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.

metabolic modeling

Lactococcus lactis

proteome constraint

laboratory evolution

ccpA

Author

Yu Chen

Novo Nordisk Foundation Center for Biosustainability

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Eunice van Pelt-KleinJan

Vrije Universiteit Amsterdam

Top Institute Food and Nutrition (TIFN)

Berdien van Olst

Wageningen University and Research

Vrije Universiteit Amsterdam

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

NIZO food research

Top Institute Food and Nutrition (TIFN)

Douwe Molenaar

Top Institute Food and Nutrition (TIFN)

Vrije Universiteit Amsterdam

Jens B Nielsen

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Novo Nordisk Foundation Center for Biosustainability

Technical University of Denmark (DTU)

BioInnovation Institute

B. Teusink

Top Institute Food and Nutrition (TIFN)

Vrije Universiteit Amsterdam

Molecular Systems Biology

1744-4292 (ISSN)

Vol. 17 4 e10093

Subject Categories

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

DOI

10.15252/msb.202010093

PubMed

33821549

More information

Latest update

6/11/2021