Omic data from evolved E. coli are consistent with computed optimal growth from genome‐scale models
Open Access
- 1 January 2010
- journal article
- Published by Springer Science and Business Media LLC in Molecular Systems Biology
- Vol. 6 (1), 390
- https://doi.org/10.1038/msb.2010.47
Abstract
After hundreds of generations of adaptive evolution at exponential growth, Escherichia coli grows as predicted using flux balance analysis (FBA) on genome‐scale metabolic models (GEMs). However, it is not known whether the predicted pathway usage in FBA solutions is consistent with gene and protein expression in the wild‐type and evolved strains. Here, we report that >98% of active reactions from FBA optimal growth solutions are supported by transcriptomic and proteomic data. Moreover, when E. coli adapts to growth rate selective pressure, the evolved strains upregulate genes within the optimal growth predictions, and downregulate genes outside of the optimal growth solutions. In addition, bottlenecks from dosage limitations of computationally predicted essential genes are overcome in the evolved strains. We also identify regulatory processes that may contribute to the development of the optimal growth phenotype in the evolved strains, such as the downregulation of known regulons and stringent response suppression. Thus, differential gene and protein expression from wild‐type and adaptively evolved strains supports observed growth phenotype changes, and is consistent with GEM‐computed optimal growth states.Keywords
This publication has 56 references indexed in Scilit:
- Model-driven evaluation of the production potential for growth-coupled products of Escherichia coliMetabolic Engineering, 2010
- Growth Rate-Dependent Global Effects on Gene Expression in BacteriaCell, 2009
- Genome evolution and adaptation in a long-term experiment with Escherichia coliNature, 2009
- Shifts in growth strategies reflect tradeoffs in cellular economicsMolecular Systems Biology, 2009
- The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coliNature Biotechnology, 2008
- Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coliMolecular Systems Biology, 2007
- A genome‐scale metabolic reconstruction for Escherichia coli K‐12 MG1655 that accounts for 1260 ORFs and thermodynamic informationMolecular Systems Biology, 2007
- Advances in proteomics data analysis and display using an accurate mass and time tag approachMass Spectrometry Reviews, 2006
- Metabolic gene–deletion strains of Escherichia coli evolve to computationally predicted growth phenotypesNature Genetics, 2004
- An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein databaseJournal of the American Society for Mass Spectrometry, 1994