Evaluating proteome allocation of Saccharomyces cerevisiae phenotypes with resource balance analysis

Dinh, Hoang V.; Maranas, Costas D.

doi:10.1016/j.ymben.2023.04.009

Title: Evaluating proteome allocation of Saccharomyces cerevisiae phenotypes with resource balance analysis

Journal Article · 18 April 2023 · Metabolic Engineering

DOI: https://doi.org/10.1016/j.ymben.2023.04.009 · OSTI ID:1993844

Dinh, Hoang V. ^[1];

^[1]

Pennsylvania State University, University Park, PA (United States)

Saccharomyces cerevisiae is an important model organism and a workhorse in bioproduction. Here, we reconstructed a compact and tractable genome-scale resource balance analysis (RBA) model (i.e., named scRBA) to analyze metabolic fluxes and proteome allocation in a computationally efficient manner. Resource capacity models such as scRBA provide the quantitative means to identify bottlenecks in biosynthetic pathways due to enzyme, compartment size, and/or ribosome availability limitations. ATP maintenance rate and in vivo apparent turnover numbers (k_app) were regressed from metabolic flux and protein concentration data to capture observed physiological growth yield and proteome efficiency and allocation, respectively. Estimated parameter values were found to vary with oxygen and nutrient availability. Altogether, this work (i) provides condition-specific model parameters to recapitulate phenotypes corresponding to different extracellular environments, (ii) alludes to the enhancing effect of substrate channeling and post-translational activation on in vivo enzyme efficiency in glycolysis and electron transport chain, and (iii) reveals that the Crabtree effect is underpinned by specific limitations in mitochondrial proteome capacity and secondarily ribosome availability rather than overall proteome capacity.

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Research Organization:: Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0018420

OSTI ID:: 1993844

Journal Information:: Metabolic Engineering, Vol. 77; ISSN 1096-7176

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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