-abundance plasmid-mediated resistance, which often remains undetected by conventional methods. This capability has direct implications for clinical practice, where such “hidden” resistance profiles can critically influence treatment decisions. Consequently, the rapid, in situ application of real-time genomics holds significant promise for improving clinical decision-making and patient outcomes.