From carbon dioxide to starch: no plants required: Many plants turn glucose from photosynthesis into polymers that form insoluble starch granules ideal for long-term energy storage in roots and seeds. Cai et al . developed a hybrid system in which carbon dioxide is reduced to methanol by an inorganic catalyst and then converted by enzymes first to three and six carbon sugar units and then to polymeric starch. This artificial starch anabolic pathway relies on engineered recombinant enzymes from many different source organisms and can be tuned to produce amylose or amylopectin at excellent rates and efficiencies relative to other synthetic carbon fixation systems—and, depending on the metric used, even to field crops. —MAF

Development of hyperproducing strains is important for biomanufacturing of biochemicals and biofuels but requires extensive efforts to engineer cellular metabolism and discover functional components. Herein, we optimize and use the CRISPR-assisted editing and CRISPRi screening methods to convert a wild-typeCorynebacterium glutamicumto a hyperproducer ofl-proline, an amino acid with medicine, feed, and food applications. To facilitatel-proline production, feedback-deregulated variants of key biosynthetic enzyme γ-glutamyl kinase are screened using CRISPR-assisted single-stranded DNA recombineering. To increase the carbon flux towardsl-proline biosynthesis, flux-control genes predicted by in silico analysis are fine-tuned using tailored promoter libraries. Finally, an arrayed CRISPRi library targeting all 397 transporters is constructed to discover anl-proline exporter Cgl2622. The final plasmid-, antibiotic-, and inducer-free strain producesl-proline at the level of 142.4 g/L, 2.90 g/L/h, and 0.31 g/g. The CRISPR-assisted strain development strategy can be used for engineering industrial-strength strains for efficient biomanufacturing.

Vitamin B₆ is an essential nutrient with extensive applications in the medicine, food, animal feed, and cosmetics industries. Pyridoxine (PN), the most common commercial form of vitamin B₆, is currently chemically synthesized using expensive and toxic chemicals. However, the low catalytic efficiencies of natural enzymes and the tight regulation of the metabolic pathway have hindered PN production by the microbial fermentation process. Here, we report an engineered Escherichia coli strain for PN production. Parallel pathway engineering is performed to decouple PN production and cell growth. Further, protein engineering is rationally designed including the inefficient enzymes PdxA, PdxJ, and the initial enzymes Epd and Dxs. By the iterative multimodule optimization strategy, the final strain produces 1.4 g/L of PN with productivity of 29.16 mg/L/h by fed-batch fermentation. The strategies reported here will be useful for developing microbial strains for the production of vitamins and other bioproducts having inherently low metabolic fluxes.

The development and implementation of microbial chassis cells have profound impacts on circular economy. Non-model bacterium Zymomonas mobilis is an excellent chassis owing to its extraordinary industrial characteristics. Here, the genome-scale metabolic model iZM516 is improved and updated by integrating enzyme constraints to simulate the dynamics of flux distribution and guide pathway design. We show that the innate dominant ethanol pathway of Z. mobilis restricts the titer and rate of these biochemicals. A dominant-metabolism compromised intermediate-chassis (DMCI) strategy is then developed through introducing low toxicity but cofactor imbalanced 2,3-butanediol pathway, and a recombinant D-lactate producer is constructed to produce more than 140.92 g/L and 104.6 g/L D-lactate (yield > 0.97 g/g) from glucose and corncob residue hydrolysate, respectively. Additionally, techno-economic analysis (TEA) and life cycle assessment (LCA) demonstrate the commercialization feasibility and greenhouse gas reduction capability of lignocellulosic D-lactate. This work thus establishes a paradigm for engineering recalcitrant microorganisms as biorefinery chassis.

Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspired by previous studies that take an allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (eciML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli (iML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using eciML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviours under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering.

The genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only considers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experimentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineering targets. Here, we present the first genome-scale enzyme-constrained model (ecCGL1) for Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using a ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of phenotypes and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used the ecCGL1 to identify several gene modification targets for l-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.