Classification and prediction of <i>Klebsiella pneumoniae</i> strains with different MLST allelic profiles <i>via</i> SERS spectral analysis

Abstract

The Gram-negative non-motile <i>Klebsiella pneuomoniae</i> is currently a major cause of hospital-acquired (HA) and community-acquired (CA) infections, leading to great public health concern globally, while rapid identification and accurate tracing of the pathogenic bacterium is essential in facilitating monitoring and controlling of <i>K. pneumoniae</i> outbreak and dissemination. Multi-locus sequence typing (MLST) is a commonly used typing approach with low cost that is able to distinguish bacterial isolates based on the allelic profiles of several housekeeping genes, despite low resolution and labor intensity of the method. Core-genome MLST scheme (cgMLST) is recently proposed to sub-type and monitor outbreaks of bacterial strains with high resolution and reliability, which uses hundreds or thousands of genes conserved in all or most members of the species. However, the method is complex and requires whole genome sequencing of bacterial strains with high costs. Therefore, it is urgently needed to develop novel methods with high resolution and low cost for bacterial typing. Surface enhanced Raman spectroscopy (SERS) is a rapid, sensitive and cheap method for bacterial identification. Previous studies confirmed that classification and prediction of bacterial strains <i>via</i> SERS spectral analysis correlated well with MLST typing results. However, there is currently no similar comparative analysis in <i>K. pneumoniae</i> strains. In this pilot study, 16 <i>K. pneumoniae</i> strains with different sequencing typings (STs) were selected and a phylogenetic tree was constructed based on core genome analysis. SERS spectra (N = 45/each strain) were generated for all the <i>K. pneumoniae</i> strains, which were then comparatively classified and predicted <i>via</i> six representative machine learning (ML) algorithms. According to the results, SERS technique coupled with the ML algorithm support vector machine (SVM) could achieve the highest accuracy (5-Fold Cross Validation = 100%) in terms of differentiating and predicting all the <i>K. pneumoniae</i> strains that were consistent to corresponding MLSTs. In sum, we show in this pilot study that the SERS-SVM based method is able to accurately predict <i>K. pneumoniae</i> MLST types, which has the application potential in clinical settings for tracing dissemination and controlling outbreak of <i>K. pneumoniae</i> in hospitals and communities with low costs and high rapidity.