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J. Biol. Chem., Vol. 281, Issue 12, 8024-8033, March 24, 2006

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Latent Pathway Activation and Increased Pathway Capacity Enable Escherichia coli Adaptation to Loss of Key Metabolic Enzymes^*

Stephen S. Fong¹², Annik Nanchen¹, Bernhard O. Palsson^¶, and Uwe Sauer

From the Institute of Molecular Systems Biology, ETH Zurich, Zurich CH-8093, Switzerland, the ^¶Department of Bioengineering, University of California, San Diego, La Jolla, California 92093-0412, and the Department of Chemical and Life Science Engineering, Virginia Commonwealth University, P.O. Box 843028, Richmond, Virginia 28284-3028

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, and 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.

Received for publication, September 12, 2005 , and in revised form, November 28, 2005.

^* This work was supported by a scholarship from the Ecole Polytechnique Fédérale de Lausanne (to A. N.). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

The on-line version of this article (available at http://www.jbc.org) contains supplemental Tables 1–3.

¹ These two authors contributed equally to this work.

² To whom correspondence should be addressed: Dept. of Chemical and Life Science Engineering, Virginia Commonwealth University, P.O. Box 843028, Richmond, VA 23284-3028. Tel.: 804-827-7038; Fax: 804-828-3846; E-mail: ssfong{at}vcu.edu.

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