Abstract
Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections pose a significant threat to human health. Fast and accurate prediction of K. pneumoniae carbapenem resistance and carbapenemase genotype is critical for guiding antibiotic treatment and reducing mortality rates. In this study, we present a novel method using Al-MOF/TiO2@Au cubic heterostructures for the metabolic analysis of intact bacterial cells, enabling rapid diagnosis of CRKP and its carbapenemases genotype. The Al-MOF/TiO2@Au cubic composites display strong light absorption and high surface area, facilitating the in situ effective extraction of metabolic fingerprints from intact bacterial cells. Utilizing this method, we rapidly and sensitively extracted metabolic fingerprints from 169 clinical isolates of K. pneumoniae obtained from patients. Machine learning analysis of the metabolic fingerprint changes successfully distinguishes CRKP from the sensitive strains, achieving the high area under the curve (AUC) values of 1.00 in both training and testing sets based on the 254 m/z features, respectively. Additionally, this platform enables rapid carbapenemase genotype discrimination of CRKP for precision antibiotic therapy. Our strategy holds great potential for swift diagnosis of CRKP and carbapenemase genotype discrimination, guiding effective management of CRKP bacterial infections in both hospital and community settings.
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