Tanshinone IIA ameliorates energy metabolism dysfunction of pulmonary fibrosis using 13C metabolic flux analysis

Evidence indicates that metabolic reprogramming characterized by the changes in cellular metabolic patterns contributes to the pathogenesis of pulmonary fibrosis (PF). It is considered as a promising therapeutic target anti-PF. The well-documented against PF properties of Tanshinone IIA (Tan IIA) have been primarily attributed to its antioxidant and anti-inflammatory potency. Emerging evidence suggests that Tan IIA may target energy metabolism pathways, including glycolysis and tricarboxylic acid (TCA) cycle. However, the detailed and advanced mechanisms underlying the anti-PF activities remain obscure. In this study, we applied [U-13C]-glucose metabolic flux analysis (MFA) to examine metabolism flux disruption and modulation nodes of Tan IIA in PF. We identified that Tan IIA inhibited the glycolysis and TCA flux, thereby suppressing the production of transforming growth factor-β1 (TGF-β1)-dependent extracellular matrix and the differentiation and proliferation of myofibroblasts in vitro. We further revealed that Tan IIA inhibited the expression of key metabolic enzyme hexokinase 2 (HK2) by inhibiting phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR)/hypoxia-inducible factor 1α (HIF-1α) pathway activities, which decreased the accumulation of abnormal metabolites. Notably, we demonstrated that Tan IIA inhibited ATP citrate lyase (ACLY) activity, which reduced the collagen synthesis pathway caused by cytosol citrate consumption. Further, these results were validated in a mouse model of bleomycin-induced PF. This study was novel in exploring the mechanism of the occurrence and development of Tan IIA in treating PF using 13C-MFA technology. It provided a novel understanding of the mechanism of Tan IIA against PF from the perspective of metabolic reprogramming.

Constrain-based analysis of gene deletion on the metabolic flux redistribution of Saccharomyces Cerevisiae

Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the deletion impact of 438 calculable genes, one by one, on the metabolic flux redistribution of S. cere-visiae_iND750. Next we analyzed the correlation between v (describing deletion impact of one gene) and d (connection degree of one gene) and the correlation between v and Vgene (flux sum controlled by one gene), and found that both of them were not of linear relation. Furthermore, we sought out 38 important genes that most greatly affected the metabolic flux distribution, and determined their functional subsystems. We also found that many of these key genes were related to many but not several subsystems. Because the in silico model of S. cere-visiae_iND750 has been tested by many ex-periments, thus is credible, we can conclude that the result we obtained has biological sig-nificance.

Investigation of the Metabolic Flux Analysis Method for Overdetermined System

With the xanthan synthesis in Xanthomoaas campestris as an example, two methods for metabolic flux analysis of overdetermined system, the experimental data error minimization method and the equation error minimization method, are compared from their mathematical basis, rationality of the results and the easiness of computation. The results show that the experimental data error minimization method is appropriate in metabolic flux analysis of overdetermined system.

Analysis of Data on Xanthan Fermentation in Stationary Phase Using Black Box and Metabolic Network Models

The xanthan fermentation data in the stationary phase was analyzed using the black box and the metabolic network models. The data consistency is checked through the elemental balance in the black box model. In the metabolic network model, the metabolic flux distribution in the cell is calculated using the metabolic flux analysis method, then the maintenance coefficients is calculated.

Calculation of Empirical and True Maintenance Coefficients by Flux Balance Analysis

The stoichiometric matrix of a simplified metabolic network inBacillus Subtillis was constructed from the flux balance equations,which were used for reconciliation of the measured rates anddetermination of the inner metabolic rates. Thus more reliableresults of the true and empirical maintenance coefficients wereobtained. The true maintenance coefficient is linearly related to thespecific growth rate and changes with the P/O ratio. The measuredbiomass yield of adenosine triphosphate (ATP) is also linearlyrelated to the P/O ratio.

