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.     

Enabling inverse metabolic engineering through genomics

Inverse metabolic engineering (IME) is a powerful framework for engineering cellular phenotypes. Progress in this field has been limited by a lack of comprehensive methods for efficiently identifying the genetic basis of relevant phenotypes. Advances in genomics technologies, including DNA microarrays and gene sequencing, have dramatically improved our ability to relate changes in phenotype with associated changes in genotype. When applied in the context of IME, these tools should enable the integration of "evolutionary" and "direct" approaches to engineering cell physiology, which should improve our understanding of the complex interactions affecting the expression, evolution and engineering of traits in natural and industrial hosts.     

代谢工程——生物工程学科的新兴研究领域

代谢工程包括推理性代谢工程及逆代谢工程。针对限制生物活性的因素 ,从不同的途径设计代谢改变策略 ,采用重组DNA技术解除或削弱该影响 ,提高目的产物的产率 ,是生化工程学科提高生物活性的新兴研究领域。讨论了推理性代谢工程及逆代谢工程的设计及应用。     

Metabolic engineering of bacteria

Yield and productivity are critical for the economics and viability of a bioprocess. In metabolic engineering the main objective is the increase of a target metabolite production through genetic engineering. Metabolic engineering is the practice of optimizing genetic and regulatory processes within cells to increase the production of a certain substance. In the last years, the development of recombinant DNA technology and other related technologies has provided new tools for approaching yields improvement by means of genetic manipulation of biosynthetic pathway. Industrial microorganisms like Escherichia coli, Actinomycetes, etc. have been developed as biocatalysts to provide new or to optimize existing processes for the biotechnological production of chemicals from renewable plant biomass. The factors like oxygenation, temperature and pH have been traditionally controlled and optimized in industrial fermentation in order to enhance metabolite production. Metabolic engineering of bacteria shows a great scope in industrial application as well as such technique may also have good potential to solve certain metabolic disease and environmental problems in near future.     

Constraint-based metabolic control analysis for rational strain engineering

The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolite concentrations, it does not consider the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework, Network Response Analysis (NRA), for rational genetic strain design. NRA is cast as a Mixed-Integer Linear Programming problem that integrates MCA, Thermodynamically-based Flux Analysis (TFA), biologically relevant constraints, as well as genome editing restrictions into a comprehensive platform for identifying metabolic engineering targets. We show that the NRA formulation and its core constraints are equivalent to the ones of Flux Balance Analysis (FBA) and TFA, which allows it to be used for a wide range of optimization criteria and with various physiological constraints. We also show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.     

Metabolic engineering

Metabolic engineering has developed as a very powerful approach to optimising industrial fermentation processes through the introduction of directed genetic changes using recombinant DNA technology. Successful metabolic engineering starts with a careful analysis of cellular function; based on the results of this analysis, an improved strain is designed and subsequently constructed by genetic engineering. In recent years some very powerful tools have been developed, both for analysing cellular function and for introducing directed genetic changes. In this paper, some of these tools are reviewed and many examples of metabolic engineering are presented to illustrate the power of the technology. The examples are categorised according to the approach taken or the aim: (1) heterologous protein production, (2) extension of substrate range, (3) pathways leading to new products, (4) pathways for degradation of xenobiotics, (5) improvement of overall cellular physiology, (6) elimination or reduction of by-product formation, and (7) improvement of yield or productivity.     

Evolutionary programming as a platform for <Emphasis Type="Italic">in silico</Emphasis> metabolic engineering

Background  

Through genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms.     

Flux analysis and metabolomics for systematic metabolic engineering of microorganisms

Rational engineering of metabolism is important for bio-production using microorganisms. Metabolic design based on in silico simulations and experimental validation of the metabolic state in the engineered strain helps in accomplishing systematic metabolic engineering. Flux balance analysis (FBA) is a method for the prediction of metabolic phenotype, and many applications have been developed using FBA to design metabolic networks. Elementary mode analysis (EMA) and ensemble modeling techniques are also useful tools for in silico strain design. The metabolome and flux distribution of the metabolic pathways enable us to evaluate the metabolic state and provide useful clues to improve target productivity. Here, we reviewed several computational applications for metabolic engineering by using genome-scale metabolic models of microorganisms. We also discussed the recent progress made in the field of metabolomics and ¹³C-metabolic flux analysis techniques, and reviewed these applications pertaining to bio-production development. Because these in silico or experimental approaches have their respective advantages and disadvantages, the combined usage of these methods is complementary and effective for metabolic engineering.     

Metabolic engineering in the -omics era: elucidating and modulating regulatory networks.

The importance of regulatory control in metabolic processes is widely acknowledged, and several enquiries (both local and global) are being made in understanding regulation at various levels of the metabolic hierarchy. The wealth of biological information has enabled identifying the individual components (genes, proteins, and metabolites) of a biological system, and we are now in a position to understand the interactions between these components. Since phenotype is the net result of these interactions, it is immensely important to elucidate them not only for an integrated understanding of physiology, but also for practical applications of using biological systems as cell factories. We present some of the recent "-omics" approaches that have expanded our understanding of regulation at the gene, protein, and metabolite level, followed by analysis of the impact of this progress on the advancement of metabolic engineering. Although this review is by no means exhaustive, we attempt to convey our ideology that combining global information from various levels of metabolic hierarchy is absolutely essential in understanding and subsequently predicting the relationship between changes in gene expression and the resulting phenotype. The ultimate aim of this review is to provide metabolic engineers with an overview of recent advances in complementary aspects of regulation at the gene, protein, and metabolite level and those involved in fundamental research with potential hurdles in the path to implementing their discoveries in practical applications.     

