The production of chemicals in biological hosts has attracted significant academic and commercial interest in the past decades due to fossil fuels depletion and the necessity for a sustainable planet. Research has focused not only on low cost fuels, but also on value-added chemicals. Although there are many microorganisms naturally producing biochemicals, genetic engineering is typically applied to improve their performance.
In metabolic engineering, the target is to eliminate competing by-products in order to increase the yield of the desired product. However, this increase usually comes at the expense of reduced growth rate and in some cases “sick” strains grow very poorly. In addition, expression of non-native pathways in microbial hosts is commonly used in the production of fuels, vitamins, proteins and pharmaceuticals. However, the cost of inducers for non-native pathways in large-scale processes can be significant and elimination of this step could be crucial for economic viability. The work presented here shows some preliminary results towards the optimization of a bioprocess when growth impairment is the bottleneck of the designed strain.
The production of chemicals as the outcome of metabolism is in conflict with the organism’s natural tendency for growth. This trade-off between growth and product formation requires further optimization to improve bioprocess economics.
Ideally, it is optimal to maximize the biomass formation in the first phase of a batch to achieve high density of the biocatalyst as fast as possible. Then, in the second phase, most of the carbon can be rerouted towards the desired product. These distinct phases have a significant impact on the productivity and yield of a process. Productivity depends on the amount of catalyst (i.e. biomass) and is optimized in the first phase. High yield is achieved by deleting pathways leading to competing by-products in the second phase.
In order to achieve this scheme, we proposed the dynamic control of gene expression for those pathways contributing to growth at the expense of byproducts formation (Anesiadis et al. 2008). Dynamic control was applied by placing these genes under the control of the toggle switch, a bistable genetic controller (Gardner et al. 2000 and Kobayashi et al. 2004). Genes supporting growth are expressed initially leading to a fast growth phase, followed by repression of these genes leading to a high production phase.
Gene repression is IPTG-inducible, however the ultimate goal is to couple the genetic controller with a quorum sensing system and tune the genetic circuit in order to apply repression of the genes at the optimal time. The programming and tuning of such a process is expected to eliminate the need for inducer, thus decreasing the operational cost.
Here, we evaluate the dynamic method for the production of lactic acid in the MG1655 Δ(adhE pta) mutant under anaerobic conditions (Fong et al. 2005). The strain was transformed with plasmid pTOG(pta) carrying the pta gene on the genetic toggle switch background. A mutant carrying plasmid pTOG(gfp) served as the control. Expression of pta and gfp was induced by heat shock at 42oC for 30 min at the beginning of the fermentation. Repression was achieved by the addition of isopropyl-β-D-thiogalactopyranoside (IPTG) to a concentration of 2 mM. The strains were grown in mineral medium supplemented with 2 g/L glucose, 0.5 g/L yeast extract and 100 mg/L ampicillin in all stages of the inoculum preparation and the final characterization. Optical density was measured at 600 nm in a Jenway 6320D spectrometer. Glucose and lactate were measured with a Shodex RI detector using an Aminex HPX-87X ion exchange column at 25oC with 0.5 and 8.5 mM H2SO4 as the mobile phase, respectively.
Time profiles of optical density and lactate concentration were obtained for triplicate experiments and one standard deviation was considered. IPTG was added after 5 hr.
The cell density confirms that the cells expressing pta grow faster than the control cells carrying the pTOG(gfp) plasmid. As a result of higher growth rate, lactate was produced at a higher titer. The final lactate concentration of the dynamic strategy is approximately 10% higher than the control (14.9 compared to 13.3 mM of lactate at the end of the fermentation).
These results show the proof of concept proposed in our earlier paper (Anesiadis et al. 2008). However, optimization of this method is required. The repression time (i.e. the time of IPTG addition) is one parameter that could be optimized. The next challenging step is to couple the pTOG(pta) plasmid with a quorum sensing system in order to repress pta expression in an inducer-free process.
The novelty of the method arises from the dynamic expression of otherwise deleted genes that contribute to growth rate and productivity. This method is expected to be significant for improving the applicability of engineered strains that have low growth rate.
Anesiadis N., Cluett W.R. & Mahadevan R. (2008) Dynamic metabolic engineering for increasing bioprocess productivity. Metab. Eng. 10: 255-266.
Fong S., Burgard A.P., Herring C.D., Knight E.M., Blattner F.R., Maranas, C.D. & Palsson B.O. (2005) In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol. Bioeng. 91: 643-648.
Gardner T.S., Cantor C.R. & Collins J.J. (2000) Construction of a genetic toggle switch in Escherichia coli. Nature. 403: 339-342.
Kobayashi H., Kærn M., Araki M., Chung K., Gardner T.S., Cantor C.R. & Collins J.J. (2004) Proc. Natl. Acad. Sci. 101: 8414-8419.