Slow protein fluctuations explain the emergence of growth phenotypes and persistence in clonal bacterial populations.
Slow protein fluctuations explain the emergence of growth phenotypes and persistence in clonal bacterial populations.
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DOI：
10.1371/journal.pone.0054272
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
McFadden J
中科院分区：
文献类型：
--
作者：
Rocco A;Kierzek AM;McFadden J
微生物学中最具挑战性的问题之一是了解一小部分能够抵抗抗生素杀死的微生物如何在基因相同的细胞群中出现，这种现象称为持久性或药物耐受性。其特征是双相杀灭曲线，接触杀菌剂的微生物最初被快速杀死，但随后速度慢得多。在这里，我们将这个问题与理解克隆群体中不同生长表型的出现这一更普遍的问题联系起来。我们通过采用所谓的弱遍历性破坏现象的框架来从数学上解决这个问题，这种现象在动态物理系统中众所周知，我们将其扩展到生物学背景。我们通过分析和直接随机模拟表明，由于控制生长速率的基因表达的随机波动减慢，可能会出现不同的生长表型。在快速基因转录的情况下，系统是遍历的，生长速率分布是单峰的，并且仅解释一种表型。相反，在慢速转录和快速翻译时，出现弱非遍历成分，增长率的群体分布变成双峰，并且识别出两种不同的生长表型。当与抗生素杀灭的公认生长速率依赖性相结合时，该模型描述了观察到的快速和慢速杀灭阶段，并再现了细菌持久性的大部分现象。该模型对于制定持续感染控制策略的努力具有重大意义。
One of the most challenging problems in microbiology is to understand how a small fraction of microbes that resists killing by antibiotics can emerge in a population of genetically identical cells, the phenomenon known as persistence or drug tolerance. Its characteristic signature is the biphasic kill curve, whereby microbes exposed to a bactericidal agent are initially killed very rapidly but then much more slowly. Here we relate this problem to the more general problem of understanding the emergence of distinct growth phenotypes in clonal populations. We address the problem mathematically by adopting the framework of the phenomenon of so-called weak ergodicity breaking, well known in dynamical physical systems, which we extend to the biological context. We show analytically and by direct stochastic simulations that distinct growth phenotypes can emerge as a consequence of slow-down of stochastic fluctuations in the expression of a gene controlling growth rate. In the regime of fast gene transcription, the system is ergodic, the growth rate distribution is unimodal, and accounts for one phenotype only. In contrast, at slow transcription and fast translation, weakly non-ergodic components emerge, the population distribution of growth rates becomes bimodal, and two distinct growth phenotypes are identified. When coupled to the well-established growth rate dependence of antibiotic killing, this model describes the observed fast and slow killing phases, and reproduces much of the phenomenology of bacterial persistence. The model has major implications for efforts to develop control strategies for persistent infections.
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