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.     

Use of transposon promoter-probe vectors in the metabolic engineering of the obligate methanotroph Methylomonas sp. strain 16a for enhanced C40 carotenoid synthesis

The recent expansion of genetic and genomic tools for metabolic engineering has accelerated the development of microorganisms for the industrial production of desired compounds. We have used transposable elements to identify chromosomal locations in the obligate methanotroph Methylomonas sp. strain 16a that support high-level expression of genes involved in the synthesis of the C(40) carotenoids canthaxanthin and astaxanthin. with three promoterless carotenoid transposons, five chromosomal locations-the fliCS, hsdM, ccp-3, cysH, and nirS regions-were identified. Total carotenoid synthesis increased 10- to 20-fold when the carotenoid gene clusters were inserted at these chromosomal locations compared to when the same carotenoid gene clusters were integrated at neutral locations under the control of the promoter for the gene conferring resistance to chloramphenicol. A chromosomal integration system based on sucrose lethality was used to make targeted gene deletions or site-specific integration of the carotenoid gene cluster into the Methylomonas genome without leaving genetic scars in the chromosome from the antibiotic resistance genes that are present on the integration vector. The genetic approaches described in this work demonstrate how metabolic engineering of microorganisms, including the less-studied environmental isolates, can be greatly enhanced by identifying integration sites within the chromosome of the host that permit optimal expression of the target genes.     

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.     

Metabolic flux analysis and metabolic engineering of microorganisms

Recent advances in metabolic flux analysis including genome-scale constraints-based flux analysis and its applications in metabolic engineering are reviewed. Various computational aspects of constraints-based flux analysis including genome-scale stoichiometric models, additional constraints used for the improved accuracy, and several algorithms for identifying the target genes to be manipulated are described. Also, some of the successful applications of metabolic flux analysis in metabolic engineering are reviewed. Finally, we discuss the limitations that need to be overcome to make the results of genome-scale flux analysis more realistically represent the real cell metabolism.     

Genetic improvement of brewer’s yeast: current state, perspectives and limits

Brewer’s yeast strain optimisation may lead to a more efficient beer production process, better final quality or healthier beer. However, brewer’s yeast genetic improvement is very challenging, especially true when it comes to lager brewer’s yeast (Saccharomyces pastorianus) which contributes to 90% of the total beer market. This yeast is a genetic hybrid and allopolyploid. While early studies applying traditional genetic approaches encountered many problems, the development of rational metabolic engineering strategies successfully introduced many desired properties into brewer’s yeast. Recently, the first genome sequence of a lager brewer’s strain became available. This has opened the door for applying advanced omics technologies and facilitating inverse metabolic engineering strategies. The latter approach takes advantage of natural diversity and aims at identifying and transferring the crucial genetic information for an interesting phenotype. In this way, strains can be optimised by introducing “natural” mutations. However, even when it comes to self-cloned strains, severe concerns about genetically modified organisms used in the food and beverage industry are still a major hurdle for any commercialisation. Therefore, research efforts will aim at developing new sophisticated screening methods for the isolation of natural mutants with the desired properties which are based on the knowledge of genotype–phenotype linkage.     

An integrated network visualization framework towards metabolic engineering applications

Background

Over the last years, several methods for the phenotype simulation of microorganisms, under specified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential.

Results

In this work, an open-source software that aims to fill the gap between ME and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks.

Conclusions

The framework and its source code are freely available, together with documentation and other resources, being illustrated with well documented case studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0420-0) contains supplementary material, which is available to authorized users.     

Mapping phenotypic landscapes using DNA micro-arrays

Inverse metabolic engineering is a useful approach for engineering phenotypes in biological systems. The overarching objective of this approach is to combine the power of evolutionary engineering approaches with the precision of constructive metabolic engineering strategies. Often the difficulty in this approach is elucidating the genetic basis of the phenotypes that emerge as a result of evolutionary mechanisms. As a result of advances in genomics technologies, several techniques now exist that substantially improve researchers ability to identify such genes. Metabolic engineers now have the ability to map phenotypic landscapes of considerable genetic diversity, which should improve understanding of the relationships that exist among phenotype, genotype, and environment. In this mini-review, we will discuss several of such genomics tools that may be useful in developing inverse metabolic engineering strategies and, in particular, mapping phenotypic landscapes.     

L-鸟氨酸发酵菌种的代谢工程研究进展

L-鸟氨酸是一种非蛋白类氨基酸参与尿素代谢及生物多胺类的合成,其对人体具有治疗肝脏疾病、增强免疫力等作用,被广泛应用于医疗、保健、食品等领域。工业上生产鸟氨酸主要有化学法、酶法及工业发酵法。其中,发酵法因其生产成本及环境保护等方面的优势而逐渐成为研究的焦点。本文归纳了近年来采用基因工程技术选育鸟氨酸高产菌种最新研究进展,重点讨论了产鸟氨酸谷氨酸棒杆菌的代谢工程改造策略,并对未来的研究方向进行了预测。     

Dealing with complexity: evolutionary engineering and genome shuffling

Comparative analysis of the growing number of microbial genome sequences has shown a high plasticity of genomes and several mechanisms for the adaptation of microbial cells to changing environmental conditions have been discovered. By contrast, the underlying metabolic networks of microorganisms are under strict control and relatively rigid, which poses a significant challenge for rational metabolic engineering approaches. Recursive shuffling of whole genomes has recently been demonstrated as an effective new evolutionary whole-cell engineering approach for the rapid improvement of industrially important microbial phenotypes.