Metabolic engineering is crucial in the development of production strains for platform chemicals, pharmaceuticals and biomaterials from renewable resources. The central carbon metabolism (CCM) of heterotrophs plays an essential role in the conversion of biomass to the cellular building blocks required for growth. Yet, engineering the CCM ultimately aims toward a maximization of flux toward products of interest. The most abundant dissimilative carbohydrate pathways amongst prokaryotes (and eukaryotes) are the Embden-Meyerhof-Parnas (EMP) and the Entner-Doudoroff (ED) pathways, which build the basics for heterotrophic metabolic chassis strains. Although the EMP is regarded as the textbook example of a carbohydrate pathway owing to its central role in production strains like Escherichia coli, Saccharomyces cerevisiae and Bacillus subtilis, it is either modified, complemented or even replaced by alternative carbohydrate pathways in different organisms. The ED pathway also plays key roles in biotechnological relevant bacteria, like Zymomonas mobilis and Pseudomonas putida, and its importance was recently discovered in photoautotrophs and marine microorganisms. In contrast to the EMP, the ED pathway and its variations are not evolutionary optimized for high ATP production and it differs in key principles such as protein cost, energetics and thermodynamics, which can be exploited in the construction of unique metabolic designs. Single ED pathway enzymes and complete ED pathway modules have been used to rewire carbon metabolisms in production strains and for the construction of cell-free enzymatic pathways. This review focuses on the differences of the ED and EMP pathways including their variations and discusses the use of alternative pathway strategies for in vivo and cell-free metabolic engineering.
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Evidence ID | Analyze ID | Gene/Complex | Systematic Name/Complex Accession | Qualifier | Gene Ontology Term ID | Gene Ontology Term | Aspect | Annotation Extension | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Phenotype | Experiment Type | Experiment Type Category | Mutant Information | Strain Background | Chemical | Details | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Disease Ontology Term | Disease Ontology Term ID | Qualifier | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Regulator | Regulator Systematic Name | Target | Target Systematic Name | Direction | Regulation of | Happens During | Regulator Type | Direction | Regulation Of | Happens During | Method | Evidence | Strain Background | Reference |
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Site | Modification | Modifier | Source | Reference |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Allele | Assay | Annotation | Action | Phenotype | SGA score | P-value | Source | Reference | Note |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Assay | Annotation | Action | Modification | Source | Reference | Note |
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Complement ID | Locus ID | Gene | Species | Gene ID | Strain background | Direction | Details | Source | Reference |
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Evidence ID | Analyze ID | Dataset | Description | Keywords | Number of Conditions | Reference |
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