Van Camp Pieter-Jan, Haslam David B, Porollo Aleksey
Department of Biomedical Informatics, University of Cincinnati, Cincinnati, OH, United States.
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
Front Microbiol. 2020 May 25;11:1013. doi: 10.3389/fmicb.2020.01013. eCollection 2020.
Early detection of antimicrobial resistance in pathogens and prescription of more effective antibiotics is a fast-emerging need in clinical practice. High-throughput sequencing technology, such as whole genome sequencing (WGS), may have the capacity to rapidly guide the clinical decision-making process. The prediction of antimicrobial resistance in Gram-negative bacteria, often the cause of serious systemic infections, is more challenging as genotype-to-phenotype (drug resistance) relationship is more complex than for most Gram-positive organisms.
We have used NCBI BioSample database to train and cross-validate eight XGBoost-based machine learning models to predict drug resistance to cefepime, cefotaxime, ceftriaxone, ciprofloxacin, gentamicin, levofloxacin, meropenem, and tobramycin tested in , , , , and . The input is the WGS data in terms of the coverage of known antibiotic resistance genes by shotgun sequencing reads. Models demonstrate high performance and robustness to class imbalanced datasets.
Whole genome sequencing enables the prediction of antimicrobial resistance in Gram-negative bacteria. We present a tool that provides an antibiogram for eight drugs. Predictions are accompanied with a reliability index that may further facilitate the decision making process. The demo version of the tool with pre-processed samples is available at https://vancampn.shinyapps.io/wgs2amr/. The stand-alone version of the predictor is available at https://github.com/pieterjanvc/wgs2amr/.
在临床实践中，快速检测病原体中的抗菌药物耐药性并开具更有效的抗生素处方的需求迅速增长。高通量测序技术，如全基因组测序（WGS），可能有能力快速指导临床决策过程。革兰氏阴性菌通常是严重全身感染的病因，其抗菌药物耐药性的预测更具挑战性，因为基因型与表型（耐药性）的关系比大多数革兰氏阳性菌更为复杂。
我们使用NCBI生物样本数据库训练并交叉验证了八个基于XGBoost的机器学习模型，以预测在[具体实验]中测试的头孢吡肟、头孢噻肟、头孢曲松、环丙沙星、庆大霉素、左氧氟沙星、美罗培南和妥布霉素的耐药性。输入是通过鸟枪法测序读数对已知抗生素耐药基因的覆盖度表示的WGS数据。模型对类别不平衡数据集表现出高性能和稳健性。
全基因组测序能够预测革兰氏阴性菌的抗菌药物耐药性。我们展示了一种工具，可提供针对八种药物的抗菌谱。预测结果伴有可靠性指标，这可能进一步促进决策过程。该工具的演示版本及预处理样本可在https://vancampn.shinyapps.io/wgs2amr/获取。预测器的独立版本可在https://github.com/pieterjanvc/wgs2amr/获取。
