Microbiology and Molecular Genetics Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, United States.
Department of Physics, Emory University, Atlanta, United States.
Elife. 2018 Mar 6;7:e32976. doi: 10.7554/eLife.32976.
Effective antibiotic use that minimizes treatment failures remains a challenge. A better understanding of how bacterial populations respond to antibiotics is necessary. Previous studies of large bacterial populations established the deterministic framework of pharmacodynamics. Here, characterizing the dynamics of population extinction, we demonstrated the stochastic nature of eradicating bacteria with antibiotics. Antibiotics known to kill bacteria (bactericidal) induced population fluctuations. Thus, at high antibiotic concentrations, the dynamics of bacterial clearance were heterogeneous. At low concentrations, clearance still occurred with a non-zero probability. These striking outcomes of population fluctuations were well captured by our probabilistic model. Our model further suggested a strategy to facilitate eradication by increasing extinction probability. We experimentally tested this prediction for antibiotic-susceptible and clinically-isolated resistant bacteria. This new knowledge exposes fundamental limits in our ability to predict bacterial eradication. Additionally, it demonstrates the potential of using antibiotic concentrations that were previously deemed inefficacious to eradicate bacteria.
有效使用抗生素以最大限度地减少治疗失败仍然是一个挑战。需要更好地了解细菌种群对抗生素的反应。以前对大型细菌种群的研究建立了药物动力学的确定性框架。在这里,通过对种群灭绝的动力学进行特征描述,我们展示了用抗生素消灭细菌的随机性。已知能杀死细菌的抗生素(杀菌)会引起种群波动。因此,在高抗生素浓度下,细菌清除的动力学是异质的。在低浓度下,清除仍然以非零概率发生。我们的概率模型很好地捕捉到了这些种群波动的显著结果。我们的模型进一步提出了一种通过增加灭绝概率来促进清除的策略。我们通过实验测试了这种针对抗生素敏感和临床分离耐药细菌的预测。这种新知识揭示了我们预测细菌清除能力的基本局限性。此外,它还展示了使用以前认为无效的抗生素浓度来消灭细菌的潜力。