Optimization of metabolic pathways for bioconversion of lignocellulose to ethanol through genetic engineering

The optimization of metabolic pathways is of fundamental importance for strategies aimed at improving the economics and yield of the lignocellulose-to-ethanol processes. Although Escherichia coli is capable of metabolizing a wide variety of substrates including hexoses and pentoses, its hexose metabolism is inferior to that of Zymomonas mobilis, an obligate, ethanologenic bacterium. We therefore inserted and expressed Z. mobilis genes encoding essential enzymes involved in the fermentation pathway, alcohol dehydrogenase II (adh II) and pyruvate decarboxylase (pdc), into E. coli, resulting in increased cell growth and ethanol production. Ethanol concentrations of > 30 g/L were obtained on 10% glucose. Additionally, since pyruvate is mainly assimilated through pyruvate formate lyase (pfl) and forms formic acid and acetyl coenzyme A, metabolic redirection was attempted through gene knockout by Red-mediated recombination to decrease the byproducts of pyruvate metabolism. Under microaerobic conditions, pflA- and pflB-mutants produced more ethanol (163% and 207%, respectively) relative to the parent strain, using glucose as a carbon source.     

New developments in engineering plant metabolic pathways

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Ensemble Modeling for Robustness Analysis in engineering non-native metabolic pathways

Metabolic pathways in cells must be sufficiently robust to tolerate fluctuations in expression levels and changes in environmental conditions. Perturbations in expression levels may lead to system failure due to the disappearance of a stable steady state. Increasing evidence has suggested that biological networks have evolved such that they are intrinsically robust in their network structure. In this article, we presented Ensemble Modeling for Robustness Analysis (EMRA), which combines a continuation method with the Ensemble Modeling approach, for investigating the robustness issue of non-native pathways. EMRA investigates a large ensemble of reference models with different parameters, and determines the effects of parameter drifting until a bifurcation point, beyond which a stable steady state disappears and system failure occurs. A pathway is considered to have high bifurcational robustness if the probability of system failure is low in the ensemble. To demonstrate the utility of EMRA, we investigate the bifurcational robustness of two synthetic central metabolic pathways that achieve carbon conservation: non-oxidative glycolysis and reverse glyoxylate cycle. With EMRA, we determined the probability of system failure of each design and demonstrated that alternative designs of these pathways indeed display varying degrees of bifurcational robustness. Furthermore, we demonstrated that target selection for flux improvement should consider the trade-offs between robustness and performance.     

Breakthrough in chloroplast genetic engineering of agronomically important crops

Chloroplast genetic engineering offers several unique advantages, including high-level transgene expression, multi-gene engineering in a single transformation event and transgene containment by maternal inheritance, as well as a lack of gene silencing, position and pleiotropic effects and undesirable foreign DNA. More than 40 transgenes have been stably integrated and expressed using the tobacco chloroplast genome to confer desired agronomic traits or express high levels of vaccine antigens and biopharmaceuticals. Despite such significant progress, this technology has not been extended to major crops. However, highly efficient soybean, carrot and cotton plastid transformation has recently been accomplished through somatic embryogenesis using species-specific chloroplast vectors. This review focuses on recent exciting developments in this field and offers directions for further research and development.     

Channeling in native microbial pathways: Implications and challenges for metabolic engineering

Intracellular enzymes can be organized into a variety of assemblies, shuttling intermediates from one active site to the next. Eukaryotic compartmentalization within mitochondria and peroxisomes and substrate tunneling within multi-enzyme complexes have been well recognized. Intriguingly, the central pathways in prokaryotes may also form extensive channels, including the heavily branched glycolysis pathway. In vivo channeling through cascade enzymes is difficult to directly measure, but can be inferred from in vitro tests, reaction thermodynamics, transport/reaction modeling, analysis of molecular diffusion and protein interactions, or steady state/dynamic isotopic labeling. Channeling presents challenges but also opportunities for metabolic engineering applications. It rigidifies fluxes in native pathways by trapping or excluding metabolites for bioconversions, causing substrate catabolite repressions or inferior efficiencies in engineered pathways. Channeling is an overlooked regulatory mechanism used to control flux responses under environmental/genetic perturbations. The heterogeneous distribution of intracellular enzymes also confounds kinetic modeling and multiple-omics analyses. Understanding the scope and mechanisms of channeling in central pathways may improve our interpretation of robust fluxomic topology throughout metabolic networks and lead to better design and engineering of heterologous pathways.     

Inverse metabolic engineering: A strategy for directed genetic engineering of useful phenotypes

The classical method of metabolic engineering, identifying a rate-determining step in a pathway and alleviating the bottleneck by enzyme overexpression, has motivated much research but has enjoyed only limited practical success. Intervention of other limiting steps, of counterbalancing regulation, and of unknown coupled pathways often confounds this direct approach. Here the concept of inverse metabolic engineering is codified and its application is illustrated with several examples. Inverse metabolic engineering means the elucidation of a metabolic engineering strategy by: first, identifying, constructing, or calculating a desired phenotype; second, determining the genetic or the particular environmental factors conferring that phenotype; and third, endowing that phenotype on another strain or organism by directed genetic or environmental manipulation. This paradigm has been successfully applied in several contexts, including elimination of growth factor requirements in mammalian cell culture and increasing the energetic efficiency of microaerobic bacterial respiration. (c) 1996 John Wiley & Sons, Inc.     

