基于基因组挖掘的农抗120活性成分制霉菌素和丰加霉素的发现和鉴定

【目的】分析刺孢吸水链霉菌北京变种(农抗120产生菌)基因组和次级代谢产物组分,研究并鉴定农抗120产生菌中未被发现的活性组分。【方法】利用antiSMASH在线分析农抗120产生菌Streptomyces hygrospinosusvar.beijingensis基因组信息,锁定可能的制霉菌素和丰加霉素生物合成基因簇。利用HPLC和LC-MS等分析方法对农抗120产生菌发酵产物进行分析,同时利用制霉菌素和丰加霉素标准品作为对照,以鉴定该菌株代谢组分中的次级代谢产物。此外,通过构建目标基因簇大片段缺失突变株,并对所得突变株发酵产物进行检测,以确定生物合成基因簇与目的代谢产物的对应关系。【结果】本研究综合利用基因组序列分析、基因缺失突变株构建以及代谢产物检测方法,鉴定了农抗120产生菌中制霉菌素和丰加霉素两种活性成分,并确定了负责这些化合物合成的基因簇。【结论】本研究所构建的多重基因簇失活突变株为挖掘刺孢吸水链霉菌北京变种更多的天然次级代谢产物奠定了基础。     

干酪乳杆菌12A碳源代谢的比较基因组学分析

【目的】在基因组学水平上,对干酪乳杆菌的碳源代谢特性及调控机制进行研究。【方法】基于BioCyc和MetaCyc数据库,利用Pathway Tools对菌株12A及7株已公布全基因序列的干酪乳杆菌进行全基因组水平的碳源代谢比较分析。【结果】全基因组比较分析结果显示,干酪乳杆菌12A可以将9种糖转运到细胞内代谢利用;可以将多种寡糖和多糖在细胞外水解成半乳糖和葡萄糖;干酪乳杆菌12A可经异型乳酸发酵或混合酸发酵途径生成乙醇及其副产物。【结论】干酪乳杆菌12A可以代谢多种类型碳源,底物选择范围宽泛,而且可以作为工业乙醇发酵的特定菌株;利用比较基因组学方法建立基因结构与细菌代谢能力的联系是可靠的。     

应用蛋白质组学的方法分析缺铁培养对化脓性链球菌MGAS5005的影响

【目的】研究铁缺失对化脓性链球菌的影响,并寻找摄铁系统中的关键蛋白。【方法】以化脓链球菌为模型,利用含Fe和不含Fe的培养基对细菌进行培养,收集全细胞蛋白进行双向电泳,定量软件分析电泳图谱,质谱鉴定差异蛋白,进而通过生物信息学分析蛋白上下游关系,从中找到关键蛋白。【结果】鉴定出20个差异蛋白,并用Cytoscape软件对差异蛋白相互关系网络进行节点分析找到其中5个瓶颈分子。【结论】在培养基中的Fe3+缺乏时,细菌的生物合成和含氮化合物、生物大分子等重要代谢受到很大影响,这为进一步阐明细菌铁代谢机制奠定了基础。     

海洋来源节菱孢菌ZSDS1-F3中PKS-NRPS类天然产物挖掘

【目的】以基因组信息为指导,定向激活海洋来源真菌Arthrinium arundinisZSDS1-F3中沉默的聚酮合成酶-非核糖体肽合成酶(PKS-NRPS)类生物合成基因簇,鉴定次级代谢产物结构。【方法】通过启动子工程和异源表达的策略激活实验室培养条件下沉默或低表达的生物合成基因簇,实现目标化合物的分离,通过HR-ESI-MS和NMR数据分析鉴定产物结构,结合基因重组和生物信息学分析结果推导化合物的生物合成途径。【结果】依据基因组生物信息学分析,从海洋来源真菌A. arundinis ZSDS1-F3中选取一个编码PKS-NRPS类次级代谢产物的生物合成基因簇开展研究,在宿主Aspergillus nidulansA1145中实现了基因簇的异源表达,从中分离到2个新化合物,并推导了其生物合成途径。【结论】基因组信息指导下的天然产物挖掘,可以目标明确地分离产物,加快真菌中新颖天然产物的发现步伐。     

