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Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism

Authors :
Adam M. Feist
Chuankai Cheng
Troy E. Sandberg
Ryan A. LaCroix
Zachary A. King
José Utrilla
Douglas McCloskey
Edward J. O’Brien
Bernhard O. Palsson
Connor A. Olson
Source :
Cheng, C, O'Brien, E J, McCloskey, D, Utrilla, J, Olson, C, LaCroix, R A, Sandberg, T E, Feist, A M, Palsson, B O & King, Z A 2019, ' Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism ', PLOS Computational Biology, vol. 15, no. 6, e1007066 . https://doi.org/10.1371/journal.pcbi.1007066, PLoS Computational Biology, Vol 15, Iss 6, p e1007066 (2019), PLoS Computational Biology
Publication Year :
2018
Publisher :
Cold Spring Harbor Laboratory, 2018.

Abstract

Growth rate and yield are fundamental features of microbial growth. However, we lack a mechanistic and quantitative understanding of the rate-yield relationship. Studies pairing computational predictions with experiments have shown the importance of maintenance energy and proteome allocation in explaining rate-yield tradeoffs and overflow metabolism. Recently, adaptive evolution experiments of Escherichia coli reveal a phenotypic diversity beyond what has been explained using simple models of growth rate versus yield. Here, we identify a two-dimensional rate-yield tradeoff in adapted E. coli strains where the dimensions are (A) a tradeoff between growth rate and yield and (B) a tradeoff between substrate (glucose) uptake rate and growth yield. We employ a multi-scale modeling approach, combining a previously reported coarse-grained small-scale proteome allocation model with a fine-grained genome-scale model of metabolism and gene expression (ME-model), to develop a quantitative description of the full rate-yield relationship for E. coli K-12 MG1655. The multi-scale analysis resolves the complexity of ME-model which hindered its practical use in proteome complexity analysis, and provides a mechanistic explanation of the two-dimensional tradeoff. Further, the analysis identifies modifications to the P/O ratio and the flux allocation between glycolysis and pentose phosphate pathway (PPP) as potential mechanisms that enable the tradeoff between glucose uptake rate and growth yield. Thus, the rate-yield tradeoffs that govern microbial adaptation to new environments are more complex than previously reported, and they can be understood in mechanistic detail using a multi-scale modeling approach.<br />Author summary This study reconciles multiple existing microbial rate-yield tradeoff theories with experimental data. There is great interest in developing quantitative descriptions of the relationship between growth rate and growth yield [1]. However, some reported experiments [2–4] in the literature do not agree with existing theories [5–7]. Specifically, overflow metabolism in E. coli can either be coupled [5, 8] or decoupled [2–4] from growth rate. We found that adaptive laboratory evolution (ALE) experiments of E. coli reveal a two-dimensional rate-yield tradeoff in adapted strains where the dimensions are (i) a tradeoff between growth rate and growth yield, previously reported by [5], and (ii) a tradeoff between substrate uptake rate and growth yield. The appearance of this two-dimensional tradeoff during adaptation suggests that microorganisms adapting to new environments are subject to a more complex set of rate-yield tradeoffs than previously reported [5, 6]. In this study, the two-dimensional rate-yield tradeoff is quantitatively explained through our multi-scale modeling approach, combining a previously reported small-scale proteome allocation model [5] with a genome-scale model of metabolism and gene-expression (ME-model) [9]. The modeling approach is also instrumental to future studies.

Details

Language :
English
Database :
OpenAIRE
Journal :
Cheng, C, O'Brien, E J, McCloskey, D, Utrilla, J, Olson, C, LaCroix, R A, Sandberg, T E, Feist, A M, Palsson, B O & King, Z A 2019, ' Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism ', PLOS Computational Biology, vol. 15, no. 6, e1007066 . https://doi.org/10.1371/journal.pcbi.1007066, PLoS Computational Biology, Vol 15, Iss 6, p e1007066 (2019), PLoS Computational Biology
Accession number :
edsair.doi.dedup.....288cae23c69f8a48f1d5dffac804608f
Full Text :
https://doi.org/10.1101/414912