Inverse metabolic engineering: a strategy for directed genetic engineering of useful phenotypes

The classical method of metabolic engineering, identifying a rate-determining step in a pathway and alleviating the bottleneck by enzyme overexpression, has motivated much research but has enjoyed only limited practical success. Intervention of other limiting steps, of counter-balancing regulation, and of unknown coupled pathways often confounds this direct approach. Here the concept of inverse metabolic engineering is codified and its application is illustrated with several examples. Inverse metabolic engineering means the elucidation of a metabolic engineering strategy by: first, identifying, constructing, or calculating a desired phenotype; second, determining the genetic or the particular environmental factors conferring that phenotype; and third, endowing that phenotype on another strain or organism by directed genetic or environmental manipulation. This paradigm has been successfully applied in several contexts, including elimination of growth factor requirements in mammalian cell culture and increasing the energetic efficiency of microaerobic bacterial respiration.     

Inferring qualitative relations in genetic networks and metabolic pathways

MOTIVATION: Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network. RESULTS: First, a Boolean network model with noise is proposed, together with an inference algorithm for it. Next, a qualitative network model is proposed, in which regulation rules are represented as qualitative rules and embedded in the network structure. Algorithms are also presented for inferring qualitative relations from time series data. Then, an algorithm for inferring S-systems (synergistic and saturable systems) from time series data is presented, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems. Theoretical results are shown for Boolean networks with noises and simple qualitative networks. Computational results are shown for Boolean networks with noises and S-systems, where real data are not used because the proposed models are still conceptual and the quantity and quality of currently available data are not enough for the application of the proposed methods.     

Opportunities and surprises in crops modified by transgenic technology: metabolic engineering of benzylisoquinoline alkaloid, gossypol and lysine biosynthetic pathways

The development of new or improved traits in plants, whether that is through traditional genetic modification and selection or through transgenic technologies, is associated with the potential risk of unintended changes with harmful or unacceptable consequences. The greater definition and precision of transgenic modification and the regulatory oversight of such technology may, however, confer advantages in safety and efficacy. This bears considerable relevance to the use of transgenic-based metabolic engineering in agricultural trait development. Metabolic engineering seeks to modify the amounts or chemical structures within selected biosynthetic routes without introducing inadvertent effects on other metabolic pathways. Examples discussed here include attempts to; (i) modify benzylisoquinoline alkaloid biosynthesis in poppy, (ii) improve the nutritional value of maize by increasing levels of free lysine, and (iii) increase the nutritional value of cottonseed by eliminating gossypol production. Clearly, evaluation of the efficacy (and unintended consequences) of such approaches is vital. A role for metabolomics in the compositional and metabolite analyses of new plant varieties derived from transgenic-based metabolic engineering is discussed. Major themes discussed in this review include; (i) the heightened level of scrutiny associated with genetically modified (GM) crop evaluations has markedly contributed to the safety in the adoption of transgenic technology, and (ii) the nature of any introduced trait may prove more relevant to safety assessments than the means by which the trait is introduced.     

Prevention of preharvest aflatoxin contamination through genetic engineering of crops

Current practices on prevention of aflatoxin contamination of crop species include time consuming, expensive agronomic practices. Of all the methods available to-date, conventional breeding and/or genetic engineering to develop host plant-based resistance to aflatoxin-producing fungi appear to be valuable for several reasons. However, breeding for disease-resistant crops is very time consuming, especially in tree crops, and does not lend itself ready to combat the evolution of new virulent fungal races. Moreover, availability of known genotypes with natural resistance to mycotoxin-producing fungi is a prerequisite for the successful breeding program. While it is possible to identify a few genotypes of corn or peanuts that are naturally resistant toAspergillus we do not know whether these antifungal factors are specific toA. flavus. In crops like cotton, there are no known naturally resistant varieties toAspergillus. Availability of transgenic varieties with antifungal traits is extremely valuable as a breeding tool. Several antifungal proteins and peptides are available for genetic engineering of susceptible crop species, thanks to the availability of efficient modern tools to understand and evaluate protein interactions by proteomics of host, and genomics and field ecology of the fungus. Transgenic approaches are being undertaken in several industry and academic laboratories to prevent invasion byAspergillus fungi or to prevent biosynthesis of aflatoxin. Recent trends in reducing aflatoxin contamination through genetic engineering of cultivated crop species with antifungal proteins are summarized in this report. Presented at the EU-USA Bilateral Workshop on Toxigenic Fungi & Mycotoxins, New Orleans, USA, July 5–7, 2005     

Genetic analysis of abiotic stress tolerance in crops

Abiotic stress tolerance is complex, but as phenotyping technologies improve, components that contribute to abiotic stress tolerance can be quantified with increasing ease. In parallel with these phenomics advances, genetic approaches with more complex genomes are becoming increasingly tractable as genomic information in non-model crops increases and even whole crop genomes can be re-sequenced. Thus, genetic approaches to elucidating the molecular basis to abiotic stress tolerance in crops are becoming more easily achievable.     

Butanol production from renewable biomass: rediscovery of metabolic pathways and metabolic engineering

Biofuel from renewable biomass is one of the answers to help solve the problems associated with limited fossil resources and climate change. Butanol has superior liquid-fuel characteristics, with similar properties to gasoline, and thus, has the potential to be used as a substitute for gasoline. Clostridia are recognized as a good butanol producers and are employed in the industrial-scale production of solvents. Due to the difficulty of performing genetic manipulations on clostridia, however, strain improvement has been rather slow. Furthermore, complex metabolic characteristics of acidogenesis followed by solventogenesis in this strain have hampered the development of engineered clostridia strains with highly efficient and selective butanol-production capabilities. In recent years, the butanol-producing characteristics in clostridia have been further characterized and alternative pathways discovered. More recently, systems-level metabolic engineering approaches were taken to develop superior strains. Herein, we review recent discoveries of metabolic pathways for butanol production and the metabolic engineering strategies being developed.