耐低温酵母Curvibasidium rogersii菌株在松萝样品中的首次分离鉴定及基于基因组分析的生物特性探究

【目的】对西藏松萝地衣来源的两株非常规酵母进行分离鉴定,并通过基因组序列分析探究其生物学特性和应用潜力。【方法】从西藏来源的松萝地衣样品内部分离得到2株耐低温酵母菌株,通过26S D1/D2和ITS序列比对分析以及生理生化实验进行菌种鉴定;通过全基因组序列分析和验证探究菌株的生物特性。【结果】两株酵母菌株经鉴定均为Curvibasidium rogersii,可以在10℃低温良好生长,在20℃生长最佳,25℃及以上温度生长缓慢或不生长。对其进行基因组测序和基因组挖掘,测序结果发现,其基因组注释出功能的部分与产油脂的低温酵母白冬孢酵母Leucosporidium creatinivorum具有最高相似性,尼罗红染色发现两株酵母都能够生产油脂,另外在基因组序列中还发现了可能参与木糖代谢的相关蛋白编码基因,实验证明两个酵母菌株可以利用木糖生长。【结论】首次分离鉴定了来自西藏松萝的酵母C. rogersii,为充分开发利用松萝和其他地衣来源微生物,以及利用可代谢木糖的新资源酵母生产微生物油脂提供了基础。     

宏基因组学分析揭示深古菌Bathyarchaeota B242的代谢特征

【背景】海洋沉积物中蕴含着丰富的微生物资源,估算约2.9×1029个细胞,与海水中的微生物总量相当。但是由于缺少可培养物,大部分的微生物缺乏生理特征、代谢方式以及生态功能的相关研究。深古菌(Bathyarchaeota)是一类典型的未培养微生物,在全球海洋沉积物中普遍存在,并且具有很高的丰度。【目的】对深古菌代谢潜能及其在海洋沉积物中发挥的生态功能进行更加深入的研究。【方法】应用宏基因组学的技术手段,对采集自瓜伊马斯盆地的深海热液沉积物样本进行了分析,获得了一个接近完整的深古菌基因组Bathyarchaeota B242。【结果】对Bathyarchaeota B242基因组的分析发现,其具有以降解蛋白质和多种碳水化合物为主的异养代谢途径,同时还具有通过还原型乙酰辅酶A途径实现的自养途径。【结论】同时具有自养和异养代谢途径对Bathyarchaeota B242适应低物质能量供给环境下的生存起到重要作用。     

基因组分析揭示拟茎点霉XP-8产松脂醇及其糖苷等多种次级代谢产物的合成途径

【目的】通过解析拟茎点霉属XP-8的基因组序列信息,揭示该菌株潜在的代谢途径,并分析松脂醇及其糖苷化合物等次级代谢产物生物合成相关的关键基因。【方法】使用Illumina Hi Seq 2500高通量测序平台对拟茎点霉XP-8菌株进行全基因组测序,并通过不同软件对测序数据进行序列拼接,基因预测与功能注释。【结果】组装后的拟茎点霉XP-8基因组大小为55.2 Mb,GC含量53.5%,含有17094个蛋白编码基因和310个非编码基因。获得了松脂醇及其糖苷化合物等次级代谢产物生物合成相关的基因。系统发育分析揭示出拟茎点霉XP-8与5种子囊菌共有12635个同源基因和5626个基因家族。【结论】拟茎点霉XP-8具有用于合成松脂醇及其糖苷化合物等多种次级代谢物的基因组基础,为下一步的代谢工程改造提供依据。     

