Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli

The identification of genetic targets that are effective in bringing about a desired phenotype change is still an open problem. While random gene knockouts have yielded improved strains in certain cases, it is also important to seek the guidance of cell-wide stoichiometric constraints in identifying promising gene knockout targets. To investigate these issues, we undertook a genome-wide stoichiometric flux balance analysis as an aid in discovering putative genes impacting network properties and cellular phenotype. Specifically, we calculated metabolic fluxes such as to optimize growth and then scanned the genome for single and multiple gene knockouts that yield improved product yield while maintaining acceptable overall growth rate. For the particular case of lycopene biosynthesis in Escherichia coli, we identified such targets that we subsequently tested experimentally by constructing the corresponding single, double and triple gene knockouts. While such strains are suggested (by the stoichiometric calculations) to increase precursor availability, this beneficial effect may be further impacted by kinetic and regulatory effects not captured by the stoichiometric model. For the case of lycopene biosynthesis, the so identified knockout targets yielded a triple knockout construct that exhibited a nearly 40% increase over an engineered, high producing parental strain.     

Uncovering the gene knockout landscape for improved lycopene production in E. coli

Systematic and combinatorial genetic approaches for the identification of gene knockout and overexpression targets have been effectively employed in the improvement of cellular phenotypes. Previously, we demonstrated how two of these tools, metabolic modeling and transposon mutagenesis, can be combined to identify strains of interest spanning the metabolic landscape of recombinant lycopene production in Escherichia coli. However, it is unknown how to best select multiple-gene knockout targets. Hence, this study seeks to understand how the overall order of gene selection, or search trajectory, biases the exploration and topology of the metabolic landscape. In particular, transposon mutagenesis and selection were employed in the background of eight different knockout genotypes. Collectively, 800,000 mutants were analyzed in hopes of exhaustively identifying all advantageous gene knockout targets. Several interesting observations, including clusters of gene functions, recurrence, and divergent genotypes, demonstrate the complexity of mapping only one genotype to one phenotype. One particularly interesting mutant, the ΔhnrΔyliE genotype, exhibited a drastically improved lycopene production capacity in basic minimal medium in comparison to the best strains identified in previous studies.     

Computing complex metabolic intervention strategies using constrained minimal cut sets

The model-driven search for gene deletion strategies that increase the production performance of microorganisms is an essential part of metabolic engineering. One theoretical approach is based on Minimal Cut Sets (MCSs) which are minimal sets of knockouts disabling the operation of a specified set of target elementary modes. A limitation of the current approach is that MCSs can induce side effects disabling also desired functionalities. We, therefore, generalize MCSs to Constrained MCSs (cMCSs) allowing for the additional definition of a set of desired modes of which a minimum number must be preserved. Exemplarily for ethanol production by Escherichia coli, we demonstrate that this approach offers enormous flexibility in defining and solving knockout problems. Moreover, many existing methods can be reformulated as special cMCS problems. The cMCSs approach allows systematic enumeration of all equivalent gene deletion combinations and also helps to determine robust knockout strategies for coupled product and biomass synthesis.     

Characterization of lycopene-overproducing E. coli strains in high cell density fermentations

Previous work identified two recombinant strains of Escherichia coli capable of significant lycopene overproduction. These strains were constructed by superimposing the deletion of three genes, selected through combinatorial and systematic searches of the metabolic landscape, onto a previously engineered strain over-expressing critical genes in the lycopene biosynthesis pathway. In this paper, we characterize the performance of these two strains in comparison to the parental, pre-engineered strain. Specifically, high cell density fermentations were performed after identifying optimized putative operating parameters. High oxygen levels and increased pH values were found to be critical for increasing both specific and volumetric product titers. Carbon balances suggest linkages between glutamate, NADPH, formate, and alanine levels with lycopene overproduction. Furthermore, lycopene production reached nearly 220 mg/l from approximately 27 g dry cell weight/l in these reactors, which is the highest value reported to date for E. coli.     

