Genome-scale model of metabolism and gene expression provides a multi-scale description of acid stress responses in Escherichia coli
Genome-scale model of metabolism and gene expression provides a multi-scale description of acid stress responses in Escherichia coli
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DOI：
10.1371/journal.pcbi.1007525
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发表时间：
2019-12-01
影响因子：
4.3
通讯作者：
Palsson, Bernhard O.
中科院分区：
文献类型：
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作者：
Du, Bin;Yang, Laurence;Palsson, Bernhard O.
对酸胁迫的反应是大肠杆菌通过胃定植在消化道中成功完成其生命周期的关键。为了对这种反应有一个基本的了解，我们建立了E.大肠杆菌，并将它们与其代谢和大分子表达的基因组规模模型（ME模型）相结合。我们考虑了三种已知的酸胁迫缓解机制：1）膜脂质脂肪酸组成的变化，2）在外部pH值和周质伴侣保护机制的周质蛋白稳定性的变化，和3）在膜蛋白的活性变化。在将这些机制整合到一个已建立的ME模型中后，我们可以在其他细胞过程的背景下模拟它们的反应。我们使用从5个大肠杆菌获得的RNA测序数据验证了这些模拟。大肠杆菌菌株在外部pH 5.5至7.0范围内生长。我们发现：i）对于ME模型中占比的差异表达基因，80%的上调基因被ME模型正确预测，以及ii）这些基因主要涉及翻译过程（45%的基因）、膜蛋白和相关过程（18%的基因）、氨基酸代谢（12%的基因）以及辅因子和辅基生物合成（8%的基因）。我们还展示了几种可以通过ME模型模拟的酸耐受性干预策略。因此，我们建立了一个定量的框架，描述，在基因组规模上，酸胁迫缓解反应的E。了解大肠杆菌的耐酸性机制，不仅具有科学意义，而且具有实用价值。大肠杆菌对食品，保健和生物技术工业有重要意义。E.大肠杆菌对酸胁迫的耐受性可以归因于其多种调节、代谢和生理机制。虽然不同的耐酸性机制已经得到很好的表征，但很少有研究关注这些机制如何共同作用以保护E。大肠杆菌的酸性胁迫。用数学方法描述了大肠杆菌的代谢流状态和蛋白质组分配。大肠杆菌允许表征这些机制如何在系统水平上相互作用和起作用。在此基础上，对现有的E. coli模型框架下，我们表征了E.大肠杆菌：1）膜脂质脂肪酸组成的变化，2）在外部pH和周质伴侣保护机制下周质蛋白稳定性的变化，和3）膜蛋白活性的变化。我们的框架与整合机制的预测表现出良好的协议与RNA测序数据的E。coli对酸胁迫下基因表达变化的影响。这里的努力为实际应用开辟了各种机会，例如挑战酸应激耐受性的干预策略以及在细胞工厂有机酸生产过程中增强耐酸性。
Response to acid stress is critical for Escherichia coli to successfully complete its life-cycle by passing through the stomach to colonize the digestive tract. To develop a fundamental understanding of this response, we established a molecular mechanistic description of acid stress mitigation responses in E. coli and integrated them with a genome-scale model of its metabolism and macromolecular expression (ME-model). We considered three known mechanisms of acid stress mitigation: 1) change in membrane lipid fatty acid composition, 2) change in periplasmic protein stability over external pH and periplasmic chaperone protection mechanisms, and 3) change in the activities of membrane proteins. After integrating these mechanisms into an established ME-model, we could simulate their responses in the context of other cellular processes. We validated these simulations using RNA sequencing data obtained from five E. coli strains grown under external pH ranging from 5.5 to 7.0. We found: i) that for the differentially expressed genes accounted for in the ME-model, 80% of the upregulated genes were correctly predicted by the ME-model, and ii) that these genes are mainly involved in translation processes (45% of genes), membrane proteins and related processes (18% of genes), amino acid metabolism (12% of genes), and cofactor and prosthetic group biosynthesis (8% of genes). We also demonstrated several intervention strategies on acid tolerance that can be simulated by the ME-model. We thus established a quantitative framework that describes, on a genome-scale, the acid stress mitigation response of E. coli that has both scientific and practical uses.Author summary Understanding the acid resistance mechanisms of E. coli has important implications in the food, health care and biotechnology industries. The ability of E. coli to tolerate acid stress can be attributed to its various regulatory, metabolic and physiological mechanisms. Although different acid resistance mechanisms have been well characterized, few studies are focused on understanding how these mechanisms work together to protect E. coli from acid stress. A mathematical representation of the metabolic flux state and proteome allocation of E. coli allows the characterization of how these mechanisms interact and function on a systems-level. Here, based on an existing E. coli model framework, we characterize three acid stress mitigation responses of E. coli: 1) change in membrane lipid fatty acid composition, 2) change in periplasmic protein stability over external pH and periplasmic chaperone protection mechanisms, and 3) change in the membrane protein activities. The predictions of our framework with the integrated mechanisms demonstrated good agreement with RNA sequencing data of E. coli on gene expression changes under acid stress. The efforts here open up various opportunities for practical applications, e.g. intervention strategies that challenge acid stress tolerance and enhancement of acid resistance during organic acid production in a cell factory.