دورية أكاديمية

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

التفاصيل البيبلوغرافية
العنوان: Classification and prediction of Klebsiella pneumoniae strains with different MLST allelic profiles via SERS spectral analysis
المؤلفون: Li-Yan Zhang, Benshun Tian, Yuan-Hong Huang, Bin Gu, Pei Ju, Yanfei Luo, Jiawei Tang, Liang Wang
المصدر: PeerJ, Vol 11, p e16161 (2023)
بيانات النشر: PeerJ Inc., 2023.
سنة النشر: 2023
المجموعة: LCC:Medicine
LCC:Biology (General)
مصطلحات موضوعية: Klebsiella pneumoniae, MLST, SERS, Hospital-acquired infection, Transmission, Medicine, Biology (General), QH301-705.5
الوصف: The Gram-negative non-motile Klebsiella pneuomoniae 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 K. pneumoniae 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 via SERS spectral analysis correlated well with MLST typing results. However, there is currently no similar comparative analysis in K. pneumoniae strains. In this pilot study, 16 K. pneumoniae 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 K. pneumoniae strains, which were then comparatively classified and predicted via 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 K. pneumoniae 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 K. pneumoniae MLST types, which has the application potential in clinical settings for tracing dissemination and controlling outbreak of K. pneumoniae in hospitals and communities with low costs and high rapidity.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2167-8359
Relation: https://peerj.com/articles/16161.pdf; https://peerj.com/articles/16161/; https://doaj.org/toc/2167-8359
DOI: 10.7717/peerj.16161
URL الوصول: https://doaj.org/article/4671639cff2b4e43ab7be5d622a819e8
رقم الأكسشن: edsdoj.4671639cff2b4e43ab7be5d622a819e8
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:21678359
DOI:10.7717/peerj.16161