Metabolic engineering challenges in the post-genomic era

Potential Natural Food Preservatives and Their Sustainable Production in Yeast: Terpenoids and Polyphenols

Terpenoids and polyphenols are high-valued plant secondary metabolites. Their high antimicrobial activities demonstrate their huge potential as natural preservatives in the food industry. With the rapid development of metabolic engineering, it has become possible to realize large-scale production of non-native terpenoids and polyphenols by using the generally recognized as safe (GRAS) strain, Saccharomyces cerevisiae, as a cell factory. This review will summarize the major terpenoid and polyphenol compounds with high antimicrobial properties, describe their native metabolic pathways as well as antimicrobial mechanisms, and highlight current progress on their heterologous biosynthesis in S. cerevisiae. Current challenges and perspectives for the sustainable production of terpenoid and polyphenol as natural food preservatives via S. cerevisiae will also be discussed.

Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs.

Enhancement of acetoin production in Candida glabrata by in silico-aided metabolic engineering

BACKGROUND

Acetoin is a promising chemical compound that can potentially serve as a high value-added platform for a broad range of applications. Many industrial biotechnological processes are moving towards the use of yeast as a platform. The multi-auxotrophic yeast, Candida glabrata, can accumulate a large amount of pyruvate, but produces only trace amounts of acetoin. Here, we attempted to engineer C. glabrata to redirect the carbon flux of pyruvate to increase acetoin production.

RESULTS

Based on an in silico strategy, a synthetic, composite metabolic pathway involving two distinct enzymes, acetolactate synthase (ALS) and acetolactate decarboxylase (ALDC), was constructed, leading to the accumulation of acetoin in C. glabrata. Further genetic modifications were introduced to increase the carbon flux of the heterologous pathway, increasing the production of acetoin to 2.08 g/L. Additionally, nicotinic acid was employed to regulate the intracellular NADH level, and a higher production of acetoin (3.67 g/L) was obtained at the expense of 2,3-butanediol production under conditions of a lower NADH/NAD+ ratio.

CONCLUSION

With the aid of in silico metabolic engineering and cofactor engineering, C. glabrata was designed and constructed to improve acetoin production.

Genome-shuffling improved acid tolerance and l-lactic acid volumetric productivity in Lactobacillus rhamnosus

Genome shuffling is an efficient approach for the rapid improvement of industrially important microbial phenotypes. Here we improved the acid tolerance and volumetric productivity of an industrial strain Lactobacillus rhamnosus ATCC 11443 by genome shuffling. Five strains with subtle improvements in pH tolerance and volumetric productivity were obtained from the populations generated by ultraviolet irradiation and nitrosoguanidine mutagenesis, and then they were subjected for recursive protoplast fusion. A library that was more likely to yield positive colonies was created by fusing the lethal protoplasts obtained from both ultraviolet irradiation and heat treatments. After three rounds of genome shuffling, four strains that could grow at pH 3.6 were obtained. We observed 3.1- and 2.6-fold increases in lactic acid production and cell growth of the best performing at pH 3.8, respectively. The maximum volumetric productivity was 5.77+/-0.05 g/lh when fermented with 10% glucose under neutralizing condition with CaCO(3), which was 26.5+/-1.5% higher than the wild type.

Characterization of an experimental miniature bioreactor for cellular perturbation studies

A mini bioreactor (3.0 mL volume) has been developed and shown to be a versatile tool for rapidly screening and quantifying the response of organisms on environmental perturbations. The mini bioreactor is essentially a plug flow device transformed into a well-mixed reactor by a recycle flow of the broth. The gas and liquid phases are separated by a silicone membrane. Dynamic mass transfer experiments were performed to determine the mass transfer capacities for oxygen and carbon dioxide. The mass transfer coefficients for oxygen and carbon dioxide were found to be 1.55 +/- 0.17 x 10(-5) m/s and 4.52 +/- 0.60 x 10(-6) m/s, respectively. Cultivation experiments with the 3.0 mL bioreactor show that (i) it can maintain biomass in the same physiological state as the 4.0 L lab scale bioreactor, (ii) reproducible perturbation experiments such as changing substrate uptake rate can be readily performed and the physiological response monitored quantitatively in terms of the O2 and CO2 uptake and production rates.