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Updated: Mar 12, 2026

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金色葡萄球菌对非β-乳糖抗生素的耐药性:全球基因组流行病学和机器学习可行性评估
Saeid Sadeghi Ghazi Chaki1, Maryam Abdulrahman Najim2, Lina A Hassan3
1Department of Microbial Biotechnology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, Tehran, Iran.
Molecular genetics and genomics : MGG
|March 10, 2026
概括
黄金葡萄球菌对非β-乳糖抗生素的耐药性正在增长,特别是菌素和细菌素,临床菌株的耐药性最高. 机器学习模型在预测由于数据差距的阻力方面取得了有限的成功.
科学领域:
- 微生物学与传染病的研究
- 基因组学和生物信息学
- 计算生物学和机器学习
背景情况:
- 黄金葡萄球菌 (Staphylococcus aureus) 抗生素耐药性增加,需要替代β-乳酸药物.
- 针对非β-乳糖细胞壁的抗生素至关重要,但对它们的耐药性正在出现.
- 影响对这些替代抗生素敏感性的基因组因素需要识别.
研究的目的:
- 整合基因组和表型数据,使用机器学习来预测金杆菌的最小抑制度 (MIC).
- 发现抗生素耐药机制的时间和地理趋势.
- 确定对非β-乳糖抗生素耐药性的基因组决定因素.
主要方法:
- 来自NCBI GenBank的111,350个S. aureus基因组的分析,识别了耐药性基因和突变.
- 对五种抗生素的表型MIC数据的编制和标准化.
- 机器学习模型的应用 (随机森林,XGBoost,弹性网,PLS) 用于MIC预测.
- 用于时间和地理趋势分析的物流回归和统计测试.
主要成果:
- 菌素耐药基因 (fosB) 普遍存在 (总体为65.3%,临床隔离物为32.5%),年均患病率增加 (0.20%).
- 在6.2%的临床隔离物中检测到抗乙素耐药基因 (bcrAB).
- 机器学习模型对达普素MICs (R2 = 0.49) 的预测性能中等,但由于MIC可变性有限,对万科素 (R2 = 0.05) 和提科普拉宁 (R2 = -0.13) 的准确性较差.
- 由于缺乏足够的表型数据 (素和素的n=1),无法对这些抗生素进行预测模型的培训.
结论:
- 黄金菌对非β-乳糖抗生素如菌素和细菌素的耐药性越来越强,特别是在临床环境中.
- 特别是在临床和动物来源中,fobB耐药性流行率的增加是一个重大问题.
- 综合基因组和表型监测至关重要,强大的数据对于基于机器学习的有效抗生素耐药性预测至关重要.
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