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

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...

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Main Authors: Lewis, Nathan (Author) , Hixson, Kim K (Author) , Conrad, Tom M (Author) , Lerman, Joshua A (Author) , Charusanti, Pep (Author) , Polpitiya, Ashoka D (Author) , Adkins, Joshua N (Author) , Schramm, Gunnar (Author) , Purvine, Samuel O (Author) , López Ferrer, Daniel (Author) , Weitz, Karl K (Author) , Eils, Roland (Author) , König, Rainer (Author) , Smith, Richard D (Author) , Palsson, Bernhard Ø (Author)
Format: Article (Journal)
Language:English
Published: 27 July 2010
In: Molecular systems biology
Year: 2010, Volume: 6, Issue: 1, Pages: 1-13
ISSN:1744-4292
DOI:10.1038/msb.2010.47
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/msb.2010.47
Verlag, lizenzpflichtig, Volltext: https://www.embopress.org/doi/full/10.1038/msb.2010.47
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Author Notes:Nathan E Lewis, Kim K Hixson, Tom M Conrad, Joshua A Lerman, Pep Charusanti, Ashoka D Polpitiya, Joshua N Adkins, Gunnar Schramm, Samuel O Purvine, Daniel Lopez-Ferrer, Karl K Weitz, Roland Eils, Rainer König, Richard D Smith and Bernhard Ø Palsson
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Summary: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.
Item Description:Gesehen am 14.03.2023
Physical Description:Online Resource
ISSN:1744-4292
DOI:10.1038/msb.2010.47

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