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  5. Engineering controllable alteration of malonyl-CoA levels to enhance polyketide production and versatility in E. coli

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Preprint
en
2024

Engineering controllable alteration of malonyl-CoA levels to enhance polyketide production and versatility in E. coli

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en
2024
DOI: 10.21203/rs.3.rs-4165928/v1

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Jay D Keasling
Jay D Keasling

University of California, Berkeley

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Jay D Keasling
Sarah H. Klass
Mia Wesselkamper
+7 more

Abstract

Abstract Engineering the heterologous expression of polyketide synthases (PKSs) in Escherichia coli has led to the production of various valuable natural and unnatural products. However, the limited availability of malonyl-CoA (M-CoA) in E. coli remains a significant impediment to efficient polyketide overproduction. In this study, engineered E. coli strains were developed to augment M-CoA levels for enhanced polyketide production by introducing a malonate transport and CoA ligation pathway. Additionally, control over the M-CoA pool was achieved by disrupting the endogenous M-CoA pathway and reconstituting it through malonate supplementation. The engineered strains exhibited increased M-CoA, fatty acid and polyketide titers, improved substrate selectivity of PKSs, and successful production of > 99% uniformly labeled 13C polyketides. Adaptive laboratory evolution of these strains provided valuable insights into pathways governing the M-CoA pools and the accumulation of mutations that resulted in significantly increased M-CoA and polyketide levels. This approach not only enhances the utility of E. coli as a heterologous host for polyketides but also illuminates the intricate dynamics of M-CoA modulation in these systems.

How to cite this publication

Jay D Keasling, Sarah H. Klass, Mia Wesselkamper, Aidan E. Cowan, Namil Lee, Seokjung Cheong, Zilong Wang, Yan Chen, Jennifer Gin, Christopher J. Petzold (2024). Engineering controllable alteration of malonyl-CoA levels to enhance polyketide production and versatility in E. coli. , DOI: https://doi.org/10.21203/rs.3.rs-4165928/v1.

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Publication Details

Type

Preprint

Year

2024

Authors

10

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.21203/rs.3.rs-4165928/v1

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