Combinatorial network of transcriptional regulation and microRNA regulation in human cancer

Background

In order to reduce time and efforts to develop microbial strains with better capability of producing desired bioproducts, genome-scale metabolic simulations have proven useful in identifying gene knockout and amplification targets. Constraints-based flux analysis has successfully been employed for such simulation, but is limited in its ability to properly describe the complex nature of biological systems. Gene knockout simulations are relatively straightforward to implement, simply by constraining the flux values of the target reaction to zero, but the identification of reliable gene amplification targets is rather difficult. Here, we report a new algorithm which incorporates physiological data into a model to improve the model??s prediction capabilities and to capitalize on the relationships between genes and metabolic fluxes.

Results

We developed an algorithm, flux variability scanning based on enforced objective flux (FVSEOF) with grouping reaction (GR) constraints, in an effort to identify gene amplification targets by considering reactions that co-carry flux values based on physiological omics data via ??GR constraints??. This method scans changes in the variabilities of metabolic fluxes in response to an artificially enforced objective flux of product formation. The gene amplification targets predicted using this method were validated by comparing the predicted effects with the previous experimental results obtained for the production of shikimic acid and putrescine in Escherichia coli. Moreover, new gene amplification targets for further enhancing putrescine production were validated through experiments involving the overexpression of each identified targeted gene under condition-controlled batch cultivation.

Conclusions

FVSEOF with GR constraints allows identification of gene amplification targets for metabolic engineering of microbial strains in order to enhance the production of desired bioproducts. The algorithm was validated through the experiments on the enhanced production of putrescine in E. coli, in addition to the comparison with the previously reported experimental data. The FVSEOF strategy with GR constraints will be generally useful for developing industrially important microbial strains having enhanced capabilities of producing chemicals of interest.     

Replacing Escherichia coli NAD-dependent glyceraldehyde 3-phosphate dehydrogenase (GAPDH) with a NADP-dependent enzyme from Clostridium acetobutylicum facilitates NADPH dependent pathways

Reactions requiring reducing equivalents, NAD(P)H, are of enormous importance for the synthesis of industrially valuable compounds such as carotenoids, polymers, antibiotics and chiral alcohols among others. The use of whole-cell biocatalysis can reduce process cost by acting as catalyst and cofactor regenerator at the same time; however, product yields might be limited by cofactor availability within the cell. Thus, our study focussed on the genetic manipulation of a whole-cell system by modifying metabolic pathways and enzymes to improve the overall production process. In the present work, we genetically engineered an Escherichia coli strain to increase NADPH availability to improve the productivity of products that require NADPH in its biosynthesis. The approach involved an alteration of the glycolysis step where glyceraldehyde-3-phosphate (GAP) is oxidized to 1,3 bisphophoglycerate (1,3-BPG). This reaction is catalyzed by NAD-dependent endogenous glyceraldehyde-3-phosphate dehydrogenase (GAPDH) encoded by the gapA gene. We constructed a recombinant E. coli strain by replacing the native NAD-dependent gapA gene with a NADP-dependent GAPDH from Clostridium acetobutylicum, encoded by the gene gapC. The beauty of this approach is that the recombinant E. coli strain produces 2 mol of NADPH, instead of NADH, per mole of glucose consumed. Metabolic flux analysis showed that the flux through the pentose phosphate (PP) pathway, one of the main pathways that produce NADPH, was reduced significantly in the recombinant strain when compared to that of the parent strain. The effectiveness of the NADPH enhancing system was tested using the production of lycopene and epsilon-caprolactone as model systems using two different background strains. The recombinant strains, with increased NADPH availability, consistently showed significant higher productivity than the parent strains.     

