Differential Bees Flux Balance Analysis with OptKnock for In Silico Microbial Strains Optimization

Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, the complexities of the metabolic networks have made the process to identify the effects of genetic modification on the desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to the combinatorial problem in obtaining optimal gene deletion strategy. Basically, the size of a genome-scale metabolic model is usually large. As the size of the problem increases, the computation time increases exponentially. In this paper, we propose Differential Bees Flux Balance Analysis (DBFBA) with OptKnock to identify optimal gene knockout strategies for maximizing the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by improving the performance of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by hybridizing Differential Evolution (DE) algorithm into neighborhood searching strategy of BAFBA. In addition, DBFBA is integrated with OptKnock to validate the results for improving the reliability the work. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as the model organisms, DBFBA has shown a better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes compared to the methods used in previous works.     

Exploring the overproduction of amino acids using the bilevel optimization framework OptKnock

In this study, we modify and extend the bilevel optimization framework OptKnock for identifying gene knockout strategies in the Escherichia coli metabolic network, leading to the overproduction of representative amino acids and key precursors for all five families. These strategies span not only the central metabolic network genes but also the amino acid biosynthetic and degradation pathways. In addition to gene deletions, the transport rates of carbon dioxide, ammonia, and oxygen, as well as the secretion pathways for key metabolites, are introduced as optimization variables in the framework. Computational results demonstrate the importance of manipulating energy-producing/consuming pathways, controlling the uptake of nitrogen and oxygen, and blocking the secretion pathways of key competing metabolites. The identified pathway modifications include not only straightforward elimination of competing reactions but also a number of nonintuitive knockouts quite distant from the amino acid-producing pathways. Specifically, OptKnock suggests three reactions (i.e., pyruvate kinase, phosphotransacetylase, and ATPase) for deletion, in addition to the straightforward elimination of 2-ketoglutarate dehydrogenase, to generate a glutamate-overproducing mutant. Similarly, phosphofructokinase and ATPase are identified as promising knockout targets to complement the removal of pyruvate formate lyase and pyruvate dehydrogenase for enhancing the yield of alanine. Although OptKnock in its present form does not consider regulatory constraints, it does provide useful suggestions largely in agreement with existing practices and, more importantly, introduces a framework for incorporating additional modeling refinements as they become available.     

In silico metabolic engineering of Clostridium ljungdahlii for synthesis gas fermentation

Synthesis gas fermentation is one of the most promising routes to convert synthesis gas (syngas; mainly comprised of H₂ and CO) to renewable liquid fuels and chemicals by specialized bacteria. The most commonly studied syngas fermenting bacterium is Clostridium ljungdahlii, which produces acetate and ethanol as its primary metabolic byproducts. Engineering of C. ljungdahlii metabolism to overproduce ethanol, enhance the synthesize of the native byproducts lactate and 2,3-butanediol, and introduce the synthesis of non-native products such as butanol and butyrate has substantial commercial value. We performed in silico metabolic engineering studies using a genome-scale reconstruction of C. ljungdahlii metabolism and the OptKnock computational framework to identify gene knockouts that were predicted to enhance the synthesis of these native products and non-native products, introduced through insertion of the necessary heterologous pathways. The OptKnock derived strategies were often difficult to assess because increase product synthesis was invariably accompanied by decreased growth. Therefore, the OptKnock strategies were further evaluated using a spatiotemporal metabolic model of a syngas bubble column reactor, a popular technology for large-scale gas fermentation. Unlike flux balance analysis, the bubble column model accounted for the complex tradeoffs between increased product synthesis and reduced growth rates of engineered mutants within the spatially varying column environment. The two-stage methodology for deriving and evaluating metabolic engineering strategies was shown to yield new C. ljungdahlii gene targets that offer the potential for increased product synthesis under realistic syngas fermentation conditions.     

