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

Abstract

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.

Understanding FBA Solutions under Multiple Nutrient Limitations

“…As an example, we use a genome-scale model of Lactococcus lactis [ 18 ]). This lactic acid bacterium has several amino acid auxotrophies [ 34 , 35 ], and is therefore grown in nutrient-rich media.…”

“…This lactic acid bacterium has several amino acid auxotrophies [ 34 , 35 ], and is therefore grown in nutrient-rich media. Amino acids are not only used for protein synthesis, but are also catabolized to yield energy-equivalents [ 18 , 36 ]. It is therefore hard to explain from first principles why L. lactis limits its uptake of amino acids, even when these are available at unlimited concentrations.…”

“…Besides that the uptake of some components is limited, the experimentally observed uptake rates of some amino acids seem to be higher than needed, so that we must enforce these uptake rates to describe reality (red objectives in Figure 4 e). For example, the forced uptake of arginine induces EM 10073 to produce ATP while ornithine is produced, which is shown to be an efficient ATP yielding conversion when glucose uptake is limiting [ 18 ]. While most of the other arginine consuming EMs use arginine in the same fraction, EM 2866 consumes a very large fraction of the arginine pool and only contributes a little to biomass formation.…”

“… Elementary Modes-based analysis reveals the costs and benefits of all metabolic strategies that constitute the optimal solution of Lactococcus lactis in rich medium. ECMs were enumerated for the active network of a L. lactis genome-scale metabolic model [ 18 ] which was constructed based on measurements from [ 37 ]. ( a – d ) The costs of all the used EMs for the 8 different constraints.…”

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 [53]. To supplement the feed with the limiting amino acid, we set the bounds of its exchange reaction to only allow import, and we penalized supplementation by adding the exchange reaction to the objective with coefficient equal to molecular mass.…”