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

Authors :
Cheng, Chuankai
O’Brien, Edward J.
McCloskey, Douglas
Utrilla, Jose
Olson, Connor
LaCroix, Ryan A.
Sandberg, Troy E.
Feist, Adam M.
Palsson, Bernhard O.
King, Zachary A.
Source :
PLoS Computational Biology; 6/3/2019, Vol. 15 Issue 6, p1-17, 17p, 2 Charts, 2 Graphs
Publication Year :
2019

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1553734X
Volume :
15
Issue :
6
Database :
Complementary Index
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
136781292
Full Text :
https://doi.org/10.1371/journal.pcbi.1007066