Laboratory Medicine, Ganzhou Municipal Hospital, Guangdong Provincial People's Hospital Ganzhou Hospital, Ganzhou, Guangdong Province, China.
Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, China.
PeerJ. 2023 Sep 25;11:e16161. doi: 10.7717/peerj.16161. eCollection 2023.
The Gram-negative non-motile 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 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 SERS spectral analysis correlated well with MLST typing results. However, there is currently no similar comparative analysis in strains. In this pilot study, 16 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 strains, which were then comparatively classified and predicted 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 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 MLST types, which has the application potential in clinical settings for tracing dissemination and controlling outbreak of in hospitals and communities with low costs and high rapidity.
目前，革兰氏阴性非运动性 是医院获得性（HA）和社区获得性（CA）感染的主要原因，在全球范围内引起了极大的公共卫生关注，而快速识别和准确追踪致病细菌对于监测和控制 的爆发和传播至关重要。多位点序列分型（MLST）是一种常用的低成本分型方法，能够根据几个看家基因的等位基因谱区分细菌分离株，尽管该方法的分辨率低且劳动强度大。核心基因组 MLST 方案（cgMLST）最近被提出，用于对细菌菌株进行亚分型和监测，具有高分辨率和可靠性，该方法使用所有或大多数成员物种中保守的数百或数千个基因。然而，该方法复杂且需要对细菌菌株进行全基因组测序，成本高。因此，迫切需要开发具有高分辨率和低成本的新型细菌分型方法。表面增强拉曼光谱（SERS）是一种快速、敏感和廉价的细菌鉴定方法。先前的研究证实，细菌菌株的分类和预测 SERS 光谱分析与 MLST 分型结果相关性良好。然而，目前在 菌株中没有类似的比较分析。在这项初步研究中，选择了 16 株具有不同测序分型（ST）的菌株，并基于核心基因组分析构建了系统发育树。对所有 菌株生成了 SERS 光谱（N = 45/每株），然后使用六种代表性机器学习（ML）算法对其进行比较分类和预测。根据结果，SERS 技术与 ML 算法支持向量机（SVM）相结合，可以在区分和预测所有与相应 MLST 一致的 菌株方面达到最高精度（5 折交叉验证=100%）。总之，在这项初步研究中，我们表明基于 SERS-SVM 的方法能够准确预测 MLST 类型，在临床环境中具有追踪和控制医院和社区中 的传播和爆发的应用潜力，成本低，速度快。
