Principles of proteome allocation are revealed using proteomic data and genome-scale models

Abstract

Integrating omics data to refine or make context-specific models is an active field of constraint-based modeling. Proteomics now cover over 95% of the Escherichia coli proteome by mass. Genome-scale models of Metabolism and macromolecular Expression (ME) compute proteome allocation linked to metabolism and fitness. Using proteomics data, we formulated allocation constraints for key proteome sectors in the ME model. The resulting calibrated model effectively computed the “generalist” (wild-type) E. coli proteome and phenotype across diverse growth environments. Across 15 growth conditions, prediction errors for growth rate and metabolic fluxes were 69% and 14% lower, respectively. The sector-constrained ME model thus represents a generalist ME model reflecting both growth rate maximization and “hedging” against uncertain environments and stresses, as indicated by significant enrichment of these sectors for the general stress response sigma factor σS. Finally, the sector constraints represent a general formalism for integrating omics data from any experimental condition into constraint-based ME models. The constraints can be fine-grained (individual proteins) or coarse-grained (functionally-related protein groups) as demonstrated here. This flexible formalism provides an accessible approach for narrowing the gap between the complexity captured by omics data and governing principles of proteome allocation described by systems-level models.

Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth

“…In this respect, cyanobacterial phototrophic growth is an ideal test case because the regular and periodic environmental changes allow us to formulate the global resource allocation problem in a welldefined way. As yet, similar efforts to investigate proteome allocation have primarily focused on heterotrophic organisms in time-independent environments (21,24,41). These recent analyses reveal several interesting differences with respect to our results.…”

“…In particular, the model-derived (maximal) growth rates for Escherichia coli corresponding to an optimally allocated proteome were consistently higher than the experimentally measured rates. In the model, the latter could be supported with 95% less proteome under certain conditions (21). This finding was attributed to cellular "bet hedging" in (generalist) wild-type E. coli against unknown environmental challenges.…”

“…This finding was attributed to cellular "bet hedging" in (generalist) wild-type E. coli against unknown environmental challenges. Furthermore, significant protein production without detectable growth benefit was observed experimentally (21,41). In contrast, the model-derived maximal growth rates reported here were within the range of the (maximal) growth rates observed for S. elongatus PCC 7942 [even though actual growth rates observed in a laboratory are typically significantly slower (42)].…”

“…To this end, we assemble and numerically evaluate an autocatalytic genome-scale model of cyanobacterial growth, based on a highquality metabolic reconstruction of the cyanobacterium Synechococcus elongatus PCC 7942. Our model significantly improves upon previous computational analyses of diurnal phototrophic growth (14,(16)(17)(18) and takes recent developments in constraintbased analysis into account (19)(20)(21)(22). Our approach is closely related to resource balance analysis (23,24) and dynamic enzymecost flux balance analysis (25), as well as integrated metabolism and gene expression (ME) models (21,26), but explicitly accounts for the properties of diurnal phototrophic growth.…”

Protein allocation and enzymatic constraints explainEscherichia coliwildtype and mutant phenotypes

“…For various steady-state environments, ME models simultaneously simulate maximum growth and substrate uptake rates, the underlying responses on the mRNA level as well as the corresponding gene expression profiles (O'Brien et al, 2013). Thus, ME models facilitate holistic insights into intracellular processes and how they are affected by environmental, biochemical, or genetic perturbations (Yang et al, 2016Chen and Nielsen, 2019), while reliably informing about corresponding flux distributions. However advantageous ME models are for making correct predictions on a flux or phenotypic level, the detail and complexity of the represented network may be cumbersome regarding future applications in strain design approaches.…”