Proteome constraints reveal targets for improving microbial fitness in nutrient-rich environments
Journal article, 2021
Cells adapt to different conditions via gene expression that tunes metabolism for maximal fitness. Constraints on cellular proteome may limit such expression strategies and introduce trade-offs. Resource allocation under proteome constraints has explained regulatory strategies in bacteria. It is unclear, however, to what extent these constraints can predict evolutionary changes, especially for microorganisms that evolved under nutrient-rich conditions, i.e., multiple available nitrogen sources, such as Lactococcus lactis. Here, we present a proteome-constrained genome-scale metabolic model of L. lactis (pcLactis) to interpret growth on multiple nutrients. Through integration of proteomics and flux data, in glucose-limited chemostats, the model predicted glucose and arginine uptake as dominant constraints at low growth rates. Indeed, glucose and arginine catabolism were found upregulated in evolved mutants. At high growth rates, pcLactis correctly predicted the observed shutdown of arginine catabolism because limited proteome availability favored lactate for ATP production. Thus, our model-based analysis is able to identify and explain the proteome constraints that limit growth rate in nutrient-rich environments and thus form targets of fitness improvement.
ccpA
laboratory evolution
metabolic modeling
proteome constraint
Lactococcus lactis
Author
Yu Chen
Novo Nordisk Foundation
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Eunice van Pelt-KleinJan
Top Institute Food and Nutrition (TIFN)
Vrije Universiteit Amsterdam
Berdien van Olst
Vrije Universiteit Amsterdam
Wageningen University and Research
Sieze Douwenga
Wageningen University and Research
Vrije Universiteit Amsterdam
Sjef Boeren
Wageningen University and Research
Top Institute Food and Nutrition (TIFN)
Herwig Bachmann
Vrije Universiteit Amsterdam
Top Institute Food and Nutrition (TIFN)
NIZO FOOD RESEARCH BV
Douwe Molenaar
Top Institute Food and Nutrition (TIFN)
Vrije Universiteit Amsterdam
Jens B Nielsen
Technical University of Denmark (DTU)
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Novo Nordisk Foundation
BioInnovation Institute
B. Teusink
Vrije Universiteit Amsterdam
Top Institute Food and Nutrition (TIFN)
Molecular Systems Biology
17444292 (eISSN)
Vol. 17 4 e10093Subject Categories (SSIF 2011)
Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology
DOI
10.15252/msb.202010093
PubMed
33821549