Karslake Jason, Maltas Jeff, Brumm Peter, Wood Kevin B
Department of Biophysics, University of Michigan, Ann Arbor, MI.
Department of Physics, University of Michigan, Ann Arbor, MI.
PLoS Comput Biol. 2016 Oct 20;12(10):e1005098. doi: 10.1371/journal.pcbi.1005098. eCollection 2016 Oct.
The inoculum effect (IE) is an increase in the minimum inhibitory concentration (MIC) of an antibiotic as a function of the initial size of a microbial population. The IE has been observed in a wide range of bacteria, implying that antibiotic efficacy may depend on population density. Such density dependence could have dramatic effects on bacterial population dynamics and potential treatment strategies, but explicit measures of per capita growth as a function of density are generally not available. Instead, the IE measures MIC as a function of initial population size, and population density changes by many orders of magnitude on the timescale of the experiment. Therefore, the functional relationship between population density and antibiotic inhibition is generally not known, leaving many questions about the impact of the IE on different treatment strategies unanswered. To address these questions, here we directly measured real-time per capita growth of Enterococcus faecalis populations exposed to antibiotic at fixed population densities using multiplexed computer-automated culture devices. We show that density-dependent growth inhibition is pervasive for commonly used antibiotics, with some drugs showing increased inhibition and others decreased inhibition at high densities. For several drugs, the density dependence is mediated by changes in extracellular pH, a community-level phenomenon not previously linked with the IE. Using a simple mathematical model, we demonstrate how this density dependence can modulate population dynamics in constant drug environments. Then, we illustrate how time-dependent dosing strategies can mitigate the negative effects of density-dependence. Finally, we show that these density effects lead to bistable treatment outcomes for a wide range of antibiotic concentrations in a pharmacological model of antibiotic treatment. As a result, infections exceeding a critical density often survive otherwise effective treatments.
接种物效应（IE）是指抗生素的最低抑菌浓度（MIC）随着微生物群体初始大小的变化而增加的现象。在多种细菌中都观察到了接种物效应，这意味着抗生素疗效可能取决于群体密度。这种密度依赖性可能会对细菌群体动态和潜在治疗策略产生显著影响，但通常无法获得人均生长率随密度变化的具体测量数据。相反，接种物效应测量的是MIC随初始群体大小的变化，而在实验时间尺度上群体密度会发生多个数量级的变化。因此，群体密度与抗生素抑制之间的函数关系通常并不清楚，关于接种物效应对不同治疗策略的影响仍有许多问题未得到解答。为了解决这些问题，我们在这里使用多路复用计算机自动化培养设备，直接测量了在固定群体密度下暴露于抗生素的粪肠球菌群体的实时人均生长率。我们发现，常用抗生素普遍存在密度依赖性生长抑制现象，一些药物在高密度时抑制作用增强，而另一些药物则减弱。对于几种药物，密度依赖性是由细胞外pH值的变化介导的，这是一种以前未与接种物效应联系起来的群体水平现象。我们使用一个简单的数学模型，证明了这种密度依赖性如何在恒定药物环境中调节群体动态。然后，我们说明了时间依赖性给药策略如何减轻密度依赖性的负面影响。最后，我们表明，在抗生素治疗的药理学模型中，这些密度效应会导致在广泛的抗生素浓度范围内出现双稳态治疗结果。因此，超过临界密度的感染通常会在其他有效治疗下存活下来。
