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A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants
Published on: February 19, 2016
Identification of hypermucoviscous Klebsiella pneumoniae strains via untargeted surface-enhanced Raman spectroscopy
Li-Yan Zhang1,2, Jia-Wei Tang2, Ben-Shun Tian2
1Laboratory Medicine, Ganzhou Municipal Hospital, Guangdong Provincial People's Hospital Ganzhou Hospital, Ganzhou, Jiangxi Province, China. zhangliyan@gdph.org.cn.
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.
Insights
Surface-enhanced Raman spectroscopy (SERS) combined with machine learning accurately identifies hypermucoviscous Klebsiella pneumoniae (hmvKp) strains. This method offers a precise alternative to traditional techniques for recognizing these hypervirulent bacterial pathogens.
Area of Science:
- Microbiology
- Spectroscopy
- Machine Learning
Background:
- Klebsiella pneumoniae causes hospital-acquired infections, with hypervirulent (hvKp) strains posing a significant threat.
- Distinguishing hvKp from classical (cKp) strains relies on methods like gene presence or hypermucoviscosity, but current phenotypic tests lack accuracy.
- Surface-enhanced Raman spectroscopy (SERS) shows promise for bacterial discrimination, yet its application to identify hypermucoviscous strains (hmvKp) is unexplored.
Purpose of the Study:
- To evaluate the efficacy of SERS coupled with machine learning for accurate identification of hypermucoviscous Klebsiella pneumoniae (hmvKp) strains.
- To compare the performance of various machine learning algorithms in classifying hmvKp and classical K. pneumoniae (cKp) strains based on SERS spectra.
Main Methods:
- Collected SERS spectra from 10 hmvKp and 10 cKp strains isolated from clinical samples.
- Employed eight distinct machine learning algorithms for model construction and spectral prediction.
- Utilized 5-fold cross-validation to assess model performance and prediction accuracy.
Main Results:
- The Support Vector Machine (SVM) algorithm demonstrated superior performance among the tested models.
- SVM achieved the highest prediction accuracy of 99.07% for identifying hmvKp strains.
- SERS combined with machine learning effectively differentiates hmvKp from cKp strains.
Conclusions:
- SERS coupled with machine learning provides a highly accurate method for identifying hmvKp strains.
- This approach can significantly aid in the rapid clinical recognition of hvKp strains.
- Further integration with relevant biomarkers and methods holds potential for future clinical applications.