Combinatorial metabolic pathway assembly in the yeast genome with RNA-guided Cas9

The yeast Saccharomyces cerevisiae is an important industrial platform for the production of grain and cellulosic ethanol, isobutanol, butanediol, isoprenoids, and other chemicals. The construction of a successful production strain usually involves multiple gene knockouts and chromosomal integration of expression cassettes to redirect the metabolic fluxes for the conversion of sugars and other feed stocks into the desired product. RNA-guided Cas9 based genome editing has been demonstrated in many prokaryotic and eukaryotic hosts including S. cerevisiae, in which it has been additionally exploited as a tool for metabolic engineering. To extend the utilization of RNA-guided Cas9 as a metabolic pathway building tool, we demonstrated the direct assembly and chromosomal integration of up to 17 overlapping DNA fragments encoding the beta-carotene biosynthetic pathway. Furthermore, we generated a combinatorial strain library for the beta-carotene biosynthetic pathway, directly integrated into the yeast genome to create a diverse library of strains. This enabled the screening of combinatorial libraries in stable chromosomally integrated strains for rapid improvements of product titers. This combinatorial approach for pathway assembly will significantly accelerate the current speed of metabolic engineering for S. cerevisiae as an industrial platform, and increase the number of strains that can be simultaneously evaluated for enzyme screening, expression optimization and protein engineering to achieve the titer, rate and yield necessary for the commercialization of new industrial fermentation products.     

多质粒共转化组合筛选方法构建木糖利用酿酒酵母的研究

快速得到目标代谢路径相关基因的大量组合以及实现组合库的高效筛选,是合成生物学领域中一个重要的研究内容。建立了三质粒共转化酵母菌株组合筛选方法并以XR-XDH木糖代谢路径在酿酒酵母中的应用为例进行阐释。首先利用Yeast Golden Gate连接法在三种不同表达载体上构建不同启动子控制下的XR、XDH、XK单个基因的表达盒,然后直接用三质粒共转化系统构建100种不同组合的重组酵母。经过木糖平板初筛筛选出16个能利用木糖的组合,将这16个组合对应的三基因表达模块组装至同一表达载体后转化底盘菌株,再通过限氧发酵进行复筛,最终筛选出木糖代谢能力、木糖醇和乙醇生成速率最优菌株Sc-LQH35（TDH3p-XR-ACS2t-FBA1p-XDH-ENO2t-PDC1p-XK-ASC1t）,在培养基中含有20 g/L木糖的条件下,其木糖醇产量为7.14 g/L,乙醇产量为5.92 g/L,而菌株Sc-LQH39（TDH3p-XR-ACS2t-FBA1p-XDH-ENO2t-ZEO1p-XK-ASC1t）则表现出较强的木糖醇生产能力,特别在限氧发酵时,其木糖醇得率可高达0.71 g/g。三质粒共转化组合筛选方法实现了木糖利用菌株的灵活构建和快速筛选,并成功得到具有优良木糖利用性能的酿酒酵母菌株,表明其在重组菌株的构建和筛选工作中有一定的应用价值。     

Effects of introducing heterologous pathways on microbial metabolism with respect to metabolic optimality

Although optimality of microbial metabolism under genetic and environmental perturbations is well studied, the effects of introducing heterologous reactions on the overall metabolism are not well understood. This point is important in the field of metabolic engineering because heterologous reactions are more frequently introduced into various microbial hosts. The genome-scale metabolic simulations of Escherichia coli strains engineered to produce 1,4-butanediol, 1,3-propanediol, and amorphadiene suggest that microbial metabolism shows much different responses to the introduced heterologous reactions in a strain-specific manner than typical gene knockouts in terms of the energetic status (e.g., ATP and biomass generation) and chemical production capacity. The 1,4-butanediol and 1,3-propanediol producers showed greater metabolic optimality than the wild-type strains and gene knockout mutants for the energetic status, while the amorphadiene producer was metabolically less optimal. For the optimal chemical production capacity, additional gene knockouts were most effective for the strain producing 1,3-propanediol, but not for the one producing 1,4-butanediol. These observations suggest that strains having heterologous metabolic reactions have metabolic characteristics significantly different from those of the wild-type strain and single gene knockout mutants. Finally, comparison of the theoretically predicted and ¹³C-based flux values pinpoints pathways with non-optimal flux values, which can be considered as engineering targets in systems metabolic engineering strategies. To our knowledge, this study is the first attempt to quantitatively characterize microbial metabolisms with different heterologous reactions. The suggested potential reasons behind each strain’s different metabolic responses to the introduced heterologous reactions should be carefully considered in strain designs.     

