微生物基因组尺度代谢网络模型构建方法的比较分析

随着后基因时代的到来,微生物的定向菌种改造在生产中发挥着越来越重要的作用.基因组尺度代谢网络模型是微生物定向改造中一种不可缺少的指导性工具,可降低菌种改造的盲目性,增加目的性和成功率.随着研究的深入,基因组尺度代谢网络模型的构建方法也越来越多.究竟选择什么样的方法才能构建出全面准确的基因组尺度代谢网络模型,对于初学者来说是一个大难题.论文结合本课题组的研究,将近年文献报导中出现过的模型构建方法进行了分类和分析,并评述了各种方法的优、缺点,以期为初学者提供参考.具体介绍的方法有:基于基因组注释构建代谢网络模型,基于蛋白组构建代谢网络模型,基于文献挖掘构建代谢网络模型,通过软件和网络平台构建代谢网络模型,基于京都基因与基因组百科全书(KEGG)构建代谢网络模型.五种构建代谢网络模型方法都有其优点,但也有不可避免的缺点,要构建较为准确全面的基因组尺度代谢网络模型,需要将各种方法结合,弥补彼此的不足.图4表0参48     

基因组尺度集成细胞网络模型研究进展

细胞网络研究是系统生物学的一个研究热点,通过结合计算机模型和实验技术,从系统角度分析复杂的生物系统,可以为生物实验提供指导和预测。近十年来,国内外许多研究团队致力于基因组规模代谢网络、基因调控网络和信号转导网络模型的构建和分析,并取得了一定成果。而不同类型网络的集成和分析是当前生物网络研究中一个新的方向,并带来了诸多新的挑战。在本文中,主要对基因组尺度集成细胞网络模型的研究进展,特别是对代谢网络和转录网络的集成进行了详细论述,着重于集成网络的构建和分析方法,最后对该领域研究前景进行了展望。     

基因组尺度代谢网络自动重构及分析工具研究进展

高通量数据的产出为基因组尺度代谢网络的构建提供了基础,但同时也对网络构建和分析方法的改进提出了挑战。随着数据量的不断增大,耗时耗力的人工构建及分析已经无法满足模型发展的需要,因而各种自动化的方法应运而生。模型构建和分析的自动化不仅能够大幅度提高模型构建和解析的速度,同时对于模型构建和分析方法的标准化和程序化也有着不可替代的作用。文中结合作者的实际研究经验,对基因组尺度代谢网络构建的自动化进程和主要的代谢网络分析工具进行了较为详细的介绍,总结了代谢网络自动重构的流程,并提出了目前面对的主要问题和未来的研究方向。     

一种基于代谢网络分析最小化基因组的方法及其在大肠杆菌中的应用

最小生命体的合成是合成生物学研究的重要方向。最小化基因组的同时而又不对细胞生长产生影响是代谢工程研究的一个重要目标。文中提出了一种从基因组尺度代谢网络模型出发,通过零通量反应删除及对非必需基因组合删除计算获得基因组最小化代谢网络模型的方法,利用该方法简化了大肠杆菌经典代谢网络模型iAF1260,由起始的1 260个基因简化得到了312个基因,而最优生物质生成速率保持不变。基因组最小化代谢网络模型预测了在细胞正常生长的前提下包含最少基因的代谢途径,为大肠杆菌获得最小基因组的湿实验设计提供了重要参考。     

基因组尺度代谢网络模型在生物质燃料产生菌改造中的应用

基因组尺度代谢网络模型是以菌株高通量测序数据和各种组学数据为基础,模拟生物体的代谢过程,该模型能够预测菌种改造位点并指导具体实验,使菌种改造更理性、更直接和更省时。基因组尺度代谢网络模型在生物质燃料产生菌的改造方面已取得一定进展,在提高目标产物产量方面已取得一定效果。本文综述了基因组尺度代谢网络模型在生物质燃料产生菌改造中的应用,展望了其应用前景。     

Helicobacter pylori 26695 基因组尺度代谢研究进展

代谢网络在代谢功能研究、生物代谢过程控制、疾病诊断分析和药物靶标设计等方面具有重要理论和实践意义。生物信息学研究利用序列同源、结构模拟、对接等手段与生化实验有效结合促进了生物体代谢网络的进一步完善。本文作者在构建幽门螺杆菌（Helicobacter pylori 26695,H.pylori 26695）代谢网络的工作基础上综合了近年来研究者对H.pylori 26695代谢通路关键酶的研究成果,并结合基因组信息,综述了H.pylori 26695特异性的重要代谢通路。本文从基因组水平阐明代谢通路与基因的关系,并详细分析了关键酶对H.pylori 26695生理的重要作用,最后探讨了重构一个连续、完整的代谢网络面临的困难及其在药物靶标设计方面的研究前景。     

