Piepenbreier Hannah, Sim Andre, Kobras Carolin M, Radeck Jara, Mascher Thorsten, Gebhard Susanne, Fritz Georg
LOEWE Center for Synthetic Microbiology and Department of Physics, Philipps-Universität Marburg, Marburg, Germany.
Department of Biology & Biochemistry, Milner Centre for Evolution, University of Bath, Bath, United Kingdom.
mSystems. 2020 Feb 4;5(1):e00687-19. doi: 10.1128/mSystems.00687-19.
Bacterial resistance against antibiotics often involves multiple mechanisms that are interconnected to ensure robust protection. So far, the knowledge about underlying regulatory features of those resistance networks is sparse, since they can hardly be determined by experimentation alone. Here, we present the first computational approach to elucidate the interplay between multiple resistance modules against a single antibiotic and how regulatory network structure allows the cell to respond to and compensate for perturbations of resistance. Based on the response of toward the cell wall synthesis-inhibiting antibiotic bacitracin, we developed a mathematical model that comprehensively describes the protective effect of two well-studied resistance modules (BceAB and BcrC) on the progression of the lipid II cycle. By integrating experimental measurements of expression levels, the model accurately predicts the efficacy of bacitracin against the wild type as well as mutant strains lacking one or both of the resistance modules. Our study reveals that bacitracin-induced changes in the properties of the lipid II cycle itself control the interplay between the two resistance modules. In particular, variations in the concentrations of UPP, the lipid II cycle intermediate that is targeted by bacitracin, connect the effect of the BceAB transporter and the homeostatic response via BcrC to an overall resistance response. We propose that monitoring changes in pathway properties caused by a stressor allows the cell to fine-tune deployment of multiple resistance systems and may serve as a cost-beneficial strategy to control the overall response toward this stressor. Antibiotic resistance poses a major threat to global health, and systematic studies to understand the underlying resistance mechanisms are urgently needed. Although significant progress has been made in deciphering the mechanistic basis of individual resistance determinants, many bacterial species rely on the induction of a whole battery of resistance modules, and the complex regulatory networks controlling these modules in response to antibiotic stress are often poorly understood. In this work we combined experiments and theoretical modeling to decipher the resistance network of against bacitracin, which inhibits cell wall biosynthesis in Gram-positive bacteria. We found a high level of cross-regulation between the two major resistance modules in response to bacitracin stress and quantified their effects on bacterial resistance. To rationalize our experimental data, we expanded a previously established computational model for the lipid II cycle through incorporating the quantitative action of the resistance modules. This led us to a systems-level description of the bacitracin stress response network that captures the complex interplay between resistance modules and the essential lipid II cycle of cell wall biosynthesis and accurately predicts the minimal inhibitory bacitracin concentration in all the studied mutants. With this, our study highlights how bacterial resistance emerges from an interlaced network of redundant homeostasis and stress response modules.
细菌对抗生素的耐药性通常涉及多种相互关联的机制，以确保强大的保护作用。到目前为止，关于这些耐药网络潜在调控特征的了解还很稀少，因为仅通过实验很难确定它们。在此，我们提出了第一种计算方法，以阐明针对单一抗生素的多个耐药模块之间的相互作用，以及调控网络结构如何使细胞对耐药性的扰动做出反应并进行补偿。基于细胞对抑制细胞壁合成的抗生素杆菌肽的反应，我们开发了一个数学模型，该模型全面描述了两个经过充分研究的耐药模块（BceAB和BcrC）对脂质II循环进程的保护作用。通过整合表达水平的实验测量数据，该模型准确预测了杆菌肽对野生型以及缺乏一个或两个耐药模块的突变菌株的效力。我们的研究表明，杆菌肽诱导的脂质II循环自身性质的变化控制了两个耐药模块之间的相互作用。特别是，杆菌肽靶向的脂质II循环中间体UPP浓度的变化，将BceAB转运蛋白的作用和通过BcrC的稳态反应连接到整体耐药反应。我们提出，监测由应激源引起的途径性质变化可以使细胞微调多个耐药系统的部署，并且可能作为一种控制对该应激源整体反应的成本效益策略。抗生素耐药性对全球健康构成重大威胁，迫切需要进行系统研究以了解潜在的耐药机制。尽管在破译单个耐药决定因素的机制基础方面取得了重大进展，但许多细菌物种依赖于一系列耐药模块的诱导，而控制这些模块以应对抗生素应激的复杂调控网络往往 poorly understood。在这项工作中，我们结合实验和理论建模来破译针对杆菌肽的耐药网络，杆菌肽抑制革兰氏阳性细菌的细胞壁生物合成。我们发现，在杆菌肽应激下，两个主要耐药模块之间存在高度的交叉调控，并量化了它们对细菌耐药性的影响。为了使我们的实验数据合理化，我们通过纳入耐药模块的定量作用，扩展了先前建立的脂质II循环计算模型。这使我们对杆菌肽应激反应网络有了系统层面的描述，该网络捕捉了耐药模块与细胞壁生物合成的关键脂质II循环之间的复杂相互作用，并准确预测了所有研究突变体中杆菌肽的最小抑菌浓度。由此，我们的研究突出了细菌耐药性是如何从冗余的稳态和应激反应模块的交错网络中产生的。 （注：原文中“poorly understood”未翻译完整，可能是输入有误，推测完整内容可能是“往往了解甚少”之类的表述）
