A random forest model based on core genome allelic profiles of MRSA for penicillin plus potassium clavulanate susceptibility prediction.
Hemu Zhuang, Feiteng Zhu, Peng Lan, Shujuan Ji, Lu Sun, Yiyi Chen, Zhengan Wang, Shengnan Jiang, Linyue Zhang, Yiwei Zhu, Yan Jiang, Yan Chen, Yunsong Yu
Author Information
Hemu Zhuang: Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province; Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.
Feiteng Zhu: Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province; Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.
Peng Lan: Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province; Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.
Shujuan Ji: Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province; Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.
Lu Sun: Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province; Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.
Yiyi Chen: Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province; Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.
Zhengan Wang: Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province; Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.
Shengnan Jiang: Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province; Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.
Linyue Zhang: Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province; Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.
Yiwei Zhu: Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province; Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.
Yan Jiang: Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province; Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.
Yan Chen: Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province; Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.
Yunsong Yu: Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province; Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.
Treatment failure of methicillin-resistant (MRSA) infections remains problematic in clinical practice because therapeutic options are limited. Penicillin plus potassium clavulanate combination (PENC) was shown to have potential for treating some MRSA infections. We investigated the susceptibility of MRSA isolates and constructed a drug susceptibility prediction model for the phenotype of the PENC. We determined the minimum inhibitory concentration of PENC for MRSA (=284) in a teaching hospital (SRRSH-MRSA). PENC susceptibility genotypes were analysed using a published genotyping scheme based on the sequence. expression in MRSA isolates was analysed by qPCR. We established a random forest model for predicting PENC-susceptible phenotypes using core genome allelic profiles from cgMLST analysis. We identified S2-R isolates with susceptible genotypes but PENC-resistant phenotypes; these isolates expressed at higher levels than did S2 MRSA (2.61 vs 0.98, <0.05), indicating the limitation of using a single factor for predicting drug susceptibility. Using the data of selected UK-sourced MRSA (=74) and MRSA collected in a previous national survey (NA-MRSA, =471) as a training set, we built a model with accuracies of 0.94 and 0.93 for SRRSH-MRSA and UK-sourced MRSA (=287, NAM-MRSA) validation sets. The AUROC of this model for SRRSH-MRSA and NAM-MRSA was 0.96 and 0.97. Although the source of the training set data affects the scope of application of the prediction model, our data demonstrated the power of the machine learning approach in predicting susceptibility from cgMLST results.
