Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

Abstract

Sustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated genome-scale models of metabolism and gene expression (ME-models) have the unique ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we use the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME model mostly agree with the standard biomass objective function used in models of metabolism alone (M models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of prebiotic amino acids in the proteins used to sustain anaerobic growth (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli . Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

Restoration of fitness lost due to dysregulation of the pyruvate dehydrogenase complex is triggered by ribosomal binding site modifications

“…We observed an approximately 4-fold upregulation of this large enzyme complex in the pdhR deletion strain ( Figure 1A ). Unregulated expression of such costly proteome components can limit resources available for other cellular functions and impair the robustness of the metabolic network ( Lloyd et al, 2020 ). We examined this potential trade-off using a genome-scale model of metabolism and macromolecular expression, iJL1678b-ME ( Lloyd et al, 2018 ).…”

Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems

“…This allows predictions that could not be captured with classical models, such as identifying bottlenecks and gene engineering targets as well as biological parameters e.g. , condition-dependent biomass composition [74] , [75] and transcription/translation machinery [74] .…”

Reconstructing organisms in silico: genome-scale models and their emerging applications

“…The ME model formulation can demand that translated proteins are folded, equipped with the proper prosthetic groups, and assembled into protein complexes in order to carry out their enzymatic function. Modeling the proteome in this level of detail inherently provides a robust link between metabolism and the biosynthesis of functional enzyme complexes 126 . ME models therefore enable genome-scale investigations into the cellular response to any dysfunction in protein synthesis or maintenance, such as those that can occur when cells experience stress conditions.…”