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Protein cost allocation explains metabolic strategies in Escherichia coli
Journal of Biotechnology ( IF 4.1 ) Pub Date : 2020-12-10 , DOI: 10.1016/j.jbiotec.2020.11.003
Pranas Grigaitis 1 , Brett G Olivier 1 , Tomas Fiedler 2 , Bas Teusink 3 , Ursula Kummer 4 , Nadine Veith 4
Affiliation  

In-depth understanding of microbial growth is crucial for the development of new advances in biotechnology and for combating microbial pathogens. Condition-specific proteome expression is central to microbial physiology and growth. A multitude of processes are dependent on the protein expression, thus, whole-cell analysis of microbial metabolism using genome-scale metabolic models is an attractive toolset to investigate the behaviour of microorganisms and their communities. However, genome-scale models that incorporate macromolecular expression are still inhibitory complex: the conceptual and computational complexity of these models severely limits their potential applications. In the need for alternatives, here we revisit some of the previous attempts to create genome-scale models of metabolism and macromolecular expression to develop a novel framework for integrating protein abundance and turnover costs to conventional genome-scale models. We show that such a model of Escherichia coli successfully reproduces experimentally determined adaptations of metabolism in a growth condition-dependent manner. Moreover, the model can be used as means of investigating underutilization of the protein machinery among different growth settings. Notably, we obtained strongly improved predictions of flux distributions, considering the costs of protein translation explicitly. This finding in turn suggests protein translation being the main regulation hub for cellular growth.



中文翻译:

蛋白质成本分配解释了大肠杆菌的代谢策略

深入了解微生物生长对于生物技术的新进展和对抗微生物病原体至关重要。条件特异性蛋白质组表达是微生物生理学和生长的核心。许多过程都依赖于蛋白质表达,因此,使用基因组规模的代谢模型对微生物代谢进行全细胞分析是研究微生物及其群落行为的有吸引力的工具集。然而,包含大分子表达的基因组规模模型仍然是抑制性复杂的:这些模型的概念和计算复杂性严重限制了它们的潜在应用。在需要替代品的情况下,在这里,我们回顾了之前创建代谢和大分子表达的基因组规模模型的一些尝试,以开发一个新的框架,将蛋白质丰度和周转成本整合到传统的基因组规模模型中。我们证明了这样的模型大肠杆菌以依赖于生长条件的方式成功地再现了实验确定的代谢适应性。此外,该模型可用作调查不同生长环境中蛋白质机制未充分利用的手段。值得注意的是,我们得到了极大改进的通量分布预测,明确考虑了蛋白质翻译的成本。这一发现反过来表明蛋白质翻译是细胞生长的主要调节中心。

更新日期:2021-01-10
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