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。     

基因组尺度分析和代谢工程策略

代谢工程是近年来发展起来的新技术,随着各种组学技术的发展,高通量数据整合方法用于分析细胞的代谢网络,改造代谢途径,以提高目标产物的产量。本文就代谢工程的发展状况,基因组尺度的分析技术,以及代谢工程策略进行了综述。分析了生物信息学和系统生物学方法在代谢途径构建和代谢网络分析中的作用,并就存在的问题和可能的解决途径进行了阐述。     

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。     

Metabolomics assisted metabolic network modeling and network wide analysis of metabolites in microbiology

Metabolomics is the science of qualitatively and quantitatively analyzing low molecular weight metabolites occur in a given biological system. It provides valuable information to elucidate the functional roles and relations of different metabolites in a metabolic pathway. In recent years, a large amount of research on microbial metabolomics has been conducted. It has become a useful tool for achieving highly efficient synthesis of target metabolites. At the same time, many studies have been conducted over the years in order to integrate metabolomics data into metabolic network modeling, which has yielded many exciting results. Additionally, metabolomics also shows great advantages in analyzing the relationship of metabolites network wide. Integrating metabolomics data into metabolic network construction and applying it in network wide analysis of cell metabolism would further improve our ability to control cellular metabolism and optimize the design of cell factories for the overproduction of valuable biochemicals. This review will examine recent progress in the application of metabolomics approaches in metabolic network modeling and network wide analysis of microbial cell metabolism.     

Genome-scale analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed-batch culture

A dynamic flux balance model based on a genome-scale metabolic network reconstruction is developed for in silico analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed-batch culture. Metabolic engineering strategies previously identified for their enhanced steady-state biomass and/or ethanol yields are evaluated for fed-batch performance in glucose and glucose/xylose media. Dynamic analysis is shown to provide a single quantitative measure of fed-batch ethanol productivity that explicitly handles the possible tradeoff between the biomass and ethanol yields. Productivity optimization conducted to rank achievable fed-batch performance demonstrates that the genetic manipulation strategy and the fed-batch operating policy should be considered simultaneously. A library of candidate gene insertions is assembled and directly screened for their achievable ethanol productivity in fed-batch culture. A number of novel gene insertions with ethanol productivities identical to the best metabolic engineering strategies reported in previous studies are identified, thereby providing additional targets for experimental evaluation. The top performing gene insertions were substrate dependent, with the highest ranked insertions for glucose media yielding suboptimal performance in glucose/xylose media. The analysis results suggest that enhancements in biomass yield are most beneficial for the enhancement of fed-batch ethanol productivity by recombinant xylose utilizing yeast strains. We conclude that steady-state flux balance analysis is not sufficient to predict fed-batch performance and that the media, genetic manipulations, and fed-batch operating policy should be considered simultaneously to achieve optimal metabolite productivity.     

基于图论的代谢网络中流通代谢物处理新方法

使用图论方法对基因组尺度代谢网络进行途径搜索是目前途径设计中最常用的方法之一.然而,由于流通代谢物(如H+、H2O、CO2和ATP等)的影响,图论搜索得到的途径在生物学上经常是不可行的.虽然前人提出了一些方法对流通代谢物进行处理,但均存在一些问题,目前还没有标准的处理方法.文中基于流通代谢物在反应中作为转移磷酸等功能基...     

Parameter optimization and sensitivity analysis for large kinetic models using a real-coded genetic algorithm

Dynamic modeling is a powerful tool for predicting changes in metabolic regulation. However, a large number of input parameters, including kinetic constants and initial metabolite concentrations, are required to construct a kinetic model. Therefore, it is important not only to optimize the kinetic parameters, but also to investigate the effects of their perturbations on the overall system. We investigated the efficiency of the use of a real-coded genetic algorithm (RCGA) for parameter optimization and sensitivity analysis in the case of a large kinetic model involving glycolysis and the pentose phosphate pathway in Escherichia coli K-12. Sensitivity analysis of the kinetic model using an RCGA demonstrated that the input parameter values had different effects on model outputs. The results showed highly influential parameters in the model and their allowable ranges for maintaining metabolite-level stability. Furthermore, it was revealed that changes in these influential parameters may complement one another. This study presents an efficient approach based on the use of an RCGA for optimizing and analyzing parameters in large kinetic models.     

Stoichiometric model and metabolic flux analysis for Leptospirillum ferrooxidans

A metabolic model for Leptospirillum ferrooxidans was developed based on the genomic information of an analogous iron oxidizing bacteria and on the pathways of ferrous iron oxidation, nitrogen and CO₂ assimilation based on experimental evidence for L. ferrooxidans found in the literature. From this metabolic reconstruction, a stoichiometric model was built, which includes 86 reactions describing the main catabolic and anabolic aspects of its metabolism. The model obtained has 2 degrees of freedom, so two external fluxes were estimated to achieve a determined and observable system. By using the external oxygen consumption rate and the generation flux biomass as input data, a metabolic flux map with a distribution of internal fluxes was obtained. The results obtained were verified with experimental data from the literature, achieving a very good prediction of the metabolic behavior of this bacterium at steady state. Biotechnol. Bioeng. 2010;107:696–706. © 2010 Wiley Periodicals, Inc.     

