Gao Weibo, Li Hang, Yang Jingxian, Zhang Jinming, Fu Rongxin, Peng Jiaxi, Hu Yechen, Liu Yitong, Wang Yingshi, Li Shuang, Zhang Shuailong
Beijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
Anal Chem. 2024 Aug 20;96(33):13398-13409. doi: 10.1021/acs.analchem.4c00741. Epub 2024 Aug 3.
Antimicrobial susceptibility testing (AST) plays a critical role in assessing the resistance of individual microbial isolates and determining appropriate antimicrobial therapeutics in a timely manner. However, conventional AST normally takes up to 72 h for obtaining the results. In healthcare facilities, the global distribution of vancomycin-resistant (VRE) infections underscores the importance of rapidly determining VRE isolates. Here, we developed an integrated antimicrobial resistance (AMR) screening strategy by combining matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) with machine learning to rapidly predict VRE from clinical samples. Over 400 VRE and vancomycin-susceptible (VSE) isolates were analyzed using MALDI-MS at different culture times, and a comprehensive dataset comprising 2388 mass spectra was generated. Algorithms including the support vector machine (SVM), SVM with L1-norm, logistic regression, and multilayer perceptron (MLP) were utilized to train the classification model. Validation on a panel of clinical samples (external patients) resulted in a prediction accuracy of 78.07%, 80.26%, 78.95%, and 80.54% for each algorithm, respectively, all with an AUROC above 0.80. Furthermore, a total of 33 mass regions were recognized as influential features and elucidated, contributing to the differences between VRE and VSE through the Shapley value and accuracy, while tandem mass spectrometry was employed to identify the specific peaks among them. Certain ribosomal proteins, such as A0A133N352 and R2Q455, were tentatively identified. Overall, the integration of machine learning with MALDI-MS has enabled the rapid determination of bacterial antibiotic resistance, greatly expediting the usage of appropriate antibiotics.
抗菌药物敏感性试验（AST）在评估单个微生物分离株的耐药性以及及时确定合适的抗菌治疗方法方面发挥着关键作用。然而，传统的AST通常需要长达72小时才能获得结果。在医疗机构中，耐万古霉素肠球菌（VRE）感染的全球分布凸显了快速鉴定VRE分离株的重要性。在此，我们通过将基质辅助激光解吸电离质谱（MALDI-MS）与机器学习相结合，开发了一种综合抗菌药物耐药性（AMR）筛选策略，以快速从临床样本中预测VRE。在不同培养时间使用MALDI-MS分析了400多个VRE和万古霉素敏感（VSE）分离株，生成了一个包含2388个质谱的综合数据集。利用支持向量机（SVM）、L1范数支持向量机、逻辑回归和多层感知器（MLP）等算法训练分类模型。对一组临床样本（外部患者）进行验证，每种算法的预测准确率分别为78.07%、80.26%、78.95%和80.54%，所有算法的曲线下面积（AUROC）均高于0.80。此外，总共识别并阐明了33个质量区域作为有影响的特征，通过Shapley值和准确率来解释VRE和VSE之间的差异，同时采用串联质谱法识别其中的特定峰。初步鉴定出了某些核糖体蛋白，如A0A133N352和R2Q455。总体而言，机器学习与MALDI-MS的结合能够快速确定细菌的抗生素耐药性，极大地加快了合适抗生素的使用。
