Tan Rundong, Yu Anqi, Liu Ziming, Liu Ziqi, Jiang Rongfeng, Wang Xiaoli, Liu Jialin, Gao Junhui, Wang Xinjun
Shanghai Biotecan Pharmaceuticals Co., Ltd., Shanghai, China.
Shanghai Zhangjiang Institute of Medical Innovation, Shanghai, China.
Front Microbiol. 2021 Aug 23;12:712886. doi: 10.3389/fmicb.2021.712886. eCollection 2021.
Minimal inhibitory concentration (MIC) is defined as the lowest concentration of an antimicrobial agent that can inhibit the visible growth of a particular microorganism after overnight incubation. Clinically, antibiotic doses for specific infections are determined according to the fraction of MIC. Therefore, credible assessment of MICs will provide a physician valuable information on the choice of therapeutic strategy. Early and precise usage of antibiotics is the key to an infection therapy. Compared with the traditional culture-based method, the approach of whole genome sequencing to identify MICs can shorten the experimental time, thereby improving clinical efficacy. is one of the most significant members of the genus in the Enterobacteriaceae family and also a common non-social pathogen. Meropenem is a broad-spectrum antibacterial agent of the carbapenem family, which can produce antibacterial effects of most Gram-positive and -negative bacteria. In this study, we used single-nucleotide polymorphism (SNP) information and nucleotide -mers count based on metagenomic data to predict MICs of meropenem against . Then, features of 110 sequenced genome data were combined and modeled with XGBoost algorithm and deep neural network (DNN) algorithm to predict MICs. We first use the XGBoost classification model and the XGBoost regression model. After five runs, the average accuracy of the test set was calculated. The accuracy of using nucleotide -mers to predict MICs of the XGBoost classification model and XGBoost regression model was 84.5 and 89.1%. The accuracy of SNP in predicting MIC was 80 and 81.8%, respectively. The results show that XGBoost regression is better than XGBoost classification in both nucleotide -mers and SNPs to predict MICs. We further selected 40 nucleotide -mers and 40 SNPs with the highest correlation with MIC values as features to retrain the XGBoost regression model and DNN regression model. After 100 and 1,000 runs, the results show that the accuracy of the two models was improved. The accuracy of the XGBoost regression model for -mers, SNPs, and -mers & SNPs was 91.1, 85.2, and 91.3%, respectively. The accuracy of the DNN regression model was 91.9, 87.1, and 91.8%, respectively. Through external verification, some of the selected features were found to be related to drug resistance.
最低抑菌浓度（MIC）定义为抗菌剂的最低浓度，该浓度在过夜培养后能够抑制特定微生物的可见生长。临床上，针对特定感染的抗生素剂量是根据MIC的分数来确定的。因此，对MIC进行可靠评估将为医生提供有关治疗策略选择的有价值信息。早期且精确地使用抗生素是感染治疗的关键。与传统的基于培养的方法相比，通过全基因组测序来确定MIC的方法可以缩短实验时间，从而提高临床疗效。是肠杆菌科属中最重要的成员之一，也是一种常见的非社会性病原体。美罗培南是碳青霉烯类的广谱抗菌剂，可对大多数革兰氏阳性和阴性细菌产生抗菌作用。在本研究中，我们基于宏基因组数据使用单核苷酸多态性（SNP）信息和核苷酸 -mers计数来预测美罗培南对的MIC。然后，将110个测序的基因组数据的特征进行组合，并使用XGBoost算法和深度神经网络（DNN）算法进行建模以预测MIC。我们首先使用XGBoost分类模型和XGBoost回归模型。经过五次运行后，计算测试集的平均准确率。使用核苷酸 -mers预测XGBoost分类模型和美罗培南MIC的准确率分别为84.5%和89.1%。SNP预测MIC的准确率分别为80%和81.8%。结果表明，在使用核苷酸 -mers和SNP预测MIC方面，XGBoost回归优于XGBoost分类。我们进一步选择了与MIC值相关性最高的40个核苷酸 -mers和40个SNP作为特征，对XGBoost回归模型和DNN回归模型进行重新训练。经过100次和1000次运行后，结果表明两个模型的准确率都有所提高。XGBoost回归模型对于 -mers、SNP以及 -mers和SNP的准确率分别为91.1%、85.2%和91.3%。DNN回归模型的准确率分别为91.9%、87.1%和91.8%。通过外部验证，发现一些所选特征与耐药性有关。
