Genome-Scale Metabolic Modeling of Escherichia coli and Its Chassis Design for Synthetic Biology Applications.

Genome-scale metabolic modeling is and will continue to play a central role in computational systems metabolic engineering and synthetic biology applications for the productions of chemicals and antibiotics. To that end, a survey and workflows of methods used for the development of high-quality genome-scale metabolic models (GEMs) and chassis design for synthetic biology are described here. The chapter consists of two parts (a) the methods of development of GEMs (Escherichia coli as a case study) and (b) E. coli chassis design for synthetic production of 1,4-butanediol (BDO). The methods described here can guide existing and future development of GEMs coupled with host chassis design for synthetic productions of novel antibiotics.

Theoretical analyses on enantiospecificity of L-2-haloacid dehalogenase (DehL) from Rhizobium sp. RC1 towards 2-chloropropionic acid.

Dehalogenases continue to garner interest of the scientific community due to their potential applications in bioremediation of halogen-contaminated environment and in synthesis of various industrially relevant products. Example of such enzymes is DehL, an L-2-haloacid dehalogenase (EC 3.8.1.2) from Rhizobium sp. RC1 that catalyses the specific cleavage of halide ion from L-2-halocarboxylic acids to produce the corresponding D-2-hydroxycarboxylic acids. Recently, the catalytic residues of DehL have been identified and its catalytic mechanism has been fully elucidated. However, the enantiospecificity determinants of the enzyme remain unclear. This information alongside a well-defined catalytic mechanism are required for rational engineering of DehL for substrate enantiospecificity. Therefore, using quantum mechanics/molecular mechanics and molecular mechanics Poisson-Boltzmann surface area calculations, the current study theoretically investigated the molecular basis of DehL enantiospecificity. The study found that R51L mutation cancelled out the dehalogenation activity of DehL towards it natural substrate, L-2-chloropropionate. The M48R mutation, however introduced a new activity towards D-2-chloropropionate, conveying the possibility of inverting the enantiospecificity of DehL from L-to d-enantiomer with a minimum of two simultaneous mutations. The findings presented here will play important role in the rational design of DehL dehalogenase for improving substrate utility.

Genome-scale metabolic models as platforms for identification of novel genes as antimicrobial drug targets.

The growing number of multidrug-resistant pathogenic bacteria is becoming a world leading challenge for the scientific community and for public health. However, advances in high-throughput technologies and whole-genome sequencing of bacterial pathogens make the construction of bacterial genome-scale metabolic models (GEMs) increasingly realistic. The use of GEMs as an alternative platforms will expedite identification of novel unconditionally essential genes and enzymes of target organisms with existing and forthcoming GEMs. This approach will follow the existing protocol for construction of high-quality GEMs, which could ultimately reduce the time, cost and labor-intensive processes involved in identification of novel antimicrobial drug targets in drug discovery pipelines. We discuss the current impact of existing GEMs of selected multidrug-resistant pathogenic bacteria for identification of novel antimicrobial drug targets and the challenges of closing the gap between genome-scale metabolic modeling and conventional experimental trial-and-error approaches in drug discovery pipelines.

Escherichia coli genome-scale metabolic gene knockout of lactate dehydrogenase (ldhA), increases succinate production from glycerol.

Genome-scale metabolic model (GEM) of Escherichia coli has been published with applications in predicting metabolic engineering capabilities on different carbon sources and directing biological discovery. The use of glycerol as an alternative carbon source is economically viable in biorefinery. The use of GEM for predicting metabolic gene deletion of lactate dehydrogenase (ldhA) for increasing succinate production in Escherichia coli from glycerol carbon source remained largely unexplored. Here, I hypothesized that metabolic gene knockout of ldhA in E. coli from glycerol could increase succinate production. A proof-of-principle strain was constructed and designated as E. coli BMS5 (ΔldhA), by predicting increased succinate production in E. coli GEM and confirmed the predicted outcomes using wet cell experiments. The mutant GEM (ΔldhA) predicted 11% increase in succinate production from glycerol compared to its wild-type model (iAF1260), and the E. coli BMS5 (ΔldhA) showed 1.05 g/l and its corresponding wild-type produced .05 g/l (23-fold increase). The proof-of-principle strain constructed in this study confirmed the aforementioned hypothesis and further elucidated the fact that E. coli GEM can prospectively and effectively predict metabolic engineering interventions using glycerol as substrate and could serve as platform for new strain design strategies and biological discovery.

Genome-scale metabolic models as platforms for strain design and biological discovery.

Genome-scale metabolic models (GEMs) have been developed and used in guiding systems' metabolic engineering strategies for strain design and development. This strategy has been used in fermentative production of bio-based industrial chemicals and fuels from alternative carbon sources. However, computer-aided hypotheses building using established algorithms and software platforms for biological discovery can be integrated into the pipeline for strain design strategy to create superior strains of microorganisms for targeted biosynthetic goals. Here, I described an integrated workflow strategy using GEMs for strain design and biological discovery. Specific case studies of strain design and biological discovery using Escherichia coli genome-scale model are presented and discussed. The integrated workflow presented herein, when applied carefully would help guide future design strategies for high-performance microbial strains that have existing and forthcoming genome-scale metabolic models.

