工业微生物代谢网络模型的研究进展及应用

基因组规模代谢网络(Genome-scale metabolic network model,GSMM)是工业微生物菌株定向改造过程中一种极为重要的指导性工具,有助于研究者快速获取特定性状的工业微生物,因此越来越受到人们的关注。文中回顾了GSMM的发展历程,总结并评述了GSMM的构建方法,以4种重要工业微生物(枯草芽孢杆菌Bacillus subtilis、大肠杆菌Escherichia coli、谷氨酸棒杆菌Corynebacterium glutamicum和酿酒酵母Saccharomyces cerevisiae)为例,阐述了GSMM在工业微生物中的发展与应用。此外,还对GSMM未来的发展趋势进行了展望。     

基因组规模代谢网络模型构建及其应用

微生物制造产业的发展迫切需要进一步提高认识、设计和改造微生物细胞代谢的能力,以推动工业生物技术快速发展。随着微生物全基因组序列等高通量数据的不断积聚和生物信息学策略的持续涌现,使全局性、系统化地解析、设计、调控微生物生理代谢功能成为可能。而基于基因组序列注释和详细生化信息整合的基因组规模代谢网络模型(GSMM)构建为全局理解和理性调控微生物生理代谢功能提供了最佳平台。以下在详述GSMM的应用基础上,描述了如何构建一个高精确度的GSMM,并展望了未来的发展方向。     

Reprogramming microorganisms for the biosynthesis of astaxanthin via metabolic engineering

There is an increasing demand for astaxanthin in food, feed, cosmetics and pharmaceutical applications because of its superior anti-oxidative and coloring properties. However, naturally produced astaxanthin is expensive, mainly due to low productivity and limited sources. Reprogramming of microorganisms for astaxanthin production via metabolic engineering is a promising strategy. We primarily focus on the application of synthetic biology, enzyme engineering and metabolic engineering in enhancing the synthesis and accumulation of astaxanthin in microorganisms in this review. We also discuss the biosynthetic pathways of astaxanthin within natural producers, and summarize the achievements and challenges in reprogramming microorganisms for enhancing astaxanthin production. This review illuminates recent biotechnological advances in microbial production of astaxanthin. Future perspectives on utilization of new technologies for boosting microbial astaxanthin production are also discussed.     

乳酸乳球菌NZ9000基因组规模代谢网络模型的构建与验证

随着后基因组时代的到来,工业微生物的代谢工程改造在工业生产上发挥着越来越重要的作用。而基因组规模代谢网络模型(Genome-scalemetabolicmodel,GSMM)将生物体体内所有已知代谢信息进行整合,为全局理解生物体的代谢状态、理性指导代谢工程改造提供了最佳的平台。乳酸乳球菌NZ9000(Lactococcuslactis NZ9000)作为工业发酵领域的重要菌株之一,由于其遗传背景清晰且几乎不分泌蛋白,是基因工程改造和外源蛋白表达的理想模式菌株。文中基于基因组功能注释和比较基因组学构建了L.lactisNZ9000的首个基因组规模代谢网络模型iWK557,包含557个基因、668个代谢物、840个反应,并进一步在定性和定量两个层次验证了iWK557的准确性,以期为理性指导L. lactis NZ9000代谢工程改造提供良好工具。     

基于约束的基因组规模代谢网络模型构建方法研究进展

基因组规模代谢网络模型(Genome-scale metabolic network model,GSMM)正成为细胞代谢特性研究的重要工具,经过多年发展相关理论方法取得了诸多进展.近年来,在基础GSMM模型基础上,通过整合基因组、转录组、蛋白组和热力学数据,实现基于各种约束的GSMM构建,在基因靶点识别、系统代谢工程...     

