Engineering a short, aldolase-based pathway for (R)-1,3-butanediol production in Escherichia coli.

Microbial processes can produce a wide range of compounds; however, producing complex and long chain hydrocarbons remains a challenge. Aldol condensation offers a direct route to synthesize these challenging chemistries and can be catalyzed by microbes using aldolases. Deoxyribose-5-phosphate aldolase (DERA) condenses aldehydes and/or ketones to ß-hydroxyaldehydes, which can be further converted to value-added chemicals such as a precursor to cholesterol-lowering drugs. Here, we implement a short, aldolase-based pathway in Escherichia coli to produce (R)-1,3-BDO from glucose, an essential component of pharmaceutical products and cosmetics. First, we expressed a three step heterologous pathway from pyruvate to produce 0.3 g/L of (R)-1,3-BDO with a yield of 11.2 mg/g of glucose in wild-type E. coli K12 MG1655. We used a systems metabolic engineering approach to improve (R)-1,3-BDO titer and yield by: 1) identifying and reducing major by-products: ethanol, acetoin, and 2,3-butanediol; 2) increasing pathway flux through DERA to reduce accumulation of toxic acetaldehyde. We then implemented a two-stage fermentation process to improve (R)-1,3-BDO titer by 8-fold to 2.4 g/L and yield by 5-fold to 56 mg/g of glucose (11% of maximum theoretical yield) in strain BD24, by controlling pH to 7 and higher dissolved oxygen level. Furthermore, this study highlights the potential of the aldolase chemistry to synthesize diverse products directly from renewable resources in microbes.

Novel approach to engineer strains for simultaneous sugar utilization.

Use of lignocellulosic biomass as a second generation feedstock in the biofuels industry is a pressing challenge. Among other difficulties in using lignocellulosic biomass, one major challenge is the optimal utilization of both 6-carbon (glucose) and 5-carbon (xylose) sugars by industrial microorganisms. Most industrial microorganisms preferentially utilize glucose over xylose owing to the regulatory phenomenon of carbon catabolite repression (CCR). Microorganisms that can co-utilize glucose and xylose are of considerable interest to the biofuels industry due to their ability to simplify the fermentation processes. However, elimination of CCR in microorganisms is challenging due to the multiple coordinating mechanisms involved. We report a novel algorithm, SIMUP, which finds metabolic engineering strategies to force co-utilization of two sugars, without targeting the regulatory pathways of CCR. Mutants of Escherichia coli based on SIMUP algorithm showed predicted growth phenotypes and co-utilized glucose and xylose; however, consumed the sugars slower than the wild-type. Some solutions identified by the algorithm were based on stoichiometric imbalance and were not obvious from the metabolic network topology. Furthermore, sequencing studies on the genes involved in CCR showed that the mechanism for co-utilization of the sugars could be different from previously known mechanisms.

Sub-optimal phenotypes of double-knockout mutants of Escherichia coli depend on the order of gene deletions.

Metabolic networks are characterized by multiple redundant reactions that do not have a clear biological function. The redundancies in the metabolic networks are implicated in adaptation to random mutations and survival under different environmental conditions. Reactions that are not active under wild-type growth conditions, but get transiently activated after a mutation event such as gene deletion are known as latent reactions. Characterization of multiple-gene knockout mutants can identify the physiological roles of latent reactions. In this study, we characterized double-gene deletion mutants of E. coli with the aim of investigating the sub-optimal physiology of the mutants and the possible roles of latent reactions. Specifically, we investigated the effects of the deletion of the glyoxylate-shunt gene aceA (encoding a latent reaction enzyme, isocitrate lyase) on the growth characteristics of the mutant E. coli Δpgi. The deletion of aceA reduced the growth rate of E. coli Δpgi, indicating that the activation of the glyoxylate shunt plays an important role in adaptation of the mutant E. coli Δpgi when no other latent reactions are concurrently inactivated. We also investigated the effect of the order of the gene deletions on the growth rates and substrate uptake rates of the double-gene deletion mutants. The results indicate that the order in which genes are deleted determines the phenotype of the mutants during the sub-optimal growth phase. To elucidate the mechanism behind the difference between the observed phenotypes, we carried out transcriptomic analysis and constraint-based modeling of the mutants. Transcriptomic analysis showed differential expression of the gene aceK (encoding the protein isocitrate dehydrogenase kinase) involved in controlling the isocitrate flux through the TCA cycle and the glyoxylate shunt. Higher acetate production in the E. coli ΔaceA1 Δpgi2 mutant was consistent with the increased aceK expression, which limits the TCA cycle flux and causes acetate production via overflow metabolism.

Metabolic model refinement using phenotypic microarray data.

Phenotypic microarray (PM) is a standardized, high-throughput technology for profiling phenotypes of microorganisms, which allows for characterization on around 2,000 different media conditions. The data generated using PM can be incorporated into genome-scale metabolic models to improve their predictive capability. In addition, a comparison of phenotypic profiles of wild-type and gene knockout mutants can give essential information about gene functions of unknown genes. In this chapter, we present a protocol to refine preconstructed metabolic models using the PM data. Both manual refinement and algorithmic approaches for integrating the PM data into metabolic models have been discussed.