Elementary Flux Mode Analysis for Optimized Ethanol Yield in Anaerobic Fermentation of Glucose with Saccharomyces cerevisiae

Elementary flux mode （EFM） analysis was used in the metabolic analysis of central carbon metabolism in Saccharomyces cerevisiae based on constructed cellular network. Calculated from the metabolic model, the ethanol-producing pathway No. 37 furthest converts the substrate into ethanol among the 78 elementary flux modes. The in silico metabolic phenotypes predicted based on this analysis fit well with the fermentation performance of the engineered strains, KAM3 and KAMll, which confirmed that EFM analysis is valid to direct the construction of Saccharomyces cerevisiae engineered strains, to increase the ethanol yield.

A novel hyper-cube shrink algorithm to predict metabolic fluxes based on enzyme activity only

OBJECTIVE One of the long-expected goals of genome-scale metabolic modeling is to evaluate the influence of the perturbed enzymes to the yield of an expected end product.METHDOS Metabolic control analysis(MCA)performs such role to calculate the sensitivity of flux change upon that of enzymes under the framework of ordinary differential equation(ODE)models,which are restricted in small-scale networks and require explicit kinetic parameters.The constraint-based models,like flux balance analysis(FBA),lack of the room of performing MCA because they are parameters-free.In this study,we developed a hyper-cube shrink algorithm(HCSA)to incorporate the enzymatic properties to the FBA model by introducing a pair of parameters for each reaction.Our algorithm was able to handle not only prediction of knockout strains but also strains with an adjustment of expression level of certain enzymes.RESULTS We first showed the concept by applying HCSA to a simplest three-nodes network.Then we show the HCSA possesses Michaelis-Menten like behaviors characterized by steady state of ODE.We obtained good prediction of a synthetic network in Saccharomyces cerevisiae producing voilacein and analogues.Finally we showed its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast.CONCLUSION We have developed an algorithm the impact on fluxes when certain enzymes were inhibited or activated.It provides us a powerful tool to evaluate the consequences of enzyme inhibitor or activator.

Flux Balance Analysis Reveals Potential Anti-HIV-1 Metabolic Targets

Background:Human immunodeficiency virus type 1(HIV-1)remains a persistent global health challenge.Therefore,a continuous exploration of novel therapeutic strategies is essential.A comprehensive understanding of how HIV-1 utilizes the cellular metabolism machinery for replication can provide insights into new therapeutic approaches.Methods:In this study,we performed a flux balance analysis using a genome-scale metabolic model(GEM)integrated with an HIV-1 viral biomass objective function to identify potential targets for anti–HIV-1 interventions.We generated a GEM by integrating an HIV-1 production reaction into CD4+T cells and optimized for both host and virus optimal states as objective functions to depict metabolic profiles of cells in the status for optimal host biomass maintenance or for optimal HIV-1 virion production.Differential analysis was used to predict biochemical reactions altered optimal for HIV-1 production.In addition,we conducted in silico simulations involving gene and reaction knock-outs to identify potential anti–HIV-1 targets,which were subsequently validated by human phytohemagglutinin(PHA)blasts infected with HIV-1.Results:Differential analysis identified several altered biochemical reactions,including increased lysine uptake and oxidative phosphorylation(OXPHOS)activities in the virus optima compared with the host optima.In silico gene and reaction knock-out simulations revealed de novo pyrimidine synthesis,and OXPHOS could serve as potential anti–HIV-1 metabolic targets.In vitro assay confirmed that targeting OXPHOS using metformin could suppress the replication of HIV-1 by 56.6%(385.4±67.5 pg/mL in the metformintreated group vs.888.4±32.3 pg/mL in the control group,P<0.001).Conclusion:Our integrated host-virus genome-scale metabolic study provides insights on potential targets(OXPHOS)for anti-HIV therapies.

Noise effect in metabolic networks

Constraint-based models such as flux balance analysis （FBA） are a powerful tool to study biological metabolic networks. Under the hypothesis that cells operate at an optimal growth rate as the result of evolution and natural selection, this model successfully predicts most cellular behaviours in growth rate. However, the model ignores the fact that cells can change their cellular metabolic states during evolution, leaving optimal metabolic states unstable. Here, we consider all the cellular processes that change metabolic states into a single term‘noise＇, and assume that cells change metabolic states by randomly walking in feasible solution space. By simulating a state of a cell randomly walking in the constrained solution space of metabolic networks, we found that in a noisy environment cells in optimal states tend to travel away from these points. On considering the competition between the noise effect and the growth effect in cell evolution, we found that there exists a trade-off between these two effects. As a result, the population of the cells contains different cellular metabolic states, and the population growth rate is at suboptimal states.

