Publication: A Universal Trade-Off Between Growth and Lag in Fluctuating Environments
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2020-07-15
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Springer Science and Business Media LLC
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Basan, Markus, Honda, Tomoya, Christodoulou, Dimitris, Horl, Manuel, Chang, Yu-Fang, Leoncini, Emanuele, Mukherjee, Avik, Okano, Hiroyuki, Taylor, Brian R, Silverman, Josh M, Sanchez, Carlos, Williamson, James R, Paulsson, Johan, Hwa, Terence, and Sauer, Uwe. "A Universal Trade-off between Growth and Lag in Fluctuating Environments." Nature (London) 584, no. 7821 (2020): 470-74.
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Abstract
The rate of cell growth is widely recognized as crucial for fitness in bacteria1,2 and a main driver of proteome allocation, but it is unclear how growth rates are ultimately determined. Increasing evidence suggests that other objectives also play key roles3–7, such as the rate of physiological adaption to changing environments8,9. The challenge for cells is that these objectives cannot be independently optimized, and maximizing one may even minimize another. Many such tradeoffs have indeed been hypothesized, but so far they have mostly been based on qualitative correlative studies8–11, and often lacked mechanistic bases. Here we report the occurrence of a tradeoff between steady-state growth and adaptability for Escherichia coli, upon abruptly shifting a growing culture from a preferred carbon source (e.g., glucose) to fermentation products (e.g., acetate). Such transitions, which are common for enteric bacteria, are often accompanied by multi-hour lags before growth resumes. The inverse lag time for dozens of shifts was found to quantitatively exhibit the same linear dependence on pre-shift growth rates, approaching zero (infinite lag) at the maximum growth rate. Metabolomic analysis revealed that the long lags resulted from the depletion of key metabolites due to the sudden reversal of central carbon flux imposed by these nutrient shifts. The metabolic data led us to a model of sequential flux limitation which not only explained the observed universal tradeoff between growth and adaptability, but also generated other quantitative predictions that then could be validated experimentally. This shows that the trade-off reflects the opposing enzyme requirements for effective glycolysis versus gluconeogenesis.
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