原核生物全基因组中16S rRNA基因的识别

【目的】识别原核生物全基因组中的16S rRNA基因。【方法】本文依据基因序列的GC碱基含量、碱基3-周期性和马尔可夫链3个方面的特性,构建了识别原核生物全基因组中16S rRNA基因的三层过滤模型。【结果】经检验,模型的特异性、敏感性和马修斯相关系数分别为99.58%、91.60%和91.49%。【结论】结果表明,本文所提出的方法可以高效、准确地识别出16S rRNA基因。     

卡伍尔氏链霉菌NA4的Ⅱ型聚酮基因簇的靶向激活及其产物鉴定

【目的】以基因组信息为导向,定向激活海洋来源卡伍尔氏链霉菌（Streptomyces cavourensis） NA4中沉默的Ⅱ型聚酮类次级代谢产物生物合成基因簇,鉴定新产生的次级代谢产物的结构和抑菌活性。【方法】通过添加启动子和敲除负调控基因的方法激活实验室培养条件下沉默或低表达的生物合成基因簇,并完成目标化合物的分离与纯化,通过电喷雾质谱（electrospray ionization-mass spectrometry,ESI-MS）和核磁共振（nuclear magnetic resonance,NMR）数据分析鉴定目标化合物结构,对目标化合物进行抑菌活性鉴定,基于生物信息学信息推导化合物的生物合成途径。【结果】根据基因组生物信息学分析,从海洋来源链霉菌Streptomyces cavourensis NA4中选取一个编码PKSⅡ型次级代谢产物的生物合成基因簇开展研究,成功激活目标基因簇,从中分离到1个PKSⅡ型化合物,推导了其生物合成途径并进行了抑菌活性鉴定。【结论】基因组导向下的天然产物挖掘,可以目标明确地分离产物,充分挖掘链霉菌编码次级代谢产物的潜力。     

基于文献挖掘的巨大芽胞杆菌代谢网络模型的构建与分析

【目的】通过挖掘实验性文献,建立巨大芽胞杆菌事实型代谢网络模型,以详尽解析生理特性,优化其生理功能。【方法】从PubMed、Derwent Innovations Index、中国知网等公共文献(专利)数据库中获取与巨大芽胞杆菌(Bacillus megaterium)相关的实验性文献建立本地文献数据库。采用文献挖掘工具获取功能基因、酶、代谢物和生化反应等信息,以其为基础构建代谢网络粗模型,进一步借助KEGG等数据库修正以及Matlab程序的模拟得到精细模型(系统生物学标记语言的形式)。【结果】最终的精细模型共有292个生化反应、378个代谢物、220个酶和217个基因。以1.62 mmol/g cell/h的葡萄糖底物吸收速率为限制性条件,模拟的菌体比生长速率为0.089 h-1,略低于实验值0.11 h-1。此外,嘧啶代谢途径的单基因敲除模拟结果表明,准确率为90%。【结论】该代谢网络模型涵盖了中心代谢途径、维生素B12合成途径和氨基酸代谢途径,并在一定程度上反映了营养底物与基因对巨大芽胞杆菌生长性能的影响。     

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...     

From Enzyme Kinetics to Metabolic Network Modeling – Visualization Tool for Enhanced Kinetic Analysis of Biochemical Network Models

Model‐based analysis of enzyme kinetics allows the determination of optimal conditions for their use in biocatalysis. For biotransformations or fermentative approaches the modeling of metabolic pathways or complex metabolic networks is necessary to obtain model‐based predictions of steps which limit product formation within the network. To set up adequate kinetic models, relevant mechanistic information about enzyme properties is required and can be taken from in vitro studies with isolated enzymes or from in vivo investigations using stimulus‐response experiments which provide a lot of kinetic information about the metabolic network. But with increasing number of reaction steps and regulatory interdependencies in the network structure the amount of simulation data dramatically increases and the simulation results from the dynamic models become difficult to analyze and interpret. Demonstrated for an Escherichia coli model of the central carbon metabolism, methods for visualization and animation of simulation data were applied and extended to facilitate model analysis and biological interpretation. The dynamic metabolite pool and metabolic flux changes were visualized simultaneously by a software tool. In addition, a new quantification method for enzyme activation/inhibition was proposed, and this information was implemented in the metabolic visualization.     