In vivo dynamics of galactose metabolism in Saccharomyces cerevisiae: metabolic fluxes and metabolite levels

The dynamics of galactose metabolism in Saccharomyces cerevisiae was studied by analyzing the metabolic response of the CEN.PK 113-7D wild-type strain when exposed to a galactose pulse during aerobic growth in a galactose-limited steady-state cultivation at a dilution rate of 0.097 h(-1). A fast sampling technique and subsequent methanol-chloroform/solid phase extractions were applied for in vivo measurements of the dynamic changes of the AMP, ADP, ATP levels and the sugar phosphates of the Leloir pathway. The ATP level was found to be significantly lower for yeast growing under galactose limitation (0.37 +/- 0.05 micromol/g CDW) than what has been reported for growth under glucose limitation. The galactose pulse of 5.58 mM was consumed within 40 min (t = 40) and 7 min after the pulse was added cell growth stopped. Subsequently, the cells started to grow and at t = 30 the specific growth rate had recovered to half the steady-state growth rate (0.047 h(-1)). To evaluate the change in flux distribution at steady state and during the galactose transient, a stoichiometric model describing the aerobic metabolism of S. cerevisiae was set up for quantification of the metabolic fluxes. At t = 7 the flux entering the TCA cycle was low and acetate and ethanol started to be excreted to the extracellular medium. During recovery of cell growth the flux entering the TCA cycle increased again, and at t = 30 this flux exceeded the corresponding steady-state flux. During the pulse an enhanced level of Gal-1P was measured, which may be responsible for a toxic metabolic response in S. cerevisiae. The increase in the Gal-1P concentration is intensified by the low affinity of Gal7 towards Gal-1P and, hence, under the physiological conditions examined Gal7 seems to exert control over flux through the Leloir pathway.     

Sensitivity of stress and strain calculations to passive material parameters in cardiac mechanical models using unloaded geometries

Cardiac stress (load) and strain (stretch) are widely studied indicators of cardiac function and outcome, but are difficult or impossible to directly measure in relation to the cardiac microstructure. An alternative approach is to estimate these states using computer methods and image-based measurements, but this still requires knowledge of the tissue material properties and the unloaded state, both of which are difficult to determine. In this work, we tested the sensitivity of these two interdependent unknowns (reference geometry and material parameters) on stress and strain calculations in cardiac tissue. Our study used a finite element model of the human ventricle, with a hyperelastic passive material model, and was driven by a cell model mediated active contraction. We evaluated 21 different published parameter sets for the five parameters of the passive material model, and for each set we optimised the corresponding unloaded geometry and contractility parameter to model a single pressure-volume loop. The resulting mechanics were compared, and calculated systolic stresses were largely insensitive to the chosen parameter set when an unloading algorithm was used. Meanwhile, material strain calculations varied substantially depending on the choice of material parameters. These results indicate that determining the correct material and unloaded configuration may be highly important to understand strain driven processes, but less so for calculating stress estimates.     

我多组学关联分析揭示表观等位基因在拟南芥环境适应性进化中的作用及机制

表观等位基因一般是指仅由DNA甲基化差异引起的表达量不同的等位基因,对植物形态结构和各种生理过程具有重要影响。但自然条件下环境因素对植物表观等位基因的影响还不清楚,同时表观等位基因在植物环境适应性进化中的作用和机制还亟待探究。为了在全基组水平鉴定拟南芥(Arabidopsis thaliana)中与特定环境因素相关的表观等位基因,并分析它们参与拟南芥环境适应性进化的可能机制,本研究利用623株拟南芥生态型的转录组、甲基化组和种源地气候数据进行多组学关联分析,并同时进行了蛋白互作网络和基因富集分析。以春季和夏季降水量为例,本研究最终鉴定到5个基因(AGL36、AT2G34100、AT4G09360、LSU4和AT5G56910)可能具有相应的表观等位基因,基因内部或附近特定区域不同甲基化水平对它们的表达可能具有调控作用。其中与种子发育有关的印记基因AGL36首次被发现可能作为表观等位基因参与拟南芥环境适应性进化,其他4个基因均与生物胁迫响应有关。自然条件下降水量能影响当地病虫害的严重程度,而DNA甲基化能通过影响这4个免疫基因的表达来影响拟南芥免疫能力。在长期演化过程中有利于个体适应当地降水模式的表观等位基因受到正向选择,这可能是这些表观等位基因参与拟南芥降水适应性进化的潜在机制。通过蛋白互作网络、GO功能分析和KEGG通路分析,本研究还首次发现LSU4可能与LSU基因家族其他成员一样参与硫代谢网络,并通过影响硫代葡萄糖苷代谢参与拟南芥生物胁迫响应。     

A guide to metabolic flux analysis in metabolic engineering: Methods,tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, “Metabolic fluxes and metabolic engineering” (Metabolic Engineering, 1: 1–11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.     

