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Reserve Flux Capacity in the Pentose Phosphate Pathway Enables Escherichia coli’s Rapid Response to Oxidative Stress
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To counteract oxidative stress and reactive oxygen species (ROS), bacteria evolved various mechanisms, primarily reducing ROS through antioxidant systems that utilize cofactor NADPH. Cells must stabilize NADPH levels by increasing flux through replenishing metabolic pathways like pentose phosphate (PP) pathway. Here, we investigate the mechanism enabling the rapid increase in NADPH supply by exposing Escherichia coli to hydrogen peroxide and quantifying the immediate metabolite dynamics. To systematically infer active regulatory interactions governing this response, we evaluated ensembles of kinetic models of glycolysis and PP pathway, each with different regulation mechanisms. Besides the known inactivation of glyceraldehyde 3-phosphate dehydrogenase by ROS, we reveal the important allosteric inhibition of the first PP pathway enzyme by NADPH. This NADPH feedback inhibition maintains a below maximum-capacity PP pathway flux under non-stress conditions. Relieving this inhibition instantly increases PP pathway flux upon oxidative stress. We demonstrate that reducing cells' capacity to rapidly reroute their flux through the PP pathway increases their oxidative stress sensitivity.
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“In E. coli, by increasing the abundance of glucose-6-phosphate dehydrogenase, metabolic flux can be diverted to the PP pathway, leading to increased tolerance to ROS [195].”
From The Role of Reactive Species on Innate Immunity (2022) · cites this paper as [195]
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Cites
Construction of Escherichia coli K‐12 in‐frame, single‐gene knockout mutants: the Keio collection
Baba, T., Ara, T., Hasegawa, M., Takai, Y., Okumura, Y., Baba, M., Datsenko, K. A., Tomita, M., Wanner, B. L., & Mori, H. (2006) · Molecular Systems Biology · doi:10.1038/msb4100050 · Cited by 7,858
Free radicals, antioxidants, and nutrition
Fang, Y.-Z., Yang, S., & Wu, G. (2002) · Nutrition · doi:10.1016/s0899-9007(02)00916-4 · Cited by 2,607
Kohen, R., & Nyska, A. (2002) · Toxicologic Pathology · doi:10.1080/01926230290166724 · Cited by 2,178
Imlay, J. A. (2013) · Nature Reviews Microbiology · doi:10.1038/nrmicro3032 · Cited by 1,749
Oxidant signals and oxidative stress
Finkel, T. (2003) · Current Opinion in Cell Biology · doi:10.1016/s0955-0674(03)00002-4 · Cited by 1,393
Kitagawa, M., Ara, T., Arifuzzaman, M., Ioka-Nakamichi, T., Inamoto, E., Toyonaga, H., & Mori, H. (2006) · DNA Research · doi:10.1093/dnares/dsi012 · Cited by 1,388
Anastasiou, D., Poulogiannis, G., Asara, J. M., Boxer, M. B., Jiang, J., Shen, M., Bellinger, G., Sasaki, A. T., Locasale, J. W., Auld, D. S., et al. (2011) · Science · doi:10.1126/science.1211485 · Cited by 1,218
The world of protein acetylation
Drazic, A., Myklebust, L. M., Ree, R., & Arnesen, T. (2016) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2016.06.007 · Cited by 847
The quantitative and condition-dependent Escherichia coli proteome
Schmidt, A., Kochanowski, K., Vedelaar, S. R., Ahrné, E., Volkmer, B., Callipo, L., Knoops, K., Bauer, M., Aebersold, R. H., & Heinemann, M. (2015) · Nature Biotechnology · doi:10.1038/nbt.3418 · Cited by 759
Self-citationGlucose‐6‐phosphate dehydrogenase, NADPH, and cell survival
Stanton, R. C. (2012) · IUBMB Life · doi:10.1002/iub.1017 · Cited by 706
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Metadata
- Authors
- Christodoulou, D., Link, H., Fuhrer, T., Kochanowski, K., Gerosa, L., & Sauer, U.
- Affiliations
- ETH Zurich (CH)
- University of California, San Francisco (US)
- Harvard University (US)
- Corresponding author
- Sauer, Uwe
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 6
- Issue
- 5
- Pages
- 569-578.e7
- ISSN
- 2405-4712 (ISSN-L)
- OpenAlex
- W2801771445
- Cited by
- 214
- References
- 57
- Citations by year
- 2026: 21 · 2025: 23 · 2024: 25 · 2023: 32 · 2022: 32 · 2021: 37 · 2020: 28 · 2019: 14 · 2018: 1
- FWCI
- 7.11
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- SystemsX.ch
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Redox biology and oxidative stress
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- pentose phosphate pathway, oxidative stress response, NADPH, Escherichia coli, hydrogen peroxide, allosteric inhibition, glyceraldehyde-3-phosphate dehydrogenase, metabolic flux, kinetic model, glycolysis, reactive oxygen species, metabolic regulation
- MeSH terms
- Escherichia coli, Glucosephosphate Dehydrogenase, Glycolysis, Hydrogen Peroxide, NADP, Pentose Phosphate Pathway, Reactive Oxygen Species, Oxidative Stress, Escherichia coli Proteins, Metabolic Networks and Pathways, Metabolic Flux Analysis
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