Unknown

Dataset Information

0

A dynamic pathway analysis approach reveals a limiting futile cycle in N-acetylglucosamine overproducing Bacillus subtilis.


ABSTRACT: Recent advances in genome engineering have further widened the gap between our ability to implement essentially any genetic change and understanding the impact of these changes on cellular function. We lack efficient methods to diagnose limiting steps in engineered pathways. Here, we develop a generally applicable approach to reveal limiting steps within a synthetic pathway. It is based on monitoring metabolite dynamics and simplified kinetic modelling to differentiate between putative causes of limiting product synthesis during the start-up phase of the pathway with near-maximal rates. We examine the synthetic N-acetylglucosamine (GlcNAc) pathway in Bacillus subtilis and find none of the acetyl-, amine- or glucose-moiety precursors to limit synthesis. Our dynamic metabolomics approach predicts an energy-dissipating futile cycle between GlcNAc6P and GlcNAc as the primary problem in the pathway. Deletion of the responsible glucokinase more than doubles GlcNAc productivity by restoring healthy growth of the overproducing strain.

SUBMITTER: Liu Y 

PROVIDER: S-EPMC5512609 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

altmetric image

Publications

A dynamic pathway analysis approach reveals a limiting futile cycle in N-acetylglucosamine overproducing Bacillus subtilis.

Liu Yanfeng Y   Link Hannes H   Liu Long L   Du Guocheng G   Chen Jian J   Sauer Uwe U  

Nature communications 20160621


Recent advances in genome engineering have further widened the gap between our ability to implement essentially any genetic change and understanding the impact of these changes on cellular function. We lack efficient methods to diagnose limiting steps in engineered pathways. Here, we develop a generally applicable approach to reveal limiting steps within a synthetic pathway. It is based on monitoring metabolite dynamics and simplified kinetic modelling to differentiate between putative causes of  ...[more]

Similar Datasets

| S-EPMC3133301 | biostudies-literature
| S-EPMC6318901 | biostudies-literature
| S-EPMC3514339 | biostudies-literature
| S-EPMC3405057 | biostudies-literature
2012-05-25 | GSE34505 | GEO
| S-EPMC2248758 | biostudies-literature
| S-EPMC3650552 | biostudies-literature
| S-EPMC4521046 | biostudies-literature
2015-01-26 | GSE65272 | GEO
| S-EPMC1428398 | biostudies-literature