A Methodology for Establishing Cleanup Objectives in the Unsaturated Soil Zone Using Sensitivity and Uncertainty Analysis for Chemical Fate and Transport

To support the Corrective-Measures and Cleanup-Alternatives Studies (CMS) prepared by Science Applications International Corporation (SAIC) at the Portsmouth Gaseous Diffusion Plant (PORTS) in Portsmouth, Ohio, a soil-leaching numerical analysis was conducted to help establish cleanup objectives for deep-soil contamination. For approximately 60 pollutants that exist at the PORTS site, the study defined those deep-soil concentrations that would most likely not cause groundwater contamination in excess of U.S. Environmental Protection Agency (USEPA) guidelines. These values were then used as the technical basis for defining soil-cleanup goals. Numerical modeling of environmental systems provides project managers with unique information that is not available from other sources. With its ability to quantify the important aspects of problem physics, modeling allows one to rapidly accumulate the physical insight needed to solve a problem in a systematic and focused manner. This increased understanding acquired early in the planning stages of a project permits managers to make decisions that are typically more thorough, cost effective, and defensible. This article describes one such numerical study conducted jointly by the Oak Ridge National Laboratory (ORNL) and SAIC for the PORTS.     

A method for accounting for maintenance costs in flux balance analysis improves the prediction of plant cell metabolic phenotypes under stress conditions

Flux balance models of metabolism generally utilize synthesis of biomass as the main determinant of intracellular fluxes. However, the biomass constraint alone is not sufficient to predict realistic fluxes in central heterotrophic metabolism of plant cells because of the major demand on the energy budget due to transport costs and cell maintenance. This major limitation can be addressed by incorporating transport steps into the metabolic model and by implementing a procedure that uses Pareto optimality analysis to explore the trade‐off between ATP and NADPH production for maintenance. This leads to a method for predicting cell maintenance costs on the basis of the measured flux ratio between the oxidative steps of the oxidative pentose phosphate pathway and glycolysis. We show that accounting for transport and maintenance costs substantially improves the accuracy of fluxes predicted from a flux balance model of heterotrophic Arabidopsis cells in culture, irrespective of the objective function used in the analysis. Moreover, when the new method was applied to cells under control, elevated temperature and hyper‐osmotic conditions, only elevated temperature led to a substantial increase in cell maintenance costs. It is concluded that the hyper‐osmotic conditions tested did not impose a metabolic stress, in as much as the metabolic network is not forced to devote more resources to cell maintenance.     

Structural mutation analysis of PTEN and its genotype‐phenotype correlations in endometriosis and cancer

The phosphatase and tensin homolog deleted on chromosome ten (PTEN) gene encodes a tumor suppressor phosphatase that has recently been found to be frequently mutated in patients with endometriosis, endometrial cancer and ovarian cancer. Here, we present the first computational analysis of 13 somatic missense PTEN mutations associated with these phenotypes. We found that a majority of the mutations are associated in conserved positions within the active site and are clustered within the signature motif, which contain residues that play a crucial role in loop conformation and are essential for catalysis. In silico analyses were utilized to identify the putative effects of these mutations. In addition, coarse‐grained models of both wild‐type (WT) PTEN and mutants were constructed using elastic network models to explore the interplay of the structural and global dynamic effects that the mutations have on the relationship between genotype and phenotype. The effects of the mutations reveal that the local structure and interactions affect polarity, protein structure stability, electrostatic surface potential, and global dynamics of the protein. Our results offer new insight into the role in which PTEN missense mutations contribute to the molecular mechanism and genotypic‐phenotypic correlation of endometriosis, endometrial cancer, and ovarian cancer. Proteins 2016; 84:1625–1643. © 2016 Wiley Periodicals, Inc.     

in silico analyses of metabolic pathway and protein interaction network for identification of next gen therapeutic targets in Chlamydophila pneumoniae

Chlamydophila pneumoniae, the causative agent of chronic obstructive pulmonary disease (COPD), is presently the fifth mortalitycausing chronic disease in the world. The understanding of disease and treatment options are limited represents a severe concernand a need for better therapeutics. With the advancements in the field of complete genome sequencing and computationalapproaches development have lead to metabolic pathway analysis and protein-protein interaction network which provides vitalevidence to the protein function and has been appropriate to the fields such as systems biology and drug discovery. Proteininteraction network analysis allows us to predict the most potential drug targets among large number of the non-homologousproteins involved in the unique metabolic pathway. A computational comparative metabolic pathway analysis of the host H.sapiens and the pathogen C pneumoniae AR39 has been carried out at three level analyses. Firstly, metabolic pathway analysis wasperformed to identify unique metabolic pathways and non-homologous proteins were identified. Secondly, essentiality of theproteins was checked, where these proteins contribute to the growth and survival of the organism. Finally these proteins werefurther subjected to predict protein interaction networks. Among the total 65 pathways in the C pneumoniae AR39 genome 10 wereidentified as the unique metabolic pathways which were not found in the human host, 32 enzymes were predicted as essential andthese proteins were considered for protein interaction analysis, later using various criteria''s we have narrowed down to prioritizeribonucleotide-diphosphate reductase subunit beta as a potential drug target which facilitate for the successful entry into drugdesigning.