Coordination of bacterial proteome with metabolism by cyclic AMP signalling.

Coordination of bacterial proteome with metabolism by cyclic AMP signalling.
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DOI:
10.1038/nature12446
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发表时间:
2013-08-15
期刊:
影响因子:
64.8
通讯作者:
--
中科院分区:
综合性期刊1区
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--
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大肠杆菌中环磷酸腺苷 (cAMP) 依赖性分解代谢物抑制作用是生物学中研究最深入的调节过程之一。然而,cAMP 信号传导的生理功能及其分子触发因素仍然难以捉摸。在这里,我们使用定量生理学方法来表明,cAMP信号传导在指数细胞生长过程中紧密协调细胞的蛋白质表达程序及其代谢需求:碳分解代谢基因的表达在限制碳流入时随着生长速率的降低而线性增加,但在限制氮或硫流入时随着生长速率的降低而线性下降。相反,生物合成基因的表达表现出与分解代谢基因相反的线性生长速率依赖性。粗粒度数学模型提供了一个定量框架,用于理解和预测基因表达对分解代谢和合成代谢限制的反应。提出并验证了一种积分反馈控制方案,其特征是通过代谢前体抑制 cAMP 信号传导。这些结果揭示了 cAMP 依赖性分解代谢物抑制的关键生理作用:确保蛋白质组资源根据不同营养环境的需要用于不同的代谢部分。我们的发现强调了定量生理学在揭示复杂分子信号网络的潜在功能方面的力量。
Cyclic AMP (cAMP) dependent catabolite repression effect in E. coli is among the most intensely studied regulatory processes in biology. However, the physiological function(s) of cAMP signalling and its molecular triggers remain elusive. Here we use a quantitative physiological approach to show that cAMP signalling tightly coordinates the cell’s protein expression program with its metabolic needs during exponential cell growth: The expression of carbon catabolic genes increased linearly with decreasing growth rates upon limitation of carbon influx, but decreased linearly with decreasing growth rate upon limitation of nitrogen or sulfur influx. In contrast, the expression of biosynthetic genes exhibited the opposite linear growth-rate dependence as the catabolic genes. A coarse-grained mathematical model provides a quantitative framework for understanding and predicting gene expression responses to catabolic and anabolic limitations. A scheme of integral feedback control featuring the inhibition of cAMP signalling by metabolic precursors is proposed and validated. These results reveal a key physiological role of cAMP-dependent catabolite repression: to ensure that proteomic resources are spent on distinct metabolic sectors as needed in different nutrient environments. Our finding underscores the power of quantitative physiology in unravelling the underlying functions of complex molecular signalling networks.
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