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Dynamic knockdown of E. coli central metabolism for redirecting fluxes of primary metabolites

Open access
Brockman, Irene M., Prather, Kristala L.J.·2015·Metabolic Engineering

Abstract

Control of native enzyme levels is important when optimizing strains for overproduction of heterologous compounds. However, for many central metabolic enzymes, static knockdown results in poor growth and protein expression. We have developed a strategy for dynamically modulating the abundance of native enzymes within the host cell and applied this to a model system for myo-inositol production from glucose. This system relies on controlled degradation of a key glycolytic enzyme, phosphofructokinase-I (Pfk-I). Through tuning Pfk-I levels, we have been able to develop an E. coli strain with a growth mode close to wild type and a production mode with an increased glucose-6-phosphate pool available for conversion into myo-inositol. The switch to production mode is trigged by inducer addition, allowing yield, titer, and productivity to be managed through induction time. By varying the time of Pfk-I degradation, we were able to achieve a two-fold improvement in yield and titers of myo-inositol.

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Metadata

Authors
Brockman, I. M., & Prather, K. L. J.
Venue
Metabolic Engineering
Publisher
Elsevier BV
Published
2015-03
Volume
28
Pages
104-113
ISSN
1096-7176 (ISSN-L)
OpenAlex
W2005454774
Cited by
164
References
3
FWCI
4.02
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Dynamic knockdown of E. coli central metabolism for redirecting fluxes of primary metabolites. https://app.cochat.ai/papers/10.1016/j.ymben.2014.12.005
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Brockman, I. M., & Prather, K. L. J. (2015). Dynamic knockdown of E. coli central metabolism for redirecting fluxes of primary metabolites. Metabolic Engineering. https://doi.org/10.1016/j.ymben.2014.12.005

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.