Dietary Change Enables Robust Growth-Coupling of Heterologous Methyltransferase Activity in Yeast.

Genetic modifications of living organisms and proteins are made possible by a catalogue of molecular and synthetic biology tools, yet proper screening assays for genetic variants of interest continue to lag behind. Synthetic growth-coupling (GC) of enzyme activities offers a simple, inexpensive way to track such improvements. In this follow-up study we present the optimization of a recently established GC design for screening of heterologous methyltransferases (MTases) and related pathways in the yeast Saccharomyces cerevisiae. Specifically, upon testing different media compositions and genetic backgrounds, improved GC of different heterologous MTase activities is obtained. Furthermore, we demonstrate the strength of the system by screening a library of catechol O-MTase variants converting protocatechuic acid into vanillic acid. We demonstrated high correlation (R2 = 0.775) between vanillic acid and cell density as a proxy for MTase activity. We envision that the improved MTase GC can aid evolution-guided optimization of biobased production processes for methylated compounds with yeast in the future.

OptCouple: Joint simulation of gene knockouts, insertions and medium modifications for prediction of growth-coupled strain designs.

Biological production of chemicals is an attractive alternative to petrochemical-based production, due to advantages in environmental impact and the spectrum of feasible targets. However, engineering microbial strains to overproduce a compound of interest can be a long, costly and painstaking process. If production can be coupled to cell growth it is possible to use adaptive laboratory evolution to increase the production rate. Strategies for coupling production to growth, however, are often not trivial to find. Here we present OptCouple, a constraint-based modeling algorithm to simultaneously identify combinations of gene knockouts, insertions and medium supplements that lead to growth-coupled production of a target compound. We validated the algorithm by showing that it can find novel strategies that are growth-coupled in silico for a compound that has not been coupled to growth previously, as well as reproduce known growth-coupled strain designs for two different target compounds. Furthermore, we used OptCouple to construct an alternative design with potential for higher production. We provide an efficient and easy-to-use implementation of the OptCouple algorithm in the cameo Python package for computational strain design.

Selecting the Best: Evolutionary Engineering of Chemical Production in Microbes.

Microbial cell factories have proven to be an economical means of production for many bulk, specialty, and fine chemical products. However, we still lack both a holistic understanding of organism physiology and the ability to predictively tune enzyme activities in vivo, thus slowing down rational engineering of industrially relevant strains. An alternative concept to rational engineering is to use evolution as the driving force to select for desired changes, an approach often described as evolutionary engineering. In evolutionary engineering, in vivo selections for a desired phenotype are combined with either generation of spontaneous mutations or some form of targeted or random mutagenesis. Evolutionary engineering has been used to successfully engineer easily selectable phenotypes, such as utilization of a suboptimal nutrient source or tolerance to inhibitory substrates or products. In this review, we focus primarily on a more challenging problem-the use of evolutionary engineering for improving the production of chemicals in microbes directly. We describe recent developments in evolutionary engineering strategies, in general, and discuss, in detail, case studies where production of a chemical has been successfully achieved through evolutionary engineering by coupling production to cellular growth.

Systems biology solutions for biochemical production challenges.

There is an urgent need to significantly accelerate the development of microbial cell factories to produce fuels and chemicals from renewable feedstocks in order to facilitate the transition to a biobased society. Methods commonly used within the field of systems biology including omics characterization, genome-scale metabolic modeling, and adaptive laboratory evolution can be readily deployed in metabolic engineering projects. However, high performance strains usually carry tens of genetic modifications and need to operate in challenging environmental conditions. This additional complexity compared to basic science research requires pushing systems biology strategies to their limits and often spurs innovative developments that benefit fields outside metabolic engineering. Here we survey recent advanced applications of systems biology methods in engineering microbial production strains for biofuels and -chemicals.