Computational identification of adaptive mutants using the VERT system

Engineering Escherichia coli K12 MG1655 to use starch

Background

To attain a sustainable bioeconomy, fuel, or valuable product, production must use biomass as substrate. Starch is one of the most abundant biomass resources and is present as waste or as a food and agroindustry by-product. Unfortunately, Escherichia coli, one of the most widely used microorganisms in biotechnological processes, cannot use starch as a carbon source.

Results

We engineered an E. coli strain capable of using starch as a substrate. The genetic design employed the native capability of the bacterium to use maltodextrins as a carbon source plus expression and secretion of its endogenous α-amylase, AmyA, in an adapted background. Biomass production improved using 35% dissolved oxygen and pH 7.2 in a controlled bioreactor.

Conclusion

The engineered E. coli strain can use starch from the milieu and open the possibility of optimize the process to use agroindustrial wastes to produce biofuels and other valuable chemicals.

Visualization of evolutionary stability dynamics and competitive fitness of Escherichia coli engineered with randomized multigene circuits

Strain engineering for synthetic biology and metabolic engineering applications often requires the expression of foreign proteins that can reduce cellular fitness. In order to quantify and visualize the evolutionary stability dynamics in engineered populations of Escherichia coli , we constructed randomized CMY (cyan-magenta-yellow) genetic circuits with independently randomized promoters, ribosome binding sites, and transcriptional terminators that express cyan fluorescent protein (CFP), red fluorescent protein (RFP), and yellow fluorescent protein (YFP). Using a CMY color system allows for a spectrum of different colors to be produced under UV light since the relative ratio of fluorescent proteins vary between circuits, and this system can be used for the visualization of evolutionary stability dynamics. Evolutionary stability results from 192 evolved populations (24 CMY circuits with 8 replicates each) indicate that both the number of repeated sequences and overall expression levels contribute to circuit stability. The most evolutionarily robust circuit has no repeated parts, lower expression levels, and is about 3-fold more stable relative to a rationally designed circuit. Visualization results show that evolutionary dynamics are highly stochastic between replicate evolved populations and color changes over evolutionary time are consistent with quantitative data. We also measured the competitive fitness of different mutants in an evolved population and find that fitness is highest in mutants that express a lower number of genes (0 and 1 > 2 > 3). In addition, we find that individual circuits with expression levels below 10% of the maximum have significantly higher evolutionary stability, suggesting there may be a hypothetical "fitness threshold" that can be used for robust circuit design.

Adaptive laboratory evolution for strain engineering

Complex phenotypes, such as tolerance to growth inhibitors, are difficult to rationally engineer into industrial model organisms due our poor understanding of their underlying molecular mechanisms. Adaptive evolution circumvents this issue by exploiting the linkage between growth rate and inhibitor resistance to select for mutants with enhanced tolerance. In order to aid experimentalists in the design and execution of adaptive laboratory evolution, we present detailed protocols for batch, continuous, and visualizing evolution in real-time (VERT) approaches to this technique.

Visualizing evolution in real-time method for strain engineering

The adaptive landscape for an industrially relevant phenotype is determined by the effects of the genetic determinants on the fitness of the microbial system. Identifying the underlying adaptive landscape for a particular phenotype of interest will greatly enhance our abilities to engineer more robust microbial strains. Visualizing evolution in real-time (VERT) is a recently developed method based on in vitro adaptive evolution that facilitates the identification of fitter mutants throughout the course of evolution. Combined with high-throughput genomic tools, VERT can greatly enhance the mapping of adaptive landscapes of industrially relevant phenotypes in microbial systems, thereby expanding our knowledge on the parameters that can be used for strain engineering.

Visualizing evolution in real time to determine the molecular mechanisms of n-butanol tolerance in Escherichia coli

Toxicity of products or feedstock components poses a challenge in the biocatalyst-based production of fuels and chemicals. The genetic determinants that are involved in increased resistance to an inhibitor form the adaptive landscape for the phenotype; so in order to engineer more robust biocatalysts, a better understanding of the adaptive landscape is required. Here, we used an adaptive laboratory evolution method called visualizing evolution in real time (VERT) to help map out part of the adaptive landscape of Escherichia coli tolerance to the biofuel n-butanol. VERT enables identification of adaptive events (population expansions triggered by adaptive mutants) via visualization of the relative proportions of different fluorescently-labeled cells. Knowledge of the occurrence of adaptive events allows for a more systematic isolation of adaptive mutants while simultaneously reducing the number of missed adaptive mutants (and the underlying adaptive mechanisms) that result from clonal interference during the course of in vitro evolution. Based on the evolutionary dynamics observed, clonal interference was found to play a significant role in shaping the population structure of E. coli during exposure to n-butanol, and VERT helped to facilitate the isolation of adaptive mutants from the population. We further combined adaptive laboratory evolution with genome shuffling to significantly enhance the desired n-butanol tolerance phenotype. Subsequent transcriptome analysis of the isolated adaptive mutants revealed different mechanisms of n-butanol resistance in different lineages. In one fluorescently-marked subpopulation, members of the Fur regulon were upregulated; which was not observed in the other subpopulation. In addition, genome sequencing of several adaptive mutants revealed the genetic basis for some of the observed transcriptome profiles. We further elucidated the potential role of the iron-related gene in n-butanol tolerance via overexpression and deletion studies and hypothesized that the upregulation of the iron-related genes indirectly led to modifications in the outer membrane, which contributed to enhanced n-butanol tolerance.