A hybrid of bees algorithm and flux balance analysis with OptKnock as a platform for in silico optimization of microbial strains

Microbial strain optimization focuses on improving technological properties of the strain of microorganisms. However, the complexities of the metabolic networks, which lead to data ambiguity, often cause genetic modification on the desirable phenotypes difficult to predict. Furthermore, vast number of reactions in cellular metabolism lead to the combinatorial problem in obtaining optimal gene deletion strategy. Consequently, the computation time increases exponentially with the increase in the size of the problem. Hence, we propose an extension of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by integrating OptKnock into BAFBA to validate the result. This paper presents a number of computational experiments to test on the performance and capability of BAFBA. Escherichia coli, Bacillus subtilis and Clostridium thermocellum are the model organisms in this paper. Also included is the identification of potential reactions to improve the production of succinic acid, lactic acid and ethanol, plus the discussion on the changes in the flux distribution of the predicted mutants. BAFBA shows potential in suggesting the non-intuitive gene knockout strategies and a low variability among the several runs. The results show that BAFBA is suitable, reliable and applicable in predicting optimal gene knockout strategy.     

OP-Synthetic: identification of optimal genetic manipulations for the overproduction of native and non-native metabolites

Constraint-based flux analysis has been widely used in metabolic engineering to predict genetic optimization strategies. These methods seek to find genetic manipulations that maximally couple the desired metabolites with the cellular growth objective. However, such framework does not work well for overproducing chemicals that are not closely correlated with biomass, for example non-native biochemical production by introducing synthetic pathways into heterologous host cells. Here, we present a computational method called OP-Synthetic, which can identify effective manipulations (upregulation, downregulation and deletion of reactions) and produce a step-by-step optimization strategy for the overproduction of indigenous and non-native chemicals. We compared OP-Synthetic with several state-of-the-art computational approaches on the problems of succinate overproduction and N-acetylneuraminic acid synthetic pathway optimization in Escherichia coli. OP-Synthetic showed its advantage for efficiently handling multiple steps optimization problems on genome wide metabolic networks. And more importantly, the optimization strategies predicted by OP-Synthetic have a better match with existing engineered strains, especially for the engineering of synthetic metabolic pathways for non-native chemical production. OP-Synthetic is freely available at:http://bioinfo.au.tsinghua.edu.cn/member/xwwang/OPSynthetic/.     

Metabolic analysis of adaptive evolution for in silico-designed lactate-producing strains

Experimental evolution is now frequently applied to many biological systems to achieve desired objectives. To obtain optimized performance for metabolite production, a successful strategy has been recently developed that couples metabolic engineering techniques with laboratory evolution of microorganisms. Previously, we reported the growth characteristics of three lactate-producing, adaptively evolved Escherichia coli mutant strains designed by the OptKnock computational algorithm. Here, we describe the use of (13)C-labeled experiments and mass distribution measurements to study the evolutionary effects on the fluxome of these differently designed strains. Metabolic flux ratios and intracellular flux distributions as well as physiological data were used to elucidate metabolic responses over the course of adaptive evolution and metabolic differences among strains. The study of 3 unevolved and 12 evolved engineered strains as well as a wild-type strain suggests that evolution resulted in remarkable improvements in both substrate utilization rate and the proportion of glycolytic flux to total glucose utilization flux. Among three strain designs, the most significant increases in the fraction of glucose catabolized through glycolysis (>50%) and the glycolytic fluxes (>twofold) were observed in phosphotransacetylase and phosphofructokinase 1 (PFK1) double deletion (pta- pfkA) strains, which were likely attributed to the dramatic evolutionary increase in gene expression and catalytic activity of the minor PFK encoded by pfkB. These fluxomic studies also revealed the important role of acetate synthetic pathway in anaerobic lactate production. Moreover, flux analysis suggested that independent of genetic background, optimal relative flux distributions in cells could be achieved faster than physiological parameters such as nutrient utilization rate.     

An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems

We introduce a computational framework termed OptReg that determines the optimal reaction activations/inhibitions and eliminations for targeted biochemical production. A reaction is deemed up- or downregulated if it is constrained to assume flux values significantly above or below its steady-state before the genetic manipulations. The developed framework is demonstrated by studying the overproduction of ethanol in Escherichia coli. Computational results reveal the existence of synergism between reaction deletions and modulations implying that the simultaneous application of both types of genetic manipulations yields the most promising results. For example, the downregulation of phosphoglucomutase in conjunction with the deletion of oxygen uptake and pyruvate formate lyase yields 99.8% of the maximum theoretical ethanol yield. Conceptually, the proposed strategies redirect both the carbon flux as well as the cofactors to enhance ethanol production in the network. The OptReg framework is a versatile tool for strain design which allows for a broad array of genetic manipulations.     

OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains

Background  

Computational modeling and analysis of metabolic networks has been successful in metabolic engineering of microbial strains for valuable biochemical production. Limitations of currently available computational methods for metabolic engineering are that they are often based on reaction deletions rather than gene deletions and do not consider the regulatory networks that control metabolism. Due to the presence of multi-functional enzymes and isozymes, computational designs based on reaction deletions can sometimes result in strategies that are genetically complicated or infeasible. Additionally, strains might not be able to grow initially due to regulatory restrictions. To overcome these limitations, we have developed a new approach (OptORF) for identifying metabolic engineering strategies based on gene deletion and overexpression.     

Strategies for systems-level metabolic engineering

Bio-based production of chemicals, fuels and materials is becoming more and more important due to the increasing environmental problems and sharply increasing oil price. To make these biobased processes economically competitive, the biotechnology industry explores new ways to improve the performance of microbial strains in fermentation processes. In contrast to the random mutagenesis and/or intuitive local metabolic engineering practiced in the past, we are now moving towards global-scale metabolic engineering, aided by various experimental and computational tools. This has recently led to some remarkable achievements for the overproduction of valueadded products. In this review, we highlight several relevant gene manipulation tools and computational tools using genome-scale stoichiometric models, and provide useful strategies for successful metabolic engineering along with selected exemplary studies.     

lep operon proximal gene is not required for growth or secretion by Escherichia coli.

Leader peptidase is an essential enzyme of Escherichia coli and is required for protein export. The structural gene for leader peptidase (lep) is separated from its promoter by an upstream gene of unknown function (lepA). The gene lepA was shown by the use of minicell analysis and overproduction to encode a protein of 74,000 daltons. To determine whether this 74,000-dalton protein functions in protein export, a mutant of E. coli H560 was constructed which has a 1.5-kilobase-pair deletion in the lepA gene. The lepA deletion mutant had no apparent defect for growth or protein export, indicating that lepA is nonessential and that the two cotranscribed genes lepA and lep probably have unrelated functions.     

Robust mutant strain design by pessimistic optimization

Background

Flux Balance Analysis (FBA) based mathematical modeling enables in silico prediction of systems behavior for genome-scale metabolic networks. Computational methods have been derived in the FBA framework to solve bi-level optimization for deriving “optimal” mutant microbial strains with targeted biochemical overproduction. The common inherent assumption of these methods is that the surviving mutants will always cooperate with the engineering objective by overproducing the maximum desired biochemicals. However, it has been shown that this optimistic assumption may not be valid in practice.

Methods

We study the validity and robustness of existing bi-level methods for strain optimization under uncertainty and non-cooperative environment. More importantly, we propose new pessimistic optimization formulations: P-ROOM and P-OptKnock, aiming to derive robust mutants with the desired overproduction under two different mutant cell survival models: (1) ROOM assuming mutants have the minimum changes in reaction fluxes from wild-type flux values, and (2) the one considered by OptKnock maximizing the biomass production yield. When optimizing for desired overproduction, our pessimistic formulations derive more robust mutant strains by considering the uncertainty of the cell survival models at the inner level and the cooperation between the outer- and inner-level decision makers. For both P-ROOM and P-OptKnock, by converting multi-level formulations into single-level Mixed Integer Programming (MIP) problems based on the strong duality theorem, we can derive exact optimal solutions that are highly scalable with large networks.

Results

Our robust formulations P-ROOM and P-OptKnock are tested with a small E. coli core metabolic network and a large-scale E. coli iAF1260 network. We demonstrate that the original bi-level formulations (ROOM and OptKnock) derive mutants that may not achieve the predicted overproduction under uncertainty and non-cooperative environment. The knockouts obtained by the proposed pessimistic formulations yield higher chemical production rates than those by the optimistic formulations. Moreover, with higher uncertainty levels, both cellular models under pessimistic approaches produce the same mutant strains.

Conclusions

In this paper, we propose a new pessimistic optimization framework for mutant strain design. Our pessimistic strain optimization methods produce more robust solutions regardless of the inner-level mutant survival models, which is desired as the models for cell survival are often approximate to real-world systems. Such robust and reliable knockout strategies obtained by the pessimistic formulations would provide confidence for in-vivo experimental design of microbial mutants of interest.