基于RAST的离子束重组菌DOB981的代谢网络研究

为进一步深入理解低能离子注入对DOB的生理代谢产生的生物学意义,本研究基于DOB全基因组De novo测序数据,应用生物信息学方法对离子束重组菌DOB981及其原始菌株进行基因组结构和功能注释,构建基因尺度的代谢网络,并用Cytoscape对其进行可视化分析。研究表明,离子束重组菌株DOB981的基因组大小比原始菌株减少了223 268 bp,ORF减少了204个,功能基因减少了136个,生化反应的数量减少了10个,而生物反应的反应底物比原始菌株增加了3个;离子束重组菌株DOB981独有19个生化反应,比原始菌株减少了10个。Cytoscape可视化分析表明,离子束重组菌DOB981代谢网络中包含1 604个节点和3 733条连线,虽然比原始菌株减少了1个节点,但连线却增加了68条。基因尺度代谢网络拓扑属性分析表明,离子束重组菌DOB981与原始菌株的代谢网络均为无标度网络,具有小世界网络(SWN)特性,但重组菌DOB981的代谢网络的特征路径长度大于原始菌株,其总体结构相对松散,且密度低。本研究不仅对DOB的环境适应性机制的研究具有重要意义,也为DOB基因组代谢网络模拟构建提供了理论基础。     

基于代谢网络预测菌种基因改造靶点方法的研究进展

高产特定产品的人工细胞工厂的构建需要对野生菌株进行大量的基因工程改造,近年来随着大量基因组尺度代谢网络模型的构建,人们提出了多种基于代谢网络分析预测基因改造靶点以使某一目标化合物合成最优的方法。这些方法利用基因组尺度代谢网络模型中的反应计量关系约束和反应不可逆性约束等,通过约束优化的方法预测可使产物合成最大化的改造靶点,避免了传统的通过相关途径的直观分析确定靶点的方法的局限性和主观性,为细胞工厂的理性设计提供了新的思路。以下结合作者的实际研究经验,对这些菌种优化方法的原理、优缺点及适用性等进行详细介绍,并讨论了目前存在的主要问题和未来的研究方向,为人们针对不同目标产品选择合适的方法及预测结果的可靠性评估提供了指导。     

海洋氮循环过程及基于基因组代谢网络模型的预测

海洋氮循环在地球元素循环中充当着必不可少的角色。海洋氮循环是由一系列氧化还原反应构成的生物化学过程。固氮作用和氮同化作用为生态系统提供了生物可用氮(铵盐)。硝化作用可进一步将铵盐氧化为硝酸盐,硝酸盐又可以通过反硝化作用转化为氮气。整个氮循环实现了海洋中不同含氮无机盐间的转换。微生物是海洋氮循环的重要驱动者,海洋氮循环的研究可以帮助理解海洋生物与地球环境相互作用及协同演化的机制,从而更好地保护地球生态环境。随着氮循环关键微生物基因组尺度代谢网络模型的发表,研究者可以利用代谢网络模型来研究不同氮循环过程的效率、环境因子对氮循环过程的影响以及解析氮循环及生物网络的内在机理等,从而帮助人们更深入地研究海洋氮转化机制。本文主要综述了海洋氮循环过程中各个转化过程的主要微生物,以及基因组尺度代谢网络模型在分析氮循环中的应用。     

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。     

Pan-cancer analysis of the metabolic reaction network

Metabolic reprogramming is considered a hallmark of malignant transformation. However, it is not clear whether the network of metabolic reactions expressed by cancers of different origin differ from each other or from normal human tissues. In this study, we reconstructed functional and connected genome-scale metabolic models for 917 primary tumor samples across 13 types based on the probability of expression for 3765 reference metabolic genes in the sample. This network-centric approach revealed that tumor metabolic networks are largely similar in terms of accounted reactions, despite diversity in the expression of the associated genes. On average, each network contained 4721 reactions, of which 74% were core reactions (present in >95% of all models). Whilst 99.3% of the core reactions were classified as housekeeping also in normal tissues, we identified reactions catalyzed by ARG2, RHAG, SLC6 and SLC16 family gene members, and PTGS1 and PTGS2 as core exclusively in cancer. These findings were subsequently replicated in an independent validation set of 3388 genome-scale metabolic models. The remaining 26% of the reactions were contextual reactions. Their inclusion was dependent in one case (GLS2) on the absence of TP53 mutations and in 94.6% of cases on differences in cancer types. This dependency largely resembled differences in expression patterns in the corresponding normal tissues, with some exceptions like the presence of the NANP-encoded reaction in tumors not from the female reproductive system or of the SLC5A9-encoded reaction in kidney-pancreatic-colorectal tumors. In conclusion, tumors expressed a metabolic network virtually overlapping the matched normal tissues, raising the possibility that metabolic reprogramming simply reflects cancer cell plasticity to adapt to varying conditions thanks to redundancy and complexity of the underlying metabolic networks. At the same time, the here uncovered exceptions represent a resource to identify selective liabilities of tumor metabolism.     

