Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility.
Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility.
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DOI：
10.1128/msphere.00443-21
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发表时间：
2021-08-25
期刊：
影响因子：
4.8
通讯作者：
Palsson B
中科院分区：
文献类型：
--
作者：
Sastry AV;Dillon N;Anand A;Poudel S;Hefner Y;Xu S;Szubin R;Feist AM;Nizet V;Palsson B
体外抗生素敏感性测试通常不能准确预测体内药物疗效，部分原因是标准化细菌培养基和体内生理环境之间的分子组成差异。在这里，我们通过转录组学数据的机器学习研究了大肠杆菌K-12 MG 1655中抗生素敏感性和培养基成分之间的相互关系。独立成分分析，信号分离算法的应用表明，由环境条件或抗生素处理诱导的复杂表型变化直接追溯到一些关键的转录调节因子，包括RpoS，Fur和Fnr的作用。将机器学习结果与转录因子激活的生物化学知识相结合，揭示了呼吸和铁可用性的介质依赖性变化，这些变化驱动了不同的抗生素敏感性。通过扩展，这里使用的数据生成和数据分析工作流程可以在任何测量条件下询问病原体的监管状态，并且可以应用于任何菌株或生物体，其中有足够的转录组学数据可用。抗生素耐药性是对全球健康的迫在眉睫的威胁。患者的治疗方案通常是根据临床微生物学实验室的标准化抗生素敏感性测试（AST）的结果选择的，但这些体外测试经常错误地分类药物有效性，因为它们与实际宿主条件的相似性很差。先前试图了解药物和培养基对抗生素疗效的综合影响的尝试集中在生理测量上，但没有将治疗结果与系统水平上的转录反应联系起来。在这里，将机器学习应用于转录组学数据，确定了细菌铁吸收和呼吸活动的关键调节因子中的介质依赖性反应。本文提供的分析工作流程可扩展至其他微生物和条件，并可用于通过确定决定抗生素敏感性的关键调控因素来改善临床AST。
In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available. IMPORTANCE Antibiotic resistance is an imminent threat to global health. Patient treatment regimens are often selected based on results from standardized antibiotic susceptibility testing (AST) in the clinical microbiology lab, but these in vitro tests frequently misclassify drug effectiveness due to their poor resemblance to actual host conditions. Prior attempts to understand the combined effects of drugs and media on antibiotic efficacy have focused on physiological measurements but have not linked treatment outcomes to transcriptional responses on a systems level. Here, application of machine learning to transcriptomics data identified medium-dependent responses in key regulators of bacterial iron uptake and respiratory activity. The analytical workflow presented here is scalable to additional organisms and conditions and could be used to improve clinical AST by identifying the key regulatory factors dictating antibiotic susceptibility.