     

Overproduction of glycogen in Escherichia coli blocked in the acetate pathway improves cell growth

Excessive production of acetate is a problem frequently encountered in aerobic high-cell-density fermentations of Escherichia coli. Here, we have examined genetic alterations resulting in glycogen overproduction as a possible means to direct the flux of carbon away from the acetate pool. Glycogen overaccumulation was achieved either by using a regulatory glgQ mutation or by transforming cells with a plasmid containing the glycogen biosynthesis genes glgC (encoding ADPG pyrophosphorylase) and glgA (encoding glycogen synthase) under their native promoter. Both strategies resulted in an approximately five-fold increase in glycogen levels but had no significant effect on acetate excretion. The glgC and glgA genes were then placed under the control of the isopropyl---D-thiogalactopyranoside (IPTG) inducible tac promoter, and this construct was used to stimulate glycogen production in a mutant defective in acetate biosynthesis due to deletion of the ack (acetate kinase) and pta (phosphotransacetylase) genes. If glycogen overproduction in the ack pta strain was induced during the late log phase, biomass production increased by 15 to 20% relative to uninduced controls. Glycogen overaccumulation had a significant influence on carbon partitioning: The output of carbon dioxide peaked earlier than in the control strain, and the levels of an unusual fermentation byproduct, pyruvate, were reduced. Exogenous pyruvate was metabolized more rapidly, suggesting higher activity of gluconeogenesis or the tricarboxylic acid (TCA) cycle as a result of glycogen overproduction. Potential mechanisms of the observed metabolic alterations are discussed. Our results suggest that ack pta mutants over producing glycogen may be a suitable starting point for constructing E. coli strains with improved characteristics in high-cell-density fermentations. (c) 1994 John Wiley & Sons, Inc.     

Engineering Escherichia coli for increased productivity of serine-rich proteins based on proteome profiling

Variations in proteome profiles of Escherichia coli in response to the overproduction of human leptin, a serine-rich (11.6% of total amino acids) protein, were examined by two-dimensional gel electrophoresis. The levels of heat shock proteins increased, while those of protein elongation factors, 30S ribosomal protein, and some enzymes involved in amino acid biosynthesis decreased, after leptin overproduction. Most notably, the levels of enzymes involved in the biosynthesis of serine family amino acids significantly decreased. Based on this information, we designed a strategy to enhance the leptin productivity by manipulating the cysK gene, encoding cysteine synthase A. By coexpression of the cysK gene, we were able to increase the cell growth rate by approximately twofold. Also, the specific leptin productivity could be increased by fourfold. In addition, we found that cysK coexpression can improve the production of another serine-rich protein, interleukin-12 beta chain, suggesting that this strategy may be useful for the production of other serine-rich proteins as well. The approach taken in this study should be useful in designing a strategy for improving recombinant protein production.     

A “push-pull-restrain” strategy to improve citronellol production in Saccharomyces cerevisiae

Microbial production of monoterpenes has attracted increasing attention in recent years. Up to date, there are only few reports on the biosynthesis of the monoterpene alcohol citronellol that is widely used as fragrant and pharmaceutical intermediates. Here, we engineered Saccharomyces cerevisiae by employing a “push-pull-restrain” strategy to improve citronellol production based on the reduction of geraniol. Starting from a engineered geraniol-producing strain, different reductases were investigated and the best performing iridoid synthase from Catharanthus roseus (CrIS) resulted in 285.89 mg/L enantiomerically pure S-citronellol in shake flasks. Geranyl diphosphate (GPP), the most important precursor for monoterpenes, was enhanced by replacing the wild farnesyl diphosphate synthase (Erg20) with the mutant Erg20^F96W, increasing the citronellol titer to 406.01 mg/L without negative influence on cell growth. Moreover, we employed synthetic protein scaffolds and protein fusion to colocalize four sequential enzymes to achieve better substrate channeling along with the deletion of an intermediate degradation pathway gene ATF1, which elevated the citronellol titer to 972.02 mg/L with the proportion of 97.8% of total monoterpenes in YPD medium. Finally, the engineered strain with complemented auxotrophic markers produced 8.30 g/L of citronellol by fed-batch fermentation, which was the highest citronellol titer reported to date. The multi-level engineering strategies developed here demonstrate the potential of monoterpenes overproduction in yeast, which can serve as a generally applicable platform for overproduction of other monoterpenes.     

