Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug-resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and expression profiles, we generated predictive models and identified biomarkers of resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8-0.9) or very high (> 0.9) sensitivity and predictive values. For all drugs except for ciprofloxacin, gene expression information improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.

中文翻译：

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使用机器学习支持的分子诊断技术预测铜绿假单胞菌的抗药性。

由于抗生素耐药性，有限的治疗选择强调了当前诊断方法的优化需求。在某些细菌物种中，可以根据其基因组序列明确预测其抗药性。在这项研究中，我们对414株耐药性铜绿假单胞菌菌株的基因组和转录组进行了测序。通过训练有关基因存在与否，其序列变异和表达谱的信息的机器学习分类器，我们生成了预测模型并确定了对四种常用抗菌药物耐药的生物标志物。单独或组合使用这些数据类型会导致高（0.8-0.9）或非常高（> 0.9）的灵敏度和预测值。对于除环丙沙星以外的所有药物，基因表达信息均改善了诊断性能。我们的研究结果为分子抗性分析工具的开发铺平了道路，该工具可根据基因组和转录组标志物可靠地预测抗菌药的敏感性。在常规微生物学诊断中实施分子药敏试验系统有望为细菌病原体的抗生素耐药性谱提供更早和更详细的信息，从而可能改变医生治疗细菌感染的方式。