Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models.

TitleOmic data from evolved E. coli are consistent with computed optimal growth from genome-scale models.
Publication TypeJournal Article
Year of Publication2010
AuthorsLewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD, Adkins JN, Schramm G, Purvine SO, Lopez-Ferrer D, Weitz KK, Eils R, König R, Smith RD, Palsson BØ
JournalMol Syst Biol
Volume6
Pagination390
Date Published2010 Jul
ISSN1744-4292
KeywordsAdaptation, Physiological, Bacterial Proteins, Computer Simulation, Escherichia coli, Evolution, Molecular, Gene Expression Regulation, Bacterial, Gene Regulatory Networks, Genomics, Genotype, Metabolomics, Models, Biological, Phenotype, Proteomics, Reproducibility of Results, Systems Biology
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.

DOI10.1038/msb.2010.47
PubMed URLhttp://www.ncbi.nlm.nih.gov/pubmed/20664636?dopt=Abstract
PMCPMC2925526
Alternate TitleMol. Syst. Biol.
PubMed ID20664636