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A more recent version of this article appeared on March 24, 2006 Originally published In Press as doi:10.1074/jbc.M510016200 on December 1, 2005
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Papers In Press, published online ahead of print November 30, 2005
J. Biol. Chem, 10.1074/jbc.M510016200
Submitted on September 12, 2005
Revised on November 28, 2005
Accepted on November 30, 2005

Latent pathway activation and increased pathway capacity enable Escherichia Coli adaptation to loss of key Ketabolic Enzymes

Stephen S Fong, Annik Nanchen, Bernhard O Palsson, and Uwe Sauer

Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA 23284

Corresponding Author: ssfong{at}vcu.edu

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 (pgi, ppc, pta, 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, while new flux distributions were not observed. Combinatorial changes in capacity and pathway activation, however, led to different intracellular flux states that enabled evolution in 3 of the 4 parallel cases tested. The molecular bases of the evolved phenotypes was 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 non-causal mechanistic changes.


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