Large-scale bi-level strain design approaches and mixed-integer programming solution techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ～10 days to ～5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering.     

Multi-dimensional gene target search for improving lycopene biosynthesis in Escherichia coli

Identification of multiple gene targets that exhibit different modes of action toward a desired phenotype is a crucial step in strain improvement. Target identification methods based on traceable genetic perturbations and stoichiometric modeling have been employed before for the mining of putative overexpression and knock-out targets. Most search methods are sequential and, as such, quite limited in the space they can explore. In this study, we investigate a multi-dimensional search approach whereby unknown interactions of gene targets identified by different search methods are assessed by employing orthogonal search strategies. To this end, we combined knock-out and overexpression gene targets, identified through systematic and combinatorial approaches, respectively, in order to improve lycopene production in Escherichia coli. Specifically, we first identified multiple overexpression targets by screening genomic libraries of E. coli in a sequential-iterative manner. Targets so identified confirmed previously amplified genes in the non-mevalonate pathway (dxs and idi) and some regulatory genes (rpoS and appY). Additionally, this method revealed novel gene targets (yjiD, ycgW, yhbL, purDH, and yggT). A two-dimensional search was subsequently undertaken, whereby the selected overexpression targets were combined with the knock-out targets predicted by stoichiometric modeling. All combinations of single (rpoS, appY, yjiD, ycgW, and yhbL), double (yjiD-ycgW) and triple (yjiD-ycgW-yhbL) overexpressions with four gene deletion backgrounds, including single (delta gdhA, or delta aceE), double (delta gdhA delta aceE), and triple (delta gdhA delta aceE delta fdhF) knockouts, were constructed and evaluated for lycopene production. Investigation of the metabolic landscape spanned by these 40 strains identified the best-engineered strain (T5(P)-dxs, T5(P)-idi, rrnB(P)-yjiD-ycgW, delta gdh delta aceE delta fdhF, pACLYC), which accumulated 16,000 ppm (16 mg/g cell) of lycopene within 24 h in a batch shake flask with 5 g/L of glucose in M9 minimal medium.     

Systems metabolic engineering: Genome-scale models and beyond

The advent of high throughput genome-scale bioinformatics has led to an exponential increase in available cellular system data. Systems metabolic engineering attempts to use data-driven approaches – based on the data collected with high throughput technologies – to identify gene targets and optimize phenotypical properties on a systems level. Current systems metabolic engineering tools are limited for predicting and defining complex phenotypes such as chemical tolerances and other global, multigenic traits. The most pragmatic systems-based tool for metabolic engineering to arise is the in silico genome-scale metabolic reconstruction. This tool has seen wide adoption for modeling cell growth and predicting beneficial gene knockouts, and we examine here how this approach can be expanded for novel organisms. This review will highlight advances of the systems metabolic engineering approach with a focus on de novo development and use of genome-scale metabolic reconstructions for metabolic engineering applications. We will then discuss the challenges and prospects for this emerging field to enable model-based metabolic engineering. Specifically, we argue that current state-of-the-art systems metabolic engineering techniques represent a viable first step for improving product yield that still must be followed by combinatorial techniques or random strain mutagenesis to achieve optimal cellular systems.     

Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli

Integrated approaches utilizing in silico analyses will be necessary to successfully advance the field of metabolic engineering. Here, we present an integrated approach through a systematic model-driven evaluation of the production potential for the bacterial production organism Escherichia coli to produce multiple native products from different representative feedstocks through coupling metabolite production to growth rate. Designs were examined for 11 unique central metabolism and amino acid targets from three different substrates under aerobic and anaerobic conditions. Optimal strain designs were reported for designs which possess maximum yield, substrate-specific productivity, and strength of growth-coupling for up to 10 reaction eliminations (knockouts). In total, growth-coupled designs could be identified for 36 out of the total 54 conditions tested, corresponding to eight out of the 11 targets. There were 17 different substrate/target pairs for which over 80% of the theoretical maximum potential could be achieved. The developed method introduces a new concept of objective function tilting for strain design. This study provides specific metabolic interventions (strain designs) for production strains that can be experimentally implemented, characterizes the potential for E. coli to produce native compounds, and outlines a strain design pipeline that can be utilized to design production strains for additional organisms.     

Central carbon metabolism influences cellulase production in Bacillus licheniformis

Bacillus licheniformis that can produce cellulase including endo glucanase and glucosidase is an important industrial microbe for cellulose degradation. The purpose of this research was to assess the effect of endo glucanase gene bglC and glucosidase gene bglH on the central metabolic flux in B. licheniformis. bglC and bglH were knocked out using homologous recombination method, respectively, and the corresponding knockout strains were obtained for ¹³C metabolic flux analysis. A significant change was observed in metabolic fluxes after ¹³C metabolic flux ratio analysis. In both of the knockout strains, the increased fluxes of the pentose phosphate pathway and malic enzyme reaction enabled an elevated supply of NADPH which provided enough reducing power for the in vivo synthesis reactions. The fluxes through tricarboxylic acid cycle and anaplerotic reactions increased fast in the two knockout strains, which meant more energy generated. The changed fluxes in central carbon metabolism provided a holistic view of the physiological status in B. licheniformis and possible targets for further strain engineering.

Significance and Impact of the Study

Cellulase is very important in the field of agriculture and bioenergy because of its degrading effect on cellulosic biomass. This study presented the effect of central carbon metabolism on cellulase production in Bacillus licheniformis. The study also provided a holistic view of the physiological status in B. licheniformis. The shifted metabolism provided a quantitative evaluation of the biosynthesis of cellulase and a priority ranked target list for further strain engineering.     

Systematic identification and elimination of flux bottlenecks in the aldehyde production pathway of Synechococcus elongatus PCC 7942

Isotopically nonstationary metabolic flux analysis (INST-MFA) provides a versatile platform to quantitatively assess in vivo metabolic activities of autotrophic systems. By applying INST-MFA to recombinant aldehyde-producing cyanobacteria, we identified metabolic alterations that correlated with increased strain performance in order to guide rational metabolic engineering. We identified four reactions adjacent to the pyruvate node that varied significantly with increasing aldehyde production: pyruvate kinase (PK) and acetolactate synthase (ALS) fluxes were directly correlated with product formation, while pyruvate dehydrogenase (PDH) and phosphoenolpyruvate carboxylase (PPC) fluxes were inversely correlated. Overexpression of enzymes for PK or ALS did not result in further improvements to the previous best-performing strain, while downregulation of PDH expression (through antisense RNA expression) or PPC flux (through expression of the reverse reaction, phosphoenolpyruvate carboxykinase) provided significant improvements. These results illustrate the potential of INST-MFA to enable a systematic approach for iterative identification and removal of pathway bottlenecks in autotrophic host cells.     