Systemic metabolic reactions are obtained by singular value decomposition of genome-scale stoichiometric matrices

Genome-scale metabolic networks can be reconstructed. The systemic biochemical properties of these networks can now be studied. Here, genome-scale reconstructed metabolic networks were analysed using singular value decomposition (SVD). All the individual biochemical conversions contained in a reconstructed metabolic network are described by a stoichiometric matrix (S). SVD of S led to the definition of the underlying modes that characterize the overall biochemical conversions that take place in a network and rank-ordered their importance. The modes were shown to correspond to systemic biochemical reactions and they could be used to identify the groups and clusters of individual biochemical reactions that drive them. Comparative analysis of the Escherichia coli, Haemophilus influenzae, and Helicobacter pylori genome-scale metabolic networks showed that the four dominant modes in all three networks correspond to: (1) the conversion of ATP to ADP, (2) redox metabolism of NADP, (3) proton-motive force, and (4) inorganic phosphate metabolism. The sets of individual metabolic reactions deriving these systemic conversions, however, differed among the three organisms. Thus, we can now define systemic metabolic reactions, or eigen-reactions, for the study of systems biology of metabolism and have a basis for comparing the overall properties of genome-specific metabolic networks.     

F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks

ABSTRACT: BACKGROUND: Flux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategiesto knock out target reactions. RESULTS: We present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner. CONCLUSIONS: We propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/     

Use of genome-scale metabolic models for understanding microbial physiology

The exploitation of microorganisms in industrial, medical, food and environmental biotechnology requires a comprehensive understanding of their physiology. The availability of genome sequences and accumulation of high-throughput data allows gaining understanding of microbial physiology at the systems level, and genome-scale metabolic models represent a valuable framework for integrative analysis of metabolism of microorganisms. Genome-scale metabolic models are reconstructed based on a combination of genome sequence information and detailed biochemical information, and these reconstructed models can be used for analyzing and simulating the operation of metabolism in response to different stimuli. Here we discuss the requirement for having detailed physiological insight in order to exploit microorganisms for production of fuels, chemicals and pharmaceuticals. We further describe the reconstruction process of genome-scale metabolic models and different algorithms that can be used to apply these models to gain improved insight into microbial physiology.     

Constraints-based genome-scale metabolic simulation for systems metabolic engineering

Random mutagenesis and selection approaches used traditionally for the development of industrial strains have largely been complemented by metabolic engineering, which allows purposeful modification of metabolic and cellular characteristics by using recombinant DNA and other molecular biological techniques. As systems biology advances as a new paradigm of research thanks to the development of genome-scale computational tools and high-throughput experimental technologies including omics, systems metabolic engineering allowing modification of metabolic, regulatory and signaling networks of the cell at the systems-level is becoming possible. In silico genome-scale metabolic model and its simulation play increasingly important role in providing systematic strategies for metabolic engineering. The in silico genome-scale metabolic model is developed using genomic annotation, metabolic reactions, literature information, and experimental data. The advent of in silico genome-scale metabolic model brought about the development of various algorithms to simulate the metabolic status of the cell as a whole. In this paper, we review the algorithms developed for the system-wide simulation and perturbation of cellular metabolism, discuss the characteristics of these algorithms, and suggest future research direction.     

Genome-scale analysis of Mannheimia succiniciproducens metabolism

Mannheimia succiniciproducens MBEL55E isolated from bovine rumen is a capnophilic gram-negative bacterium that efficiently produces succinic acid, an industrially important four carbon dicarboxylic acid. In order to design a metabolically engineered strain which is capable of producing succinic acid with high yield and productivity, it is essential to optimize the whole metabolism at the systems level. Consequently, in silico modeling and simulation of the genome-scale metabolic network was employed for genome-scale analysis and efficient design of metabolic engineering experiments. The genome-scale metabolic network of M. succiniciproducens consisting of 686 reactions and 519 metabolites was constructed based on reannotation and validation experiments. With the reconstructed model, the network structure and key metabolic characteristics allowing highly efficient production of succinic acid were deciphered; these include strong PEP carboxylation, branched TCA cycle, relative weak pyruvate formation, the lack of glyoxylate shunt, and non-PTS for glucose uptake. Constraints-based flux analyses were then carried out under various environmental and genetic conditions to validate the genome-scale metabolic model and to decipher the altered metabolic characteristics. Predictions based on constraints-based flux analysis were mostly in excellent agreement with the experimental data. In silico knockout studies allowed prediction of new metabolic engineering strategies for the enhanced production of succinic acid. This genome-scale in silico model can serve as a platform for the systematic prediction of physiological responses of M. succiniciproducens to various environmental and genetic perturbations and consequently for designing rational strategies for strain improvement.