» Articles » PMID: 31405984

Energy Metabolism Controls Phenotypes by Protein Efficiency and Allocation

Overview
Specialty Science
Date 2019 Aug 14
PMID 31405984
Citations 54
Authors
Affiliations
Soon will be listed here.
Abstract

Cells require energy for growth and maintenance and have evolved to have multiple pathways to produce energy in response to varying conditions. A basic question in this context is how cells organize energy metabolism, which is, however, challenging to elucidate due to its complexity, i.e., the energy-producing pathways overlap with each other and even intertwine with biomass formation pathways. Here, we propose a modeling concept that decomposes energy metabolism into biomass formation and ATP-producing pathways. The latter can be further decomposed into a high-yield and a low-yield pathway. This enables independent estimation of protein efficiency for each pathway. With this concept, we modeled energy metabolism for and and found that the high-yield pathway shows lower protein efficiency than the low-yield pathway. Taken together with a fixed protein constraint, we predict overflow metabolism in and the Crabtree effect in , meaning that energy metabolism is sufficient to explain the metabolic switches. The static protein constraint is supported by the findings that protein mass of energy metabolism is conserved across conditions based on absolute proteomics data. This also suggests that enzymes may have decreased saturation or activity at low glucose uptake rates. Finally, our analyses point out three ways to improve growth, i.e., increasing protein allocation to energy metabolism, decreasing ATP demand, or increasing activity for key enzymes.

Citing Articles

Modeling for understanding and engineering metabolism.

Nielsen J, Petranovic D QRB Discov. 2025; 6:e11.

PMID: 40070847 PMC: 11894412. DOI: 10.1017/qrd.2025.1.


A thermodynamic bottleneck in the TCA cycle contributes to acetate overflow in .

Shahreen N, Ahn J, Alsiyabi A, Chowdhury N, Shinde D, Chaudhari S mSphere. 2025; 10(1):e0088324.

PMID: 39745366 PMC: 11774044. DOI: 10.1128/msphere.00883-24.


A framework for understanding collective microbiome metabolism.

Huelsmann M, Schubert O, Ackermann M Nat Microbiol. 2024; 9(12):3097-3109.

PMID: 39604625 DOI: 10.1038/s41564-024-01850-3.


Sensitivities in protein allocation models reveal distribution of metabolic capacity and flux control.

van den Bogaard S, Saa P, Alter T Bioinformatics. 2024; 40(12).

PMID: 39558589 PMC: 11631525. DOI: 10.1093/bioinformatics/btae691.


The Warburg Effect is the result of faster ATP production by glycolysis than respiration.

Kukurugya M, Rosset S, Titov D Proc Natl Acad Sci U S A. 2024; 121(46):e2409509121.

PMID: 39514306 PMC: 11573683. DOI: 10.1073/pnas.2409509121.


References
1.
Niebel B, Leupold S, Heinemann M . An upper limit on Gibbs energy dissipation governs cellular metabolism. Nat Metab. 2020; 1(1):125-132. DOI: 10.1038/s42255-018-0006-7. View

2.
McCloskey D, Gangoiti J, King Z, Naviaux R, Barshop B, Palsson B . A model-driven quantitative metabolomics analysis of aerobic and anaerobic metabolism in E. coli K-12 MG1655 that is biochemically and thermodynamically consistent. Biotechnol Bioeng. 2013; 111(4):803-15. DOI: 10.1002/bit.25133. View

3.
Valgepea K, Adamberg K, Nahku R, Lahtvee P, Arike L, Vilu R . Systems biology approach reveals that overflow metabolism of acetate in Escherichia coli is triggered by carbon catabolite repression of acetyl-CoA synthetase. BMC Syst Biol. 2010; 4:166. PMC: 3014970. DOI: 10.1186/1752-0509-4-166. View

4.
Aung H, Henry S, Walker L . Revising the Representation of Fatty Acid, Glycerolipid, and Glycerophospholipid Metabolism in the Consensus Model of Yeast Metabolism. Ind Biotechnol (New Rochelle N Y). 2014; 9(4):215-228. PMC: 3963290. DOI: 10.1089/ind.2013.0013. View

5.
Peebo K, Valgepea K, Maser A, Nahku R, Adamberg K, Vilu R . Proteome reallocation in Escherichia coli with increasing specific growth rate. Mol Biosyst. 2015; 11(4):1184-93. DOI: 10.1039/c4mb00721b. View