Proteome constraints reveal targets for improving microbial fitness in nutrient‐rich environments

• Published in: Molecular systems biology Vol. 17; no. 4; pp. e10093 - n/a
• Main Authors: Chen, Yu, van Pelt‐KleinJan, Eunice, van Olst, Berdien, Douwenga, Sieze, Boeren, Sjef, Bachmann, Herwig, Molenaar, Douwe, Nielsen, Jens, Teusink, Bas
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.04.2021; EMBO Press; John Wiley and Sons Inc; Springer Nature
• Subjects: Adenosine Triphosphate - metabolism; Amino acids; Arginine; Arginine - metabolism; Bacterial Proteins - metabolism; Biological Evolution; Bioreactors; Catabolism; ccpA; Cell division; Chemostats; Constraints; EMBO21; EMBO23; EMBO56; Enzyme kinetics; Fermentation; Fitness; Gene expression; Genetic Fitness; Genomes; Glucose; Glucose - metabolism; Growth rate; laboratory evolution; Lactic acid; Lactococcus lactis; Lactococcus lactis - genetics; Lactococcus lactis - metabolism; metabolic modeling; Metabolism; Metabolites; Microorganisms; Models, Biological; Mutation - genetics; Nitrogen sources; Nutrient availability; Nutrient sources; Nutrients; Protein synthesis; Proteins; Proteome - metabolism; proteome constraint; Proteomes; Proteomics; Reproducibility of Results; Reproductive fitness; Resource allocation; Sensitivity analysis; Shutdowns; laboratory evolution; proteome constraint; metabolic modeling; ccpA; Lactococcus lactis
• ISSN: 1744-4292; 1744-4292
• DOI: 10.15252/msb.202010093

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. Synopsis A proteome‐constrained genome‐scale metabolic model of Lactococcus lactis (pcLactis) is presented. The model is used to interpret microbial behaviour in nutrient‐rich conditions and predicts testable targets for fitness improvement. A proteome‐constrained genome‐scale metabolic model of L. lactis is built. Arginine catabolism in L. lactis can be explained by the resource allocation paradigm. Glucose and arginine uptake are predicted to have great impact on growth when total proteome is not constrained. Improved fitness is related with upregulation of glucose and arginine metabolism. Graphical Abstract A proteome‐constrained genome‐scale metabolic model of Lactococcus lactis (pcLactis) is presented. The model is used to interpret microbial behaviour in nutrient‐rich conditions and predicts testable targets for fitness improvement.