Overflow metabolism in E. coli results from efficient proteome allocation
By Markus Basan, Sheng Hui, H. Okano, Zhongge Zhang, Yang Shen, James R. Williamson, Terence Hwa. Nature, 2015
670 citations recorded by the source corpus.
Overflow metabolism refers to the seemingly wasteful strategy in which cells use fermentation instead of the more efficient respiration to generate energy, despite the availability of oxygen. Known as the Warburg effect in the context of cancer growth, this phenomenon occurs ubiquitously for fast-growing cells, including bacteria, fungi and mammalian cells, but its origin has remained unclear despite decades of research. Here we study metabolic overflow in Escherichia coli, and show that it is a global physiological response used to cope with changing proteomic demands of energy biogenesis and biomass synthesis under different growth conditions. A simple model of proteomic resource allocation can quantitatively account for all of the observed behaviours, and accurately predict responses to new perturbations. The key hypothesis of the model, that the proteome cost of energy biogenesis by respiration exceeds that by fermentation, is quantitatively confirmed by direct measurement of protein abundances via quantitative mass spectrometry.
Subfields: Molecular Biology, Spectroscopy
View Paper: https://www.semanticscholar.org/paper/6aa705f52e5614f77fb506b1f534dd15c36b0735
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In Molecular Biology
- Bernhard O. Palsson
- Jens Nielsen
- D. McCloskey
- Eduard J. Kerkhoven
- Benjamín J. Sánchez
- Avlant Nilsson
- Cheng Zhang
- Petri-Jaan Lahtvee
Citations in the corpus
- The Warburg Effect: How Does it Benefit Cancer Cells? (2016, background): “Furthermore, others have proposed that aerobic glycolysis is a tradeoff to support biosynthesis [34,35,62].”
- Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints (2017, methodology): “These results reinforce the idea that there is a simple principle for protein allocation in microorganisms (Basan et al, 2015; Hui et al, 2015; Nilsson & Nielsen, 2016).”
- A white-box machine learning approach for revealing antibiotic mechanisms of action (2019, positive): “Evolution has optimized bacteria for efficient resource allocation under unstressed growth (Basan et al., 2015; Hui et al., 2015; Scott et al., 2014), and insults to the ATP pool and other energy currencies are sufficient for stimulating central carbon metabolism (Holm et al.”
- Probiotics and competitive exclusion of pathogens in shrimp aquaculture (2020, background): “…et al., 2009), has been observed in a number of fast-growing bacterial and fungal species including Saccharomyces cerevisiae, Escherichia coli and Bacillus subtilis (van Dijken et al., 1993; Vemuri et al., 2006; Sonenshein, 2007; Molenaar et al., 2009; Basan et al., 2015; LaCroix et al., 2015).”