Latent pathway activation and increased pathway capacity enable Escherichia coli adaptation to loss of key metabolic enzymes
Latent pathway activation and increased pathway capacity enable Escherichia coli adaptation to loss of key metabolic enzymes
复制标题
DOI：
10.1074/jbc.m510016200
复制
发表时间：
2006-03-24
影响因子：
4.8
通讯作者：
Sauer, U
中科院分区：
文献类型：
--
作者：
Fong, SS;Nanchen, A;Sauer, U
生物系统适应遗传和环境扰动的能力是一个基本的但在分子水平上知之甚少的过程。通过量化代谢通量和全球mRNA丰度，我们研究了大肠杆菌4个代谢基因缺失突变体（Delta pgi、Delta ppc、Delta pta和Delta tpi）的遗传和代谢机制，并对每个突变体进行了平行进化实验。对基因缺失的最初反应是通过局部旁路反应或通常潜伏的途径进行通量重定向。进化的主要作用是提高了已经活跃的途径的容量，而没有观察到新的通量分布。然而，容量和通路激活的组合变化导致了不同的细胞内通量状态，从而使四个平行测试案例中的三个得以进化。然后通过全球mRNA转录分析阐明了进化表型的分子基础。三羧酸循环中潜在通路的激活和通量的变化与转录水平上的分子变化密切相关。相比之下，其他中枢代谢途径的通量变化显然与转录网络的变化无关。这些结果通过展示大肠杆菌代谢网络补偿遗传扰动的灵活性以及结合多个高通量数据集区分因果和非因果机制变化的效用，为进化过程的动力学提供了新的见解。
The ability of biological systems to adapt to genetic and environmental perturbations is a fundamental but poorly understood process at the molecular level. By quantifying metabolic fluxes and global mRNA abundance, we investigated the genetic and metabolic mechanisms that underlie adaptive evolution of four metabolic gene deletion mutants of Escherichia coli (Delta pgi, Delta ppc, Delta pta, and Delta tpi) in parallel evolution experiments of each mutant. The initial response to the gene deletions was flux rerouting through local bypass reactions or normally latent pathways. The principal effect of evolution was improved capacity of already active pathways, whereas new flux distributions were not observed. Combinatorial changes in capacity and pathway activation, however, led to different intracellular flux states that enabled evolution in three of the four parallel cases tested. The molecular bases of the evolved phenotypes were then elucidated by global mRNA transcript analyses. Activation of latent pathways and flux changes in the tricarboxylic acid cycle were found to correlate well with molecular changes at the transcriptional level. Flux alterations in other central metabolic pathways, in contrast, were apparently not connected to changes in the transcriptional network. These results give new insight into the dynamics of the evolutionary process by demonstrating the flexibility of the metabolic network of E. coli to compensate for genetic perturbations and the utility of combining multiple high throughput data sets to differentiate between causal and noncausal mechanistic changes.