基于Matlab对解脂耶氏酵母基因组规模代谢网络模型仿真结果的可视化分析

【目的】近十年来,基因组代谢网络模型迅速发展。通过构建基因组代谢网络模型进行计算机仿真模拟已成为研究生物体复杂的生理代谢不可或缺的工具。实现对仿真结果的可视化分析,可以直观地追踪模型中的代谢流向,从而更好地对仿真结果进行分析。【方法】在简要概述目前可视化方法的基础上,提出了一种基于Matlab实现基因组规模代谢网络模型仿真结果可视化的方法：通过CellDesigner预先绘制与模型相匹配的图,通过RAVEN toolbox中的函数于Matlab进行读图、并实现仿真结果的可视化。【结果】以解脂耶氏酵母基因组规模代谢网络模型iYL619_PCP v1.7为对象,实现并阐明其仿真结果的可视化。【结论】通过该方法可以清晰地监测模型中的流量和流向变化,提高仿真结果的分析效率。     

Engineering of microorganisms for the production of biofuels and perspectives based on systems metabolic engineering approaches

The increasing oil price and environmental concerns caused by the use of fossil fuel have renewed our interest in utilizing biomass as a sustainable resource for the production of biofuel. It is however essential to develop high performance microbes that are capable of producing biofuels with very high efficiency in order to compete with the fossil fuel. Recently, the strategies for developing microbial strains by systems metabolic engineering, which can be considered as metabolic engineering integrated with systems biology and synthetic biology, have been developed. Systems metabolic engineering allows successful development of microbes that are capable of producing several different biofuels including bioethanol, biobutanol, alkane, and biodiesel, and even hydrogen. In this review, the approaches employed to develop efficient biofuel producers by metabolic engineering and systems metabolic engineering approaches are reviewed with relevant example cases. It is expected that systems metabolic engineering will be employed as an essential strategy for the development of microbial strains for industrial applications.     

利用代谢工程技术提高工业微生物对胁迫的抗性

代谢工程是工业微生物菌种改造的平台技术,不仅可用于改变微生物细胞内的代谢流向,也可以用于改善工业微生物的生理功能。在工业生产过程中,微生物细胞会面临多种胁迫作用,这些胁迫诱导的基因调节作用,都有可能影响细胞的许多重要生理功能,从而影响生物转化过程的效率。从工业应用的观点出发,选择生产性能良好、对发酵过程中的主要胁迫因素有较强耐受性的菌株至关重要。以下评述了借鉴传统代谢工程技术和反向代谢工程技术来提高工业微生物对胁迫抗性的若干研究策略,提出了该领域目前存在的问题,以及利用代谢工程技术改善微生物胁迫抗性——即微生物生理功能工程的发展方向。     

An expanded role for microbial physiology in metabolic engineering and functional genomics: moving towards systems biology

Microbial physiology has traditionally played a very important role in both fundamental research and in industrial applications of microorganisms. The classical approach in microbial physiology has been to analyze the role of individual components (genes or proteins) in the overall cell function. With the progress in molecular biology it has become possible to optimize industrial fermentations through introduction of directed genetic modification - an approach referred to as metabolic engineering. Furthermore, as a consequence of large sequencing programs the complete genomic sequence has become available for an increasing number of microorganisms. This has resulted in substantial research efforts in assigning function to all identified open reading frames - referred to as functional genomics. In both metabolic engineering and functional genomics there is a trend towards application of a macroscopic view on cell function, and this leads to an expanded role of the classical approach applied in microbial physiology. With the increased understanding of the molecular mechanisms it is envisaged that in the future it will be possible to describe the interaction between all the components in the system (the cell), also at the quantitative level, and this is the goal of systems biology. Clearly this will have a significant impact on microbial physiology as well as on metabolic engineering.     

Opportunities for yeast metabolic engineering: Lessons from synthetic biology

Constant progress in genetic engineering has given rise to a number of promising areas of research that facilitated the expansion of industrial biotechnology. The field of metabolic engineering, which utilizes genetic tools to manipulate microbial metabolism to enhance the production of compounds of interest, has had a particularly strong impact by providing new platforms for chemical production. Recent developments in synthetic biology promise to expand the metabolic engineering toolbox further by creating novel biological components for pathway design. The present review addresses some of the recent advances in synthetic biology and how these have the potential to affect metabolic engineering in the yeast Saccharomyces cerevisiae. While S. cerevisiae for years has been a robust industrial organism and the target of multiple metabolic engineering trials, its potential for synthetic biology has remained relatively unexplored and further research in this field could strongly contribute to industrial biotechnology. This review also addresses are general considerations for pathway design, ranging from individual components to regulatory systems, overall pathway considerations and whole-organism engineering, with an emphasis on potential contributions of synthetic biology to these areas. Some examples of applications for yeast synthetic biology and metabolic engineering are also discussed.     

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.     

