Summary and future perspectives
6.2. Recommendations for future work
Significant improvement has been achieved in microbial production of the FFA through metabolic engineering and synthetic biology with the yield reaching 69% of the theoretical yield in this thesis. To be industrially relevant strain, the TRY (titer, rate, and yield) of FFA production should be improved by additional engineering.
6.2.1. Increasing NADPH pool by controlling carbon flux between EMP and PPP Balance of cofactors and their regeneration are often required as production of desired products is mediated by oxidation-reduction reaction. Improving, balancing, and regenerating the cofactors have been carried out through metabolic engineering such as redirection of metabolic flux toward pentose phosphate pathway (PPP) [142, 143], replacement of central metabolic enzyme generating other cofactors [144], interconversion of NADH and NADPH [145], and protein engineering to change cofactor specificity [146]. Fatty acid formation is an energy-intensive process
that requires eight mole of each ATP, NADH, and NADPH to produce one mole of palmitic acid.
Especially, increasing the NADPH availability increased FFA production [147], therefore, its concentration has been considered one of rate-limiting factors for enhancing FFA production in E. coli.
A strategy to increase intracellular concentration of NADPH is redirecting carbon flux into PPP. Deletion of pgi encoding glucose-6-phosphate isomerase was able to improve [NADPH]/[NADP+] ratio up to 3- and 2-fold from theoretical prediction and experimental data, as compared with wild-type E. coli [143, 148]. However, excess level of NADPH reduced expression of citrate synthase in TCA cycle, glucose uptake, and cell growth [149, 150]. Therefore, other approaches to increase the NADPH availability (by expressing transhydrogenase or altering cofactor specificity of NADH-consuming enzyme) have been carried out [150, 151]. Another strategy is to utilize transhydrogenase, interconverting NADH and NADPH, because 35-45% of NADPH is synthesized by PntAB (membrane-bound transhydrogenase, encoded by pntAB), major route for NADPH synthesis as well as PPP during aerobic growth of E. coli in glucose medium [152].
However, aforementioned engineering might not appropriately respond demand of NADPH depending on cellular physiological state. Recently, genetically encoded biosensors that dynamically regulate expression of target genes are employed to increase production of desired compounds in the presence of cellular signal [153]. Regulating activity of enzyme or pathway by the biosensor could avoid detrimental effects such as reduced cell growth, resulted from permanent overexpression or deletion of genes. Thus, it might be favorable to construct NADPH-sensing biosensor and employ to increase NADPH availability.
Several biosensors that recognize the ratio of NADPH/NADP+ have been successfully developed and used as a tool for high-throughput screening of mutant enzymes or strain from random mutant library [154, 155]. In E. coli, the transcriptional regulator SoxR previously used to construct NADPH biosensor [155]. The [2Fe-2S] cluster in the SoxR is oxidized to [2Fe-2S]2+ under low level of NADPH, resulting in conformational change of the SoxR and regulation of gene expression [156- 159]. Therefore, placing genes such as zwf (encoding NADP+-dependent glucose-6-phosphate dehydrogenase) under the control of SoxR-responsive promoter might dynamically regulate expression level of the genes depending on NADPH availability, resulting in increased NADPH concentration and FFA production.
6.2.2. Developing active expression system in stationary phase
Transition of cell growth from exponential phase to stationary phase leads to a lot of physiological changes such as nutrient uptake, central metabolism, and metabolite synthesis [160].
accumulated guanosine tetraphosphate (ppGpp) that destabilizes the σ70 promoters [161, 162].
Thereby, the ppGpp induces use of other sigma factors and changes gene expression [163, 164].
As a primary metabolites in E. coli, fatty acid and phospholipid are robustly synthesized when cell grows fast. However, expression of genes involved in the synthesis of fatty acid phospholipid is generally decreased in stationary phase by ppGpp-mediated the stringent response.
High level of ppGpp in stationary phase inhibited activity of glycerol-3-phosphate acyltransferase (encoded by plsB, the first committed step in phospholipid biosynthesis) and accumulates acyl-ACP whose accumulation feedback inhibited the activity of ACC, FabH, and FabI [93, 165]. Furthermore, the ppGpp directly inhibited and altered the expression level and activity of fatty acid synthase [94, 166, 167]. It might limit production titer and productivity in stationary phase as shown in Fig. 5.1.
Therefore, designing stable FFA synthesis in stationary phase is necessary for enhancing FFA production.
Replacing the native promoters of fatty acid synthase into stationary phase-responsive promoter might not be efficient engineering because it would reduce phospholipid synthesis in exponential phase. Therefore, additional copy of gene cluster harboring fatty acid synthase (similar manner with MEP pathway in E. coli) under the control of stationary phase active promoter could lead to stable expression of fatty acid synthase: from native gene cluster in exponential phase and from synthetic gene cluster in stationary phase. Further optimization such as regulating RBS strength or altering 5ʹ-UTR might be capable of increasing the FFA production in stationary phase.
6.2.3. Reprogramming metabolic pathway to develop efficient microbial cell factories for efficient FFA production
Most cellular phenotypes are mediated by many genes, leading to multiple genetic manipulations to achieve desired phenotypes. However, most current metabolic engineering rely on simple genetic manipulation such as gene deletion or gene overexpression. Although the manipulation has made progresses in strain improvement, the approach is time-required and labor-intensive process to regulate activity of the target pathway. Furthermore, sequential engineering of the target pathway often has difficulties in obtaining optimal phenotype because of complicated cellular metabolism [168]. Therefore, simultaneous manipulation of expression level of genes involved in target pathway is necessary.
Transcriptional engineering has received a lot of attention to alter cellular phenotype by modifying a behavior of transcriptional factors. Several studies have reported that engineering transcriptional factors, in terms of function or expression level, successfully improves strain’s
performance, resulting in increment in production of desired chemicals or tolerance to stressful condition [168-171]. The main advantage of the transcriptional engineering is to simultaneously reprogram a series of gene transcription. Thus, it has high chance to obtain optimal phenotype that exhibits increased production of desired compounds.
In E. coli, the production of FFA from glucose involves a lot of systems (e.g.
phosphotransferase (PTS) system, glycolysis, pentose phosphate pathway (PPP), TCA cycle, acetyl- CoA carboxylation, and fatty acid synthetic pathway). Genetic engineering of one or two enzyme(s) previously resulted in modest improvement in FFA production. Therefore, simultaneous modification of the systems is required to obtain optimal cellular metabolism, such as altered carbon source metabolism, cofactor regeneration, and precursor availability.
Several transcriptional regulators (such as CRP, Cra, ArcAB, Mlc, CreBC, and PdhR) could be selected to regulate the activity of PTS system, glycolysis, TCA cycle, electron transport system, PPP, and fatty acid degradation. Randomizing the expression level of the regulators could be achieved through introducing random sequences into promoter, RBS, or 5ʹ-UTR. Simultaneous alteration of expression level of the regulators might cause changes in a series of gene expressions and enable multiple modifications at the genomic level. The optimal strain could be selected by using the fatty acid biosensor that recognizes a different level of fatty acid.