Model-guided metabolic gene knockout of gnd for enhanced succinate production in Escherichia coli from glucose and glycerol substrates.

The metabolic role of 6-phosphogluconate dehydrogenase (gnd) under anaerobic conditions with respect to succinate production in Escherichia coli remained largely unspecified. Herein we report what are to our knowledge the first metabolic gene knockout of gnd to have increased succinic acid production using both glucose and glycerol substrates in E. coli. Guided by a genome scale metabolic model, we engineered the E. coli host metabolism to enhance anaerobic production of succinic acid by deleting the gnd gene, considering its location in the boundary of oxidative and non-oxidative pentose phosphate pathway. This strategy induced either the activation of malic enzyme, causing up-regulation of phosphoenolpyruvate carboxylase (ppc) and down regulation of phosphoenolpyruvate carboxykinase (ppck) and/or prevents the decarboxylation of 6 phosphogluconate to increase the pool of glyceraldehyde-3-phosphate (GAP) that is required for the formation of phosphoenolpyruvate (PEP). This approach produced a mutant strain BMS2 with succinic acid production titers of 0.35 g l(-1) and 1.40 g l(-1) from glucose and glycerol substrates respectively. This work further clearly elucidates and informs other studies that the gnd gene, is a novel deletion target for increasing succinate production in E. coli under anaerobic condition using glucose and glycerol carbon sources. The knowledge gained in this study would help in E. coli and other microbial strains development for increasing succinate production and/or other industrial chemicals.

Model-assisted formate dehydrogenase-O (fdoH) gene knockout for enhanced succinate production in Escherichia coli from glucose and glycerol carbon sources.

Succinic acid is an important platform chemical that has broad applications and is been listed as one of the top twelve bio-based chemicals produced from biomass by the US Department of Energy. The metabolic role of Escherichia coli formate dehydrogenase-O (fdoH) under anaerobic conditions in relation to succinic acid production remained largely unspecified. Herein we report, what are to our knowledge, the first metabolic fdoH gene knockout that have enhanced succinate production using glucose and glycerol substrates in E. coli. Using the most recent E. coli reconstruction iJO1366, we engineered its host metabolism to enhance the anaerobic succinate production by deleting the fdoH gene, which blocked H(+) conduction across the mutant cell membrane for the enhanced succinate production. The engineered mutant strain BMS4 showed succinate production of 2.05 g l(-1) (41.2-fold in 7 days) from glycerol and .39 g l(-1) (6.2-fold in 1 day) from glucose. This work revealed that a single deletion of the fdoH gene is sufficient to increase succinate production in E. coli from both glucose and glycerol substrates.

Model-aided atpE gene knockout strategy in Escherichia coli for enhanced succinic acid production from glycerol.

Succinic acid is an important platform chemical with a variety of applications. Model-guided metabolic engineering strategies in Escherichia coli for strain improvement to increase succinic acid production using glucose and glycerol remain largely unexplored. Herein, we report what are, to our knowledge, the first metabolic knockout of the atpE gene to have increased succinic acid production using both glucose and alternative glycerol carbon sources in E. coli. Guided by a genome-scale metabolic model, we engineered the E. coli host to enhance anaerobic production of succinic acid by deleting the atpE gene, thereby generating additional reducing equivalents by blocking H(+) conduction across the mutant cell membrane. This strategy produced 1.58 and .49 g l(-1) of succinic acid from glycerol and glucose substrate, respectively. This work further elucidates a model-guided and/or system-based metabolic engineering, involving only a single-gene deletion strategy for enhanced succinic acid production in E. coli.

In silico deletion of PtsG gene in Escherichia coli genome-scale model predicts increased succinate production from glycerol.

Systems metabolic engineering and in silico analyses are necessary to study gene knockout candidate for enhanced succinic acid production by Escherichia coli. Metabolically engineered E. coli has been reported to produce succinate from glucose and glycerol. However, investigation on in silico deletion of ptsG/b1101 gene in E. coli from glycerol using minimization of metabolic adjustment algorithm with the OptFlux software platform has not yet been elucidated. Herein we report what is to our knowledge the first direct predicted increase in succinate production following in silico deletion of the ptsG gene in E. coli GEM from glycerol with the OptFlux software platform. The result indicates that the deletion of this gene in E. coli GEM predicts increased succinate production that is 20% higher than the wild-type control model. Hence, the mutant model maintained a growth rate that is 77% of the wild-type parent model. It was established that knocking out of the ptsG/b1101 gene in E. coli using glucose as substrate enhanced succinate production, but the exact mechanism of this effect is still obscure. This study informs other studies that the deletion of ptsG/b1101 gene in E. coli GEM predicted increased succinate production, enabling a model-driven experimental inquiry and/or novel biological discovery on the underground metabolic role of this gene in E. coli central metabolism in relation to increasing succinate production when glycerol is the substrate.