应用代谢网络模型解析工业微生物胞内代谢

为了快速、高效地理解工业微生物胞内代谢特征,寻找潜在的代谢工程改造靶点,基因组规模代谢网络模型(GSMM)作为一种系统生物学工具越来越受到人们的关注。文中在回顾GSMM 20年发展历程的基础上,分析了当前GSMM的研究现状,总结了GSMM的构建及分析方法,从预测细胞表型和指导代谢工程两个方面阐述了GSMM在解析工业微生物胞内代谢中的应用,并展望了GSMM未来的发展趋势。     

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。     

Genome-scale metabolic model in guiding metabolic engineering of microbial improvement

In the past few decades, despite all the significant achievements in industrial microbial improvement, the approaches of traditional random mutation and selection as well as the rational metabolic engineering based on the local knowledge cannot meet today’s needs. With rapid reconstructions and accurate in silico simulations, genome-scale metabolic model (GSMM) has become an indispensable tool to study the microbial metabolism and design strain improvements. In this review, we highlight the application of GSMM in guiding microbial improvements focusing on a systematic strategy and its achievements in different industrial fields. This strategy includes a repetitive process with four steps: essential data acquisition, GSMM reconstruction, constraints-based optimizing simulation, and experimental validation, in which the second and third steps are the centerpiece. The achievements presented here belong to different industrial application fields, including food and nutrients, biopharmaceuticals, biopolymers, microbial biofuel, and bioremediation. This strategy and its achievements demonstrate a momentous guidance of GSMM for metabolic engineering breeding of industrial microbes. More efforts are required to extend this kind of study in the meantime.     

Systems-level analysis of genome-scalein silico metabolic models using MetaFluxNet

The systems-level analysis of microbes with myriad of heterologous data generated by omics technologies has been applied to improve our understanding of cellular function and physiology and consequently to enhance production of various bioproducts. At the heart of this revolution residesin silico genome-scale metabolic model. In order to fully exploit the power of genome-scale model, a systematic approach employing user-friendly software is required. Metabolic flux analysis of genome-scale metabolic network is becoming widely employed to quantify the flux distribution and validate model-driven hypotheses. Here we describe the development of an upgraded MetaFluxNet which allows (1) construction of metabolic models connected to metabolic databases, (2) calculation of fluxes by metabolic flux analysis, (3) comparative flux analysis with flux-profile visualization, (4) the use of metabolic flux analysis markup language to enable models to be exchanged efficiently, and (5) the exporting of data from constraints-based flux analysis into various formats. MetaFluxNet also allows cellular physiology to be predicted and strategies for strain improvement to be developed from genome-based information on flux distributions. This integrated software environment promises to enhance our understanding on metabolic network at a whole organism level and to establish novel strategies for improving the properties of organisms for various biotechnological applications.     

Parallel labeling experiments with [U-(13)C]glucose validate E. coli metabolic network model for (13)C metabolic flux analysis

(13)C-metabolic flux analysis (MFA) is a widely used method for measuring intracellular metabolic fluxes in living cells. (13)C MFA relies on several key assumptions: (1) the assumed metabolic network model is complete, in that it accounts for all significant enzymatic and transport reactions; (2) (13)C-labeling measurements are accurate and precise; and (3) enzymes and transporters do not discriminate between (12)C- and (13)C-labeled metabolites. In this study, we tested these inherent assumptions of (13)C MFA for wild-type E. coli by parallel labeling experiments with [U-(13)C]glucose as tracer. Cells were grown in six parallel cultures in custom-constructed mini-bioreactors, starting from the same inoculum, on medium containing different mixtures of natural glucose and fully labeled [U-(13)C]glucose, ranging from 0% to 100% [U-(13)C]glucose. Macroscopic growth characteristics of E. coli showed no observable kinetic isotope effect. The cells grew equally well on natural glucose, 100% [U-(13)C]glucose, and mixtures thereof. (13)C MFA was then used to determine intracellular metabolic fluxes for several metabolic network models: an initial network model from literature; and extended network models that accounted for potential dilution effects of isotopic labeling. The initial network model did not give statistically acceptable fits and produced inconsistent flux results for the parallel labeling experiments. In contrast, an extended network model that accounted for dilution of intracellular CO(2) by exchange with extracellular CO(2) produced statistically acceptable fits, and the estimated metabolic fluxes were consistent for the parallel cultures. This study illustrates the importance of model validation for (13)C MFA. We show that an incomplete network model can produce statistically unacceptable fits, as determined by a chi-square test for goodness-of-fit, and return biased metabolic fluxes. The validated metabolic network model for E. coli from this study can be used in future investigations for unbiased metabolic flux measurements.     