Functional analysis of the gas vesicle gene cluster of the halophilic archaeon Haloferax mediterranei defines the vac-region boundary and suggests a regulatory role for the gvpD gene or its product

A series of deletions introduced into the gvp gene cluster of Haloferax mediterranei, comprising 14 genes involved in gas vesicle synthesis (mc-vac-region), was investigated by transformation experiments. Gas vesicle production and the expression of the gvpA gene encoding the major gas vesicle protein, GvpA, was monitored in each Haloferax volcanii transformant. Whereas transformants containing the entire mc-vac-region produced gas vesicles (Vac+), various deletions in the region 5' to gvpA (encompassing gvpD-gvpM) or 3' to gvpA (containing gvpC, gvpN and gvpO) revealed Vac- transformants. All these transformants expressed gvpA and contained the 8 kDa GvpA protein as shown by Western analysis. However, transformants containing the gvpA gene by itself indicated a lower level of GvpA than observed with each of the other transformants. None of these transformants containing deletion constructs assembled the GvpA protein into gas vesicles. In contrast, transformants containing a construct carrying a 918 bp deletion internal to gvpD exhibited a tremendous gas vesicle overproduction, suggesting a regulatory role for the gvpD gene or its product. This is the first assignment of a functional role for one of the 13 halobacterial gvp genes found in addition to gvpA that are involved in the synthesis of this unique structure.     

CONSTRICTOR: Constraint Modification Provides Insight into Design of Biochemical Networks

Advances in computational methods that allow for exploration of the combinatorial mutation space are needed to realize the potential of synthetic biology based strain engineering efforts. Here, we present Constrictor, a computational framework that uses flux balance analysis (FBA) to analyze inhibitory effects of genetic mutations on the performance of biochemical networks. Constrictor identifies engineering interventions by classifying the reactions in the metabolic model depending on the extent to which their flux must be decreased to achieve the overproduction target. The optimal inhibition of various reaction pathways is determined by restricting the flux through targeted reactions below the steady state levels of a baseline strain. Constrictor generates unique in silico strains, each representing an “expression state”, or a combination of gene expression levels required to achieve the overproduction target. The Constrictor framework is demonstrated by studying overproduction of ethylene in Escherichia coli network models iAF1260 and iJO1366 through the addition of the heterologous ethylene-forming enzyme from Pseudomonas syringae. Targeting individual reactions as well as combinations of reactions reveals in silico mutants that are predicted to have as high as 25% greater theoretical ethylene yields than the baseline strain during simulated exponential growth. Altering the degree of restriction reveals a large distribution of ethylene yields, while analysis of the expression states that return lower yields provides insight into system bottlenecks. Finally, we demonstrate the ability of Constrictor to scan networks and provide targets for a range of possible products. Constrictor is an adaptable technique that can be used to generate and analyze disparate populations of in silico mutants, select gene expression levels and provide non-intuitive strategies for metabolic engineering.     

Engineering Escherichia coli for Increased Productivity of Serine-Rich Proteins Based on Proteome Profiling

Variations in proteome profiles of Escherichia coli in response to the overproduction of human leptin, a serine-rich (11.6% of total amino acids) protein, were examined by two-dimensional gel electrophoresis. The levels of heat shock proteins increased, while those of protein elongation factors, 30S ribosomal protein, and some enzymes involved in amino acid biosynthesis decreased, after leptin overproduction. Most notably, the levels of enzymes involved in the biosynthesis of serine family amino acids significantly decreased. Based on this information, we designed a strategy to enhance the leptin productivity by manipulating the cysK gene, encoding cysteine synthase A. By coexpression of the cysK gene, we were able to increase the cell growth rate by approximately twofold. Also, the specific leptin productivity could be increased by fourfold. In addition, we found that cysK coexpression can improve the production of another serine-rich protein, interleukin-12 β chain, suggesting that this strategy may be useful for the production of other serine-rich proteins as well. The approach taken in this study should be useful in designing a strategy for improving recombinant protein production.