How do environment-dependent switching rates between susceptible and persister cells affect the dynamics of biofilms faced with antibiotics?
How do environment-dependent switching rates between susceptible and persister cells affect the dynamics of biofilms faced with antibiotics?
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DOI：
10.1038/s41522-018-0049-2
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发表时间：
2018
影响因子：
9.2
通讯作者：
Mathias JD
中科院分区：
文献类型：
--
作者：
Carvalho G;Balestrino D;Forestier C;Mathias JD
Persisters形成耐胁迫细胞的亚群，其在生物膜存活和从诸如抗生素治疗的干扰中恢复的能力中起主要作用。持久性的机制是多种多样的，受环境条件的影响，和persister人口比以前怀疑的更异质性。我们使用计算模型来评估易感细胞和持久细胞之间的三种切换策略对细菌生物膜生长、存活和从抗生素治疗中恢复的能力的影响。测试的策略是：（1）恒定开关，（2）底物依赖性开关和（3）药物依赖性开关。我们在基于个体的生物膜模型中实施了这些策略，并在虚拟生物膜上模拟了抗生素冲击。由于文献中关于转换率的可用数据有限，因此对每种策略评估了9个参数集。基板和生物依赖性开关允许高开关速率，而不影响生物膜的生长。与底物依赖性开关相比，恒定和非恒定依赖性开关与生物膜顶部的较高比例的持久性相关，接近底物源，这可能赋予多物种生物膜内的竞争优势。恒定和底物依赖性策略需要在治疗期间限制持续者的唤醒和死亡与使持续状态足够快以在去除寄生虫后快速恢复之间进行折衷。总的来说，模拟提供了新的见解，在生物膜中的持留种群的动态和他们的动态的增长，生存和恢复时，面对干扰之间的关系。计算机模拟产生了有用的见解的战略，细菌在生物膜使用切换到耐药性的“持久”状态。有各种各样的机制可以促进向持久形式的过渡。处于持续状态的细胞群体被证明比以前认为的更加多样化。法国的研究人员，由Gabriel Carvalho在Aubière的复杂系统工程实验室领导，模拟了不同的转换策略，这些策略允许生物膜在抗生素治疗的情况下存活，恢复和生长。他们的程序研究了比以前的模拟方法更大范围的可变因素对切换率的影响。这些变量包括相关化合物的浓度和流动模式、不断变化的环境条件以及不同抗生素带来的挑战。研究结果将有助于了解和治疗各种具有医学意义的生物膜感染。
Persisters form sub-populations of stress-tolerant cells that play a major role in the capacity of biofilms to survive and recover from disturbances such as antibiotic treatments. The mechanisms of persistence are diverse and influenced by environmental conditions, and persister populations are more heterogeneous than formerly suspected. We used computational modeling to assess the impact of three switching strategies between susceptible and persister cells on the capacity of bacterial biofilms to grow, survive and recover from antibiotic treatments. The strategies tested were: (1) constant switches, (2) substrate-dependent switches and (3) antibiotic-dependent switches. We implemented these strategies in an individual-based biofilm model and simulated antibiotic shocks on virtual biofilms. Because of limited available data on switching rates in the literature, nine parameter sets were assessed for each strategy. Substrate and antibiotic-dependent switches allowed high switching rates without affecting the growth of the biofilms. Compared to substrate-dependent switches, constant and antibiotic-dependent switches were associated with higher proportions of persisters in the top of the biofilms, close to the substrate source, which probably confers a competitive advantage within multi-species biofilms. The constant and substrate-dependent strategies need a compromise between limiting the wake-up and death of persisters during treatments and leaving the persister state fast enough to recover quickly after antibiotic-removal. Overall, the simulations gave new insights into the relationships between the dynamics of persister populations in biofilms and their dynamics of growth, survival and recovery when faced with disturbances. Computer simulations yield useful insights into strategies bacteria in biofilms use to switch into antibiotic-tolerant “persister” states. A diverse range of mechanisms are available to promote the transition into persister forms. The populations of cells in persister states are proving to be more varied than previously thought. Researchers in France, led by Gabriel Carvalho at the Complex Systems Engineering Laboratory in Aubière, modelled different switching strategies that allow biofilms to survive, recover and grow despite antibiotic treatments. Their procedure examines the effect on switching rates of a greater range of variable factors than previous simulation methods. These variables include the concentration and flow patterns of relevant chemical compounds, changing environmental conditions, and the challenge presented by different antibiotics. The results will help research toward understanding and treating a variety of medically significant biofilm infections.
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影响因子：
4.6
作者：
Ghanbari A;Dehghany J;Schwebs T;Müsken M;Häussler S;Meyer-Hermann M
通讯作者：
Meyer-Hermann M
影响因子：
5.4
作者：
Allison, Kyle R.;Brynildsen, Mark P.;Collins, James J.
通讯作者：
Collins, James J.
DOI：
10.1016/j.ddtec.2014.02.003
发表时间：
2014-03-01
期刊：
Drug discovery today. Technologies
影响因子：
--
作者：
Jolivet-Gougeon, Anne;Bonnaure-Mallet, Martine
通讯作者：
Bonnaure-Mallet, Martine
影响因子：
3.8
作者：
Alpkvist, Erik;Picioreanu, Cristian;Heyden, Anders
通讯作者：
Heyden, Anders
DOI：
10.1099/00221287-144-12-3275
发表时间：
1998-12-01
期刊：
MICROBIOLOGY-UK
影响因子：
--
作者：
Kreft, JU;Booth, G;Wimpenny, JWT
通讯作者：
Wimpenny, JWT