Contribution of cell surface hydrophobicity protein 1 (Csh1p) to virulence of hydrophobic Candida albicans serotype A cells

The CSH1 gene product is the first protein implicated to affect the phenotype of cell surface hydrophobicity in Candida albicans. Ablation of expression of CSH1 resulted in a 75% loss of the cell surface hydrophobicity (CSH) phenotype. When the C. albicans csh1 knockout derivative was cultured from frozen stocks, it had reacquired CSH levels similar to the parent strain and isogenic reintegrant in the absence of Csh1p re-expression through an unknown mechanism. Prior to reacquisition of CSH, the knockout was less adherent to fibronectin than the parent. Comparison of the csh1 knockout and CSH1 reintegrant in a hematogenous dissemination model allows analysis of Csh1p contribution to virulence using matched strains with similar levels of CSH. No statistical significance between the knockout and reintegrant was found in virulence based on median day of survival, although a reproducible delay in onset of lethal infection for the knockout was observed. A modest difference in mucosal colonization in a vaginal infection model was also observed between the knockout and reintegrant. The present study demonstrates that Csh1p contributes to virulence of C. albicans in mice, but other gene products also contribute to the CSH phenotype and virulence.     

Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA

Malonyl-coenzyme A is an important precursor metabolite for the biosynthesis of polyketides, flavonoids and biofuels. However, malonyl-CoA naturally synthesized in microorganisms is consumed for the production of fatty acids and phospholipids leaving only a small amount available for the production of other metabolic targets in recombinant biosynthesis. Here we present an integrated computational and experimental approach aimed at improving the intracellular availability of malonyl-CoA in Escherichia coli. We used a customized version of the recently developed OptForce methodology to predict a minimal set of genetic interventions that guarantee a prespecified yield of malonyl-CoA in E. coli strain BL21 Star™. In order to validate the model predictions, we have successfully constructed an E. coli recombinant strain that exhibits a 4-fold increase in the levels of intracellular malonyl-CoA compared to the wild type strain. Furthermore, we demonstrate the potential of this E. coli strain for the production of plant-specific secondary metabolites naringenin (474 mg/L) with the highest yield ever achieved in a lab-scale fermentation process. Combined effect of the genetic interventions was found to be synergistic based on a developed analysis method that correlates genetic modification to cell phenotype, specifically the identified knockout targets (ΔfumC and ΔsucC) and overexpression targets (ACC, PGK, GAPD and PDH) can cooperatively force carbon flux towards malonyl-CoA. The presented strategy can also be readily expanded for the production of other malonyl-CoA-derived compounds like polyketides and biofuels.     

An inverse metabolic engineering approach for the design of an improved host platform for over-expression of recombinant proteins in Escherichia coli

ABSTRACT: BACKGROUND: A useful goal for metabolic engineering would be to generate non-growing but metabolically active quiescent cells which would divert the metabolic fluxes towards product formation rather than growth. However, for products like recombinant proteins which are intricately coupled to the growth process it is difficult to identify the genes that need to be knocked-out/knocked-in to get this desired phenotype. To circumvent this we adopted an inverse metabolic engineering strategy which would screen for the desired phenotype and thus help in the identification of genetic targets which need to be modified to get overproducers of recombinant protein. Such quiescent cells would obviate the need for high cell density cultures and increase the operational life span of bioprocesses. RESULTS: A novel strategy for generating a library consisting of randomly down regulated metabolic pathways in E. coli was designed by cloning small genomic DNA fragments in expression vectors. Some of these DNA fragments got inserted in the reverse orientation thereby generating anti-sense RNA upon induction. These anti-sense fragments would hybridize to the sense mRNA of specific genes leading to gene 'silencing'. This library was first screened for slow growth phenotype and subsequently for enhanced over-expression ability. Using Green Fluorescent Protein (GFP) as a reporter protein on second plasmid, we were able to identify metabolic blocks which led to significant increase in expression levels. Thus down-regulating the ribB gene (3, 4 dihydroxy-2-butanone-4-phosphate synthase) led to a 7 fold increase in specific product yields while down regulating the gene kdpD (histidine kinase) led to 3.2 fold increase in specific yields. CONCLUSION: We have designed a high throughput screening approach which is a useful tool in the repertoire of reverse metabolic engineering strategies for the generation of improved hosts for recombinant protein expression.