Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses.
Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses.
复制标题
DOI：
10.1371/journal.pcbi.1011232
复制
发表时间：
2023-06
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
抗生素耐药性对人类健康构成越来越大的风险，因为目前的抗生素正在失去对日益耐药的病原菌的效力。特别令人关注的是多重耐药菌株的出现，这在革兰氏阴性菌如大肠杆菌中迅速出现。大量的工作已经确定，抗生素耐药机制取决于表型异质性，这可能是由抗生素耐药基因的随机表达介导的。这种分子水平的表达和由此产生的群体水平之间的联系是复杂和多尺度的。因此，为了更好地了解抗生素耐药性，需要的是新的机制模型，反映单细胞表型动力学以及群体水平的异质性，作为一个综合的整体。在这项工作中，我们试图通过建立我们以前在“全细胞”建模方面的经验来桥接单细胞和群体规模建模，这种方法集成了生物过程的数学和机械描述，以概括整个细胞的实验观察行为。为了将全细胞建模扩展到“全菌落”规模，我们嵌入了多个全细胞E。在一个动态空间环境的模型中使用大肠杆菌模型，使我们能够在云上运行大型的并行模拟，其中包含先前全细胞模型的所有分子细节以及在共享环境中生长的菌落的许多交互效应。模拟结果用于探索E.大肠杆菌的两种抗生素具有不同的作用机制，四环素和氨苄青霉素，使我们能够确定亚代表达的基因，如β-内酰胺酶ampC，这大大有助于显着的细胞差异，在稳态周质氨苄青霉素，是一个重要的因素，在确定细胞存活。抗生素耐药性细菌对人类健康构成威胁，使目前的感染治疗方法效果不佳，甚至过时。计算建模已被用于研究各种尺度下与抗生素耐药性相关的现象，从抗生素分子穿过细胞屏障的扩散到医院中的耐药性传播。然而，这些模型无法捕捉同时发生在多个尺度上的现象。通过在共享的空间环境中结合单个大肠杆菌细胞的详细数学模型的多个实例，我们能够模拟具有单细胞分子机制细节的细菌菌落。我们用这个模型研究了E.大肠杆菌对两种具有非常不同作用模式的抗生素的反应，评估这些反应如何受到基因和蛋白质表达的细胞间变异的影响。这项工作对理解对抗生素的紧急菌落水平反应具有意义，并可能为更普遍地模拟菌落规模的紧急现象提供有价值的方法。
Antibiotic resistance poses mounting risks to human health, as current antibiotics are losing efficacy against increasingly resistant pathogenic bacteria. Of particular concern is the emergence of multidrug-resistant strains, which has been rapid among Gram-negative bacteria such as Escherichia coli. A large body of work has established that antibiotic resistance mechanisms depend on phenotypic heterogeneity, which may be mediated by stochastic expression of antibiotic resistance genes. The link between such molecular-level expression and the population levels that result is complex and multi-scale. Therefore, to better understand antibiotic resistance, what is needed are new mechanistic models that reflect single-cell phenotypic dynamics together with population-level heterogeneity, as an integrated whole. In this work, we sought to bridge single-cell and population-scale modeling by building upon our previous experience in “whole-cell” modeling, an approach which integrates mathematical and mechanistic descriptions of biological processes to recapitulate the experimentally observed behaviors of entire cells. To extend whole-cell modeling to the “whole-colony” scale, we embedded multiple instances of a whole-cell E. coli model within a model of a dynamic spatial environment, allowing us to run large, parallelized simulations on the cloud that contained all the molecular detail of the previous whole-cell model and many interactive effects of a colony growing in a shared environment. The resulting simulations were used to explore the response of E. coli to two antibiotics with different mechanisms of action, tetracycline and ampicillin, enabling us to identify sub-generationally-expressed genes, such as the beta-lactamase ampC, which contributed greatly to dramatic cellular differences in steady-state periplasmic ampicillin and was a significant factor in determining cell survival. Antibiotic-resistant bacteria pose a threat to human health, making current treatments for infection less effective or even obsolete. Computational modeling has been used to investigate phenomena related to antibiotic resistance at various scales, from diffusion of antibiotic molecules across cell barriers to the spread of resistance in hospitals. However, these models fail to capture phenomena that occur across multiple scales simultaneously. By combining multiple instances of a detailed mathematical model of individual Escherichia coli cells in a shared spatial environment, we were able to simulate bacterial colonies with single-cell detail of molecular mechanisms. We used this model to investigate the response of E. coli to two antibiotics with very different modes of action, evaluating how these responses were impacted by cell-to-cell variation in gene and protein expression. This work has implications for understanding emergent colony-level responses to antibiotics, and may offer a valuable approach to modeling colony-scale emergent phenomena more generally.
登录
查看更多内容
影响因子：
4
作者：
通讯作者：
--
影响因子：
4.5
作者：
Bernier SP;Lebeaux D;DeFrancesco AS;Valomon A;Soubigou G;Coppée JY;Ghigo JM;Beloin C
通讯作者：
Beloin C
DOI：
10.1073/pnas.78.10.6324
发表时间：
1981-01-01
期刊：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES
影响因子：
--
作者：
CHAO, L;LEVIN, BR
通讯作者：
LEVIN, BR
影响因子：
3.2
作者：
ARIZA, RR;COHEN, SP;DEMPLE, B
通讯作者：
DEMPLE, B
影响因子：
3.2
作者：
COHEN, SP;MCMURRY, LM;LEVY, SB
通讯作者：
LEVY, SB