Rapid detection of K1 hypervirulent Klebsiella pneumoniae by MALDI-TOF MS

Abstract

Hypervirulent strains of Klebsiella pneumoniae (hvKP) are genetic variants of K. pneumoniae which can cause life-threatening community-acquired infection in healthy individuals. Currently, methods for efficient differentiation between classic K. pneumoniae (cKP) and hvKP strains are not available, often causing delay in diagnosis and treatment of hvKP infections. To address this issue, we devised a Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) approach for rapid identification of K1 hvKP strains. Four standard algorithms, genetic algorithm (GA), support vector machine (SVM), supervised neural network (SNN), and quick classifier (QC), were tested for their power to differentiate between K1 and non-K1 strains, among which SVM was the most reliable algorithm. Analysis of the receiver operating characteristic curves of the interest peaks generated by the SVM model was found to confer highly accurate detection sensitivity and specificity, consistently producing distinguishable profiles for K1 hvKP and non-K1 strains. Of the 43 K. pneumoniae modeling strains tested by this approach, all were correctly identified as K1 hvKP and non-K1 capsule type. Of the 20 non-K1 and 17 K1 hvKP validation isolates, the accuracy of K1 hvKP and non-K1 identification was 94.1 and 90.0%, respectively, according to the SVM model. In summary, the MALDI-TOF MS approach can be applied alongside the conventional genotyping techniques to provide rapid and accurate diagnosis, and hence prompt treatment of infections caused by hvKP.