Technical methods to improve yield,activity and stability in the development of microbial lipases

Lipases are ubiquitous biocatalysts that catalyze various reactions in organic solvents or in solvent-free systems and are increasingly applied in various industrial fields. In view of the excellent catalytic activities and the huge application potential, more than 20 microbial lipases have been realized in large-scale commercial production. The potential for commercial exploitation of a microbial lipase is determined by its yield, activity, stability and other characteristics. This review will survey the various technical methods that have been developed to enhance yield, activity and stability of microbial lipases from four aspects, including improvements in lipase-producing strains, modification of lipase genes, fermentation engineering of lipases and downstream processing technology of lipase products.     

全基因组范围代谢网络的构建和最小基因组研究

全基因组范围代谢网络（genome-scale metabolic network,GSMN）的构建是合成生物学研究的一个重要研究手段。通过整合各种组学数据和借助计算机进行模拟分析,将基因型与表型的关系进行定量关联,从而为从全局的角度探索和揭示生物代谢机制,进而对生物进行合理的重新设计和工程改造提供了有效的框架。该方法在最小基因组研究中也有着突出的优势,通过计算机辅助的基因组最小化模拟与分析,能够系统鉴定微生物基因组基因的必需性。迄今为止,已有近百个基因组范围的代谢网络发表,覆盖的生物包括原核生物、真核生物和古生生物,并广泛应用于医药、能源、环境、工业和农业等多个领域,展现出了广阔的应用前景。将对全基因组范围代谢网络构建的方法、应用,特别是其在最小基因组研究中的应用作简要的综述。     

谷氨酸棒杆菌生产异亮氨酸辅因子策略及其基因组整合研究进展

L?异亮氨酸属于三大支链氨基酸,是人体8种必需氨基酸之一,广泛应用于食品、药品、保健品、化妆品等领域。目前,微生物发酵法是工业生产L?异亮氨酸的主要方法,其中谷氨酸棒杆菌（Corynebacterium glutamicum）是发酵生产L?异亮氨酸的优势菌株,然而随机诱变会使产量的提高能力达到饱和,难以得到更加高产的菌株,因此针对诱变菌株进行理性改造已成为进一步提高产量的主要方式;且随着遗传操作技术在谷氨酸棒杆菌中的应用与优化,代谢工程育种已逐渐取代传统的诱变育种。综述了谷氨酸棒杆菌中L?异亮氨酸的生物合成途径、代谢调控机制和理性改造L?异亮氨酸生产菌株的策略,并对辅助因子工程应用于理性改造及对谷氨酸棒杆菌基因组整合策略进行了系统阐述,以期为工业水平稳定生产L?异亮氨酸高产菌株的基因组整合策略提供参考依据。     

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.     

Modular co-culture engineering,a new approach for metabolic engineering

With the development of metabolic engineering, employment of a selected microbial host for accommodation of a designed biosynthetic pathway to produce a target compound has achieved tremendous success in the past several decades. Yet, increasing requirements for sophisticated microbial biosynthesis call for establishment and application of more advanced metabolic engineering methodologies. Recently, important progress has been made towards employing more than one engineered microbial strains to constitute synthetic co-cultures and modularizing the biosynthetic labor between the co-culture members in order to improve bioproduction performance. This emerging approach, referred to as modular co-culture engineering in this review, presents a valuable opportunity for expanding the scope of the broad field of metabolic engineering. We highlight representative research accomplishments using this approach, especially those utilizing metabolic engineering tools for microbial co-culture manipulation. Key benefits and major challenges associated with modular co-culture engineering are also presented and discussed.     

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.     

Genome-scale metabolic models as platforms for strain design and biological discovery

Genome-scale metabolic models (GEMs) have been developed and used in guiding systems’ metabolic engineering strategies for strain design and development. This strategy has been used in fermentative production of bio-based industrial chemicals and fuels from alternative carbon sources. However, computer-aided hypotheses building using established algorithms and software platforms for biological discovery can be integrated into the pipeline for strain design strategy to create superior strains of microorganisms for targeted biosynthetic goals. Here, I described an integrated workflow strategy using GEMs for strain design and biological discovery. Specific case studies of strain design and biological discovery using Escherichia coli genome-scale model are presented and discussed. The integrated workflow presented herein, when applied carefully would help guide future design strategies for high-performance microbial strains that have existing and forthcoming genome-scale metabolic models.