Jia Xuefeng, Xiong Yongfen, Xu Yanping, Chen Fangyuan, Han Peng, Qu Jieming, He Quanli, Rao Guanhua
Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China.
Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin, China.
Microbiol Spectr. 2025 Aug 5;13(8):e0055625. doi: 10.1128/spectrum.00556-25. Epub 2025 Jul 11.
Antimicrobial resistance (AMR) represents a critical global health challenge, demanding rapid and accurate antimicrobial susceptibility testing (AST) to guide timely treatments. Traditional culture-based AST methods are slow, while existing whole-genome sequencing (WGS)-based models often suffer from overfitting, poor interpretability, and diminished performance on clinical metagenomic data. In this study, we developed an interpretable genotypic AST approach for using minimal genomic determinants. Analysis of 4,796 . genomes and AST data for 18 antibiotics revealed one to five key resistance genes per antibiotic, including two previously uncharacterized vancomycin resistance markers. These features enabled highly accurate rule-based predictions, achieving area under the curve (AUC) values ranging from 0.94 to 1.00. The model demonstrated an overall sensitivity of 97.43% and specificity of 99.02%, respectively, with a very major error (VME) rate of 2.57% and a major error (ME) rate of 0.98% for isolate-level testing. Furthermore, after optimization for shallow-depth metagenomic sequencing, the model achieved 81.82% to 100% accuracy in AST predictions for 59 clinical samples, bypassing the need for bacterial isolation and reducing diagnostic time by an average of 39.9 hours. By combining minimal feature selection with strong interpretability and adaptability to metagenomic data, this method offers a practical and transformative solution for rapid and reliable AST in clinical settings.IMPORTANCEAntimicrobial resistance (AMR) in poses a critical challenge to global health, necessitating rapid and reliable antimicrobial susceptibility testing (AST) for timely treatment decisions. Traditional culture-based AST is slow, while existing whole-genome sequencing (WGS)-based approaches often suffer from overfitting and poor interpretability. This study introduces a rule-based, interpretable genotypic AST model for that leverages minimal genomic determinants, achieving over 97% accuracy in isolate-level testing and high accuracy in clinical metagenomic samples. By extracting key resistance features and applying a rule-based approach, our model enables faster AST predictions and enhances hospital surveillance of resistant strain outbreaks. This culture-independent method reduces diagnostic time by nearly 40 hours, providing a scalable and actionable solution for clinical AMR management.
抗菌药物耐药性（AMR）是一项严峻的全球健康挑战，需要快速准确的抗菌药物敏感性测试（AST）来指导及时治疗。传统的基于培养的AST方法速度较慢，而现有的基于全基因组测序（WGS）的模型往往存在过拟合、可解释性差以及在临床宏基因组数据上性能下降的问题。在本研究中，我们开发了一种可解释的基因型AST方法，该方法使用最少的基因组决定因素。对4796个基因组和18种抗生素的AST数据进行分析后发现，每种抗生素有1至5个关键耐药基因，其中包括两个以前未鉴定的万古霉素耐药标记。这些特征实现了基于规则的高精度预测，曲线下面积（AUC）值在0.94至1.00之间。该模型在菌株水平测试中的总体敏感性分别为97.43%，特异性为99.02%，非常重大错误（VME）率为2.57%，重大错误（ME）率为0.98%。此外，在针对浅深度宏基因组测序进行优化后，该模型在对59个临床样本的AST预测中准确率达到81.82%至100%，无需进行细菌分离，平均诊断时间缩短39.9小时。通过将最少特征选择与强大的可解释性以及对宏基因组数据的适应性相结合，该方法为临床环境中快速可靠的AST提供了一种实用且具有变革性的解决方案。重要性抗菌药物耐药性（AMR）对全球健康构成了严峻挑战，需要快速可靠的抗菌药物敏感性测试（AST）来做出及时的治疗决策。传统的基于培养的AST速度较慢，而现有的基于全基因组测序（WGS）的方法往往存在过拟合和可解释性差的问题。本研究引入了一种基于规则的、可解释的基因型AST模型，该模型利用最少的基因组决定因素，在菌株水平测试中准确率超过97%，在临床宏基因组样本中准确率也很高。通过提取关键耐药特征并应用基于规则的方法，我们的模型能够更快地进行AST预测，并加强医院对耐药菌株爆发的监测。这种无需培养的方法将诊断时间缩短了近40小时，为临床AMR管理提供了一种可扩展且可行的解决方案。