Integrated 13C-metabolic flux analysis of 14 parallel labeling experiments in Escherichia coli

The use of parallel labeling experiments for ¹³C metabolic flux analysis (¹³C-MFA) has emerged in recent years as the new gold standard in fluxomics. The methodology has been termed COMPLETE-MFA, short for complementary parallel labeling experiments technique for metabolic flux analysis. In this contribution, we have tested the limits of COMPLETE-MFA by demonstrating integrated analysis of 14 parallel labeling experiments with Escherichia coli. An effort on such a massive scale has never been attempted before. In addition to several widely used isotopic tracers such as [1,2-¹³C]glucose and mixtures of [1-¹³C]glucose and [U-¹³C]glucose, four novel tracers were applied in this study: [2,3-¹³C]glucose, [4,5,6-¹³C]glucose, [2,3,4,5,6-¹³C]glucose and a mixture of [1-¹³C]glucose and [4,5,6-¹³C]glucose. This allowed us for the first time to compare the performance of a large number of isotopic tracers. Overall, there was no single best tracer for the entire E. coli metabolic network model. Tracers that produced well-resolved fluxes in the upper part of metabolism (glycolysis and pentose phosphate pathways) showed poor performance for fluxes in the lower part of metabolism (TCA cycle and anaplerotic reactions), and vice versa. The best tracer for upper metabolism was 80% [1-¹³C]glucose+20% [U-¹³C]glucose, while [4,5,6-¹³C]glucose and [5-¹³C]glucose both produced optimal flux resolution in the lower part of metabolism. COMPLETE-MFA improved both flux precision and flux observability, i.e. more independent fluxes were resolved with smaller confidence intervals, especially exchange fluxes. Overall, this study demonstrates that COMPLETE-MFA is a powerful approach for improving flux measurements and that this methodology should be considered in future studies that require very high flux resolution.     

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。     

FluxAnalyzer: exploring structure,pathways, and flux distributions in metabolic networks on interactive flux maps

MOTIVATION: The analysis of structure, pathways and flux distributions in metabolic networks has become an important approach for understanding the functionality of metabolic systems. The need of a user-friendly platform for stoichiometric modeling of metabolic networks in silico is evident. RESULTS: The FluxAnalyzer is a package for MATLAB and facilitates integrated pathway and flux analysis for metabolic networks within a graphical user interface. Arbitrary metabolic network models can be composed by instances of four types of network elements. The abstract network model is linked with network graphics leading to interactive flux maps which allow for user input and display of calculation results within a network visualization. Therein, a large and powerful collection of tools and algorithms can be applied interactively including metabolic flux analysis, flux optimization, detection of topological features and pathway analysis by elementary flux modes or extreme pathways. The FluxAnalyzer has been applied and tested for complex networks with more than 500,000 elementary modes. Some aspects of the combinatorial complexity of pathway analysis in metabolic networks are discussed. AVAILABILITY: Upon request from the corresponding author. Free for academic users (license agreement). Special contracts are available for industrial corporations. SUPPLEMENTARY INFORMATION: http://www.mpi-magdeburg.mpg.de/projects/fluxanalyzer.