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Rapid Detection of K1 Hypervirulent Klebsiella pneumoniae by MALDI-TOF MS
Yonglu Huang1, Jiaping Li1, Danxia Gu1
1Department of Clinical Microbiology, Second Affiliated Hospital of Zhejiang University Hangzhou, China.
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.
Insights
Rapidly differentiate hypervirulent Klebsiella pneumoniae (hvKP) from classic strains using Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). This method improves diagnosis speed and accuracy for life-threatening hvKP infections.
Area of Science:
- Microbiology
- Infectious Diseases
- Analytical Chemistry
Background:
- Hypervirulent Klebsiella pneumoniae (hvKP) strains cause severe community-acquired infections in healthy individuals.
- Current diagnostic methods lack efficiency in differentiating hvKP from classic K. pneumoniae (cKP), delaying critical treatment.
- Rapid and accurate identification of hvKP is crucial for effective patient management.
Purpose of the Study:
- To develop a rapid and accurate method for differentiating K1 hvKP strains from non-K1 strains.
- To evaluate the efficacy of Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) combined with machine learning algorithms for this differentiation.
- To provide a tool for faster diagnosis of hvKP infections.
Main Methods:
- Utilized MALDI-TOF MS to analyze K. pneumoniae strains.
- Applied four algorithms (genetic algorithm, support vector machine, supervised neural network, quick classifier) to differentiate K1 and non-K1 strains.
- Validated the most reliable algorithm (SVM) using receiver operating characteristic curves and a set of modeling and validation isolates.
Main Results:
- The Support Vector Machine (SVM) algorithm demonstrated the highest reliability in differentiating K1 and non-K1 strains.
- The MALDI-TOF MS approach achieved high accuracy in identifying K1 hvKP and non-K1 strains, with correct identification rates of 94.1% and 90.0% in validation isolates, respectively.
- Distinguishable spectral profiles were consistently generated for K1 hvKP and non-K1 strains.
Conclusions:
- MALDI-TOF MS, coupled with SVM analysis, offers a rapid and accurate method for identifying K1 hvKP strains.
- This approach can supplement conventional genotyping techniques for timely diagnosis of hvKP infections.
- Prompt diagnosis facilitated by this method can lead to earlier and more effective treatment of patients with hvKP.
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