From modules to networks: A systems-level analysis of the bacitracin stress response in Bacillus subtilis
From modules to networks: A systems-level analysis of the bacitracin stress response in Bacillus subtilis
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从模块到网络：枯草芽孢杆菌中杆菌肽应激反应的系统级分析
DOI：
10.1101/827469
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Piepenbreier H
中科院分区：
文献类型：
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作者：
Piepenbreier H
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。到目前为止，关于这些抗性网络的潜在调控特征的知识很少，因为它们很难单独通过实验来确定。在这里，我们提出了第一个计算方法来阐明针对单一抗生素的多个抗性模块之间的相互作用，以及调控网络结构如何允许细胞响应和补偿抗性的扰动。基于枯草芽孢杆菌对细胞壁合成抑制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研究充分的抗性模块（BceAB和BcrC）对脂质II循环进展的保护作用。通过整合表达水平的实验测量，该模型准确地预测了杆菌肽对B的功效。枯草芽孢杆菌野生型以及缺乏一种或两种抗性模块的突变菌株。我们的研究表明，杆菌肽诱导的脂质II循环性质的变化本身控制着两个抵抗模块之间的相互作用。特别是，UPP（杆菌肽靶向的脂质II循环中间体）浓度的变化将BceAB转运蛋白的作用和通过BcrC的稳态反应与总体抗性反应联系起来。我们建议，监测压力源引起的通路特性的变化，使细胞微调部署的多个阻力系统，并可能作为一个成本效益的战略，以控制对这种stressor.IMPORTANCEAntibiotic耐药性的整体反应对全球健康构成重大威胁，系统的研究，了解潜在的耐药机制是迫切需要的。虽然在破译单个耐药决定簇的机制基础方面取得了重大进展，但许多细菌物种依赖于诱导一整套耐药模块，并且控制这些模块响应抗生素应激的复杂调控网络通常知之甚少。在这项工作中，我们结合实验和理论建模来破译枯草芽孢杆菌对杆菌肽的抗性网络，杆菌肽抑制革兰氏阳性菌的细胞壁生物合成。我们发现了一个高水平的交叉调节之间的两个主要的电阻模块在响应杆菌肽应力和量化的细菌耐药性的影响。为了使我们的实验数据合理化，我们通过纳入阻力模块的定量作用扩展了之前建立的脂质II循环计算模型。这使我们对杆菌肽应激反应网络进行了系统级描述，该网络捕获了抗性模块和细胞壁生物合成的基本脂质II周期之间的复杂相互作用，并准确预测了所有研究突变体中的最小抑菌杆菌肽浓度。有了这个，我们的研究强调了细菌耐药性是如何从冗余的稳态和压力反应模块的交织网络中出现的。
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 Bacillus subtilis 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 B. subtilis 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.IMPORTANCEAntibiotic 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 Bacillus subtilis 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.