Improving designer glycan production in Escherichia coli through model-guided metabolic engineering
Metabolic Engineering Communications, ISSN: 2214-0301, Vol: 9, Page: e00088
2019
- 14Citations
- 75Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations14
- Citation Indexes14
- 14
- CrossRef8
- Captures75
- Readers75
- 75
Article Description
Asparagine-linked ( N -linked) glycosylation is the most common protein modification in eukaryotes, affecting over two-thirds of the proteome. Glycosylation is also critical to the pharmacokinetic activity and immunogenicity of many therapeutic proteins currently produced in complex eukaryotic hosts. The discovery of a protein glycosylation pathway in the pathogen Campylobacter jejuni and its subsequent transfer into laboratory strains of Escherichia coli has spurred great interest in glycoprotein production in prokaryotes. However, prokaryotic glycoprotein production has several drawbacks, including insufficient availability of non-native glycan precursors. To address this limitation, we used a constraint-based model of E. coli metabolism in combination with heuristic optimization to design gene knockout strains that overproduced glycan precursors. First, we incorporated reactions associated with C. jejuni glycan assembly into a genome-scale model of E. coli metabolism. We then identified gene knockout strains that coupled optimal growth to glycan synthesis. Simulations suggested that these growth-coupled glycan overproducing strains had metabolic imbalances that rerouted flux toward glycan precursor synthesis. We then validated the model-identified knockout strains experimentally by measuring glycan expression using a flow cytometric-based assay involving fluorescent labeling of cell surface-displayed glycans. Overall, this study demonstrates the promising role that metabolic modeling can play in optimizing the performance of a next-generation microbial glycosylation platform.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2214030117300160; http://dx.doi.org/10.1016/j.mec.2019.e00088; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85063866701&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/31008057; https://linkinghub.elsevier.com/retrieve/pii/S2214030117300160; https://dx.doi.org/10.1016/j.mec.2019.e00088
Elsevier BV
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