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

Abstract

Cells adapt to different conditions via gene expression that tunes metabolism and stress resistance for maximal fitness. Constraints on cellular proteome may limit such expression strategies and introduce trade-offs; Resource allocation under proteome constraints has emerged as a powerful paradigm to explain 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 the lactic acid bacterium Lactococcus lactis. Here we present an approach to identify preferred nutrients from integration of experimental data with a proteome-constrained genome-scale metabolic model of L. lactis (pcLactis), which explicitly accounts for gene expression processes and associated constraints. Using glucose-limited chemostat data, we identified the uptake of glucose and arginine as dominant constraints, whose pathway proteins were indeed upregulated in evolved mutants. However, above a growth rate of 0.5 h-1, pcLactis suggests that available enzymes function at their maximum capacity, which allows an increase in growth rate only by altering gene expression to change metabolic fluxes, as was mainly observed for arginine metabolism. Thus, our integrative analysis of flux and proteomics data with a proteome-constrained model is able to identify and explain the constraints that form targets of regulation and fitness improvement in nutrient-rich growth environments.

Growth-Rate Dependent And Nutrient-Specific Gene Expression Resource Allocation In Fission Yeast

“…Our data provide a rich resource to constrain future genome-scale models of fission yeast that integrate metabolism and gene expression, which will allow testing this hypothesis (O’Brien et al 2013; Sánchez et al 2017; Y. Chen et al 2020).…”

“…Resource allocation constraints have been successfully introduced into genome-wide metabolic models of several organisms as more high-quality expression data has become available (O’Brien et al 2013; Sánchez et al 2017; Y. Chen et al 2020). In summary, quantitative surveys of the gene expression cost of metabolic pathways are key to understanding cell physiology.…”

SALARECON connects the Atlantic salmon genome to growth and feed efficiency

“…To simulate growth limitations from protein synthesis rather than energy generation, we also allowed unlimited uptake of glucose. We multiplied molecular mass with reduced cost in the optimal solution for each amino acid exchange reaction and identified the one with largest negative value as limiting 49 . To supplement the feed with the limiting amino acid, we set the bounds of its exchange reaction to only allow import and penalized supplementation by adding the exchange reaction to the objective with coefficient equal to molecular mass.…”

Manganese modulates metabolic activity and redox homeostasis in translationally-blockedLactococcus cremoris, impacting metabolic persistence, cell-culturability, and flavor formation

“…Proteome samples were harvested in quadruplicates from exponentially growing cultures of strain NCDO712 that was precultured for 20 generations in the presence or absence of manganese. Protein extraction and analysis were performed as described previously (25). Details and modifications to the proteomics methods can be found in Supplementary Methods 4.…”