Kratz Josiah C, Banerjee Shiladitya
Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
PRX Life. 2024 Jan-Mar;2(1). doi: 10.1103/prxlife.2.013010. Epub 2024 Feb 29.
To optimize their fitness, cells face the crucial task of efficiently responding to various stresses. This necessitates striking a balance between conserving resources for survival and allocating resources for growth and division. The fundamental principles governing these tradeoffs is an outstanding challenge in the physics of living systems. In this study, we introduce a coarse-grained theoretical framework for bacterial physiology that establishes a connection between the physiological state of cells and their survival outcomes in dynamic environments, particularly in the context of antibiotic exposure. Predicting bacterial survival responses to varying antibiotic doses proves challenging due to the profound influence of the physiological state on critical parameters, such as the minimum inhibitory concentration (MIC) and killing rates, even within an isogenic cell population. Our proposed theoretical model bridges the gap by linking extracellular antibiotic concentration and nutrient quality to intracellular damage accumulation and gene expression. This framework allows us to predict and explain the control of cellular growth rate, death rate, MIC, and survival fraction in a wide range of time-varying environments. Surprisingly, our model reveals that cell death is rarely due to antibiotic levels being above the maximum physiological limit, but instead survival is limited by the inability to alter gene expression sufficiently quickly to transition to a less susceptible physiological state. Moreover, bacteria tend to overexpress stress response genes at the expense of reduced growth, conferring greater protection against further antibiotic exposure. This strategy is in contrast to those employed in different nutrient environments, in which bacteria allocate resources to maximize growth rate. This highlights an important tradeoff between the cellular capacity for growth and the ability to survive antibiotic exposure.
为了优化自身适应性，细胞面临着有效应对各种应激的关键任务。这就需要在为生存保存资源与为生长和分裂分配资源之间取得平衡。支配这些权衡取舍的基本原理是生命系统物理学中一个突出的挑战。在本研究中，我们引入了一个细菌生理学的粗粒度理论框架，该框架在细胞的生理状态与其在动态环境中，特别是在抗生素暴露情况下的生存结果之间建立了联系。由于生理状态对诸如最低抑菌浓度（MIC）和杀灭率等关键参数有深远影响，即使在同基因细胞群体中，预测细菌对不同抗生素剂量的生存反应也颇具挑战性。我们提出的理论模型通过将细胞外抗生素浓度和营养质量与细胞内损伤积累和基因表达联系起来，弥补了这一差距。该框架使我们能够预测和解释在广泛的随时间变化的环境中细胞生长速率、死亡率、MIC和存活分数的控制情况。令人惊讶的是，我们的模型表明，细胞死亡很少是由于抗生素水平高于最大生理极限，而是由于无法足够迅速地改变基因表达以转变到较不易感的生理状态，从而限制了存活。此外，细菌倾向于以生长减缓为代价过度表达应激反应基因，从而对进一步的抗生素暴露提供更大的保护。这种策略与在不同营养环境中采用的策略形成对比，在不同营养环境中，细菌分配资源以最大化生长速率。这凸显了细胞生长能力与在抗生素暴露下存活能力之间的重要权衡。
