Identification of hypermucoviscous Klebsiella pneumoniae strains via untargeted surface-enhanced Raman spectroscopy.

ABSTRACT

Klebsiella pneumoniae is one of the most common causes of hospital-acquired infections, especially due to the emergence of the hypervirulent K. pneumoniae (hvKp) strains. Multiple methods have been developed to discriminate hvKp strains from classical K. pneumoniae (cKp) strains, such as the presence of candidate genes (e.g., peg-344, iroB, and iucA), high level of siderophore production, hypermucoviscosity phenotype, etc. Although the string test is commonly used to confirm the hypermucoviscosity of K. pneumoniae strains, it is a method lacking rigidity and accuracy. Surface-enhanced Raman spectroscopy (SERS) coupled with machine learning algorithms has been widely used in discriminating bacterial pathogens with different phenotypes. However, the technique has not be applied to identify hypermucoviscous K. pneumoniae (hmvKp) strains. In this study, we isolated a set of K. pneumoniae strains from clinical samples, among which hmvKp strains (N = 10) and cKP strains (N = 10) were randomly selected to collect SESR spectra. Eight machine learning algorithms were recruited for model construction and spectral prediction in this study, among which support vector machine (SVM) outperforms all other algorithms with the highest prediction accuracy of hmvKp strains (5-fold cross validation = 99.07%). Taken together, this pilot study confirms that SERS, combined with machine learning algorithms, can accurately identify hmvKp strains, which can facilitate the fast recognition of hvKP strains when combined with relevant methods and biomarkers in clinical settings in the near future.