Metabolic flux analysis on arachidonic acid fermentation

The analysis of flux distributions in metabolic networks has become an important approach for understand-ing the fermentation characteristics of the process.A model of metabolic flux analysis of arachidonic acid(AA)synthesis in Mortierella alpina ME-1 was established and carbon flux distributions were estimated in different fermentation phases with different concentrations of N-source.During the expo-nential,decelerating and stationary phase,carbon fluxes to AA were 3.28%,8.80%and 6.97%,respectively,with sufficient N-source broth based on the flux of glucose uptake,and those were increased to 3.95%,19.21%and 39.29%,respectively,by regulating the shifts of carbon fluxes via fermentation with limited N-source broth and adding 0.05% NaNO_(3) at 96 h.Eventually AA yield was increased from 1.3 to 3.5 g·L^(−1).These results suggest a way to improve AA fermentation,that is,fermentation with limited N-source broth and adding low concentration N-source during the stationary phase.

基于MFA的青岛市城阳区物质代谢研究

基于欧盟的物质流分析(MFA)框架,对青岛市城阳区的物质代谢状态和代谢强度进行了系统分析.结果表明:1995─2004年城阳区资源投入量和污染物排放量分别增长3.6和1.8倍,化石燃料和矿物约占资源投入量的50.26%~76.30%,进口资源量所占比例呈逐年增长趋势,经济发展的资源外部依赖性较强.万元GDP资源投入由6.01 t降至3.56 t,万元GDP污染物排放量也降低了63.74%,表明城阳区经济发展的资源利用效率和生态效率已有不同程度的提高,但仍远高于发达国家20世纪末期的水平,因此提高资源生产力和控制污染物排放是实现城阳区可持续发展的必由之路.

Expanded flux variability analysis on metabolic network of Escherichia coli

Flux balance analysis, based on the mass conservation law in a cellular organism, has been extensively employed to study the interplay between structures and functions of cellular metabolic networks. Consequently, the phenotypes of the metabolism can be well elucidated. In this paper, we introduce the Expanded Flux Variability Analysis (EFVA) to characterize the intrinsic nature of metabolic reactions, such as flexibility, modularity and essentiality, by exploring the trend of the range, the maximum and the minimum flux of reactions. We took the metabolic network of Escherichia coli as an example and analyzed the variability of reaction fluxes under different growth rate constraints. The average variabil-ity of all reactions decreases dramatically when the growth rate increases. Consider the noise effect on the metabolic system, we thus argue that the microorganism may practically grow under a suboptimal state. Besides, under the EFVA framework, the reactions are easily to be grouped into catabolic and anabolic groups. And the anabolic groups can be further assigned to specific biomass constitute. We also discovered the growth rate dependent essentiality of reactions.

Progress in the applications of flux analysis of metabolic networks

The metabolic network has become a hot topic in the area of system biology and flux-based analysis plays a very important role in understanding the characteristics of organism metabolic networks. We review mainly the static methods for analyzing metabolic networks such as flux balance analysis (FBA), minimization of metabolic adjustment (MOMA), regulatory on / off minimization (ROOM), and dynamic flux balance analysis with linear quadratic regulator (DFBA-LQR). Then several kinds of commonly used software for flux analysis are introduced briefly and compared with each other. Finally, we highlight the applications of metabolic network flux analysis, especially its usage combined with other biological characteristics and its usage for drug design. The idea of combining the analysis of metabolic networks and other biochemical data has been gradually promoted and used in several aspects such as the combination of metabolic flux and the regulation of gene expression, the influence of protein evolution caused by metabolic flux, the relationship between metabolic flux and the topological characteristics, the optimization of metabolic engineering. More comprehensive and accurate properties of metabolic networks will be obtained by integrating metabolic flux analysis, network topological characteristics and dynamic modeling.

Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design

Metabolic modeling and machine learning(ML)are crucial components of the evolving next-generation tools in systems and synthetic biology,aiming to unravel the intricate relationship between genotype,phenotype,and the environment.Nonetheless,the comprehensive exploration of integrating these two frameworks,and fully harnessing the potential of fluxomic data,remains an unexplored territory.In this study,we present,rigorously evaluate,and compare ML-based techniques for data integration.The hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol production.Specifically,we investigated the influence of succinate dehydrogenase(SDH)on ethanol biosynthesis in Saccharomyces cerevisiae through shake flask experiments.The findings indicate a noticeable increase in ethanol yield,ranging from 6%to 10%,in SDH subunit gene knockout strains compared to the wild-type strain.Moreover,in pursuit of a high-yielding strain for ethanol production,dual-gene deletion experiments were conducted targeting glycerol-3-phosphate dehydrogenase(GPD)and SDH.The results unequivocally demonstrate significant enhancements in ethanol production for the engineered strains Δsdh4Δgpd1,Δsdh5Δgpd1,Δsdh6Δgpd1,Δsdh4Δgpd2,Δsdh5Δgpd2,and Δsdh6Δgpd2,with improvements of 21.6%,27.9%,and 22.7%,respectively.Overall,the results highlighted that integrating mechanistic flux features substantially improves the prediction of gene knockout strains not accounted for in metabolic reconstructions.In addition,the finding in this study delivers valuable tools for comprehending and manipulating intricate phenotypes,thereby enhancing prediction accuracy and facilitating deeper insights into mechanistic aspects within the field of synthetic biology.

Impaired tricarboxylic acid cycle flux and mitochondrial aerobic respiration during isoproterenol induced myocardial ischemia is rescued by bilobalide

There is an urgent need to elucidate the pathogenesis of myocardial ischemia(MI)and potential drug treatments.Here,the anti-MI mechanism and material basis of Ginkgo biloba L.extract(GBE)were studied from the perspective of energy metabolism flux regulation.Metabolic flux analysis(MFA)was performed to investigate energy metabolism flux disorder and the regulatory nodes of GBE components in isoproterenol(ISO)-induced ischemia-like cardiomyocytes.It showed that[U-13 C]glucose derived m+2 isotopologues from the upstream tricarboxylic acid(TCA)cycle metabolites were markedly accumulated in ISO-injured cardiomyocytes,but the opposite was seen for the downstream metabolites,while their total cellular concentrations were increased.This indicates a blockage of carbon flow from glycolysis and enhanced anaplerosis from other carbon sources.A Seahorse test was used to screen for GBE components with regulatory effects on mitochondrial aerobic respiratory dysfunction.It showed that bilobalide protected against impaired mitochondrial aerobic respiration.MFA also showed that bilobalide significantly modulated the TCA cycle flux,reduced abnormal metabolite accumulation,and balanced the demand of different carbon sources.Western blotting and PCR analysis showed that bilobalide decreased the enhanced expression of key metabolic enzymes in injured cells.Bilobalide’s efficacy was verified by in vivo experiments in rats.This is the first report to show that bilobalide,the active ingredient of GBE,protects against MI by rescuing impaired TCA cycle flux.This provides a new mechanism and potential drug treatment for MI.It also shows the potential of MFA/Seahorse combination as a powerful strategy for pharmacological research on herbal medicine.

提升头孢菌素C发酵效率的复合碳源补料工艺开发及代谢机制

开发了一种以葡萄糖和豆油为底物碳源的复合补料策略,能够显著提升头孢菌素C(CPC)的合成效率,使其产量提升了14.8%,并观测到碳源变化在宏观呼吸代谢、中间代谢物、细胞内通量分配和还原力NADPH利用方面产生了全局影响,其中二氧化碳释放速率(CER)的变化表明在葡萄糖和豆油复合补料策略下细胞中心代谢网络的通量发生了明显改变,碳流向CPC的通量得到增强,同时促进了蛋氨酸和丝氨酸的胞外吸收。胞内有机酸α-酮戊二酸、丙酮酸、苹果酸的浓度明显提升,有利于谷氨酸、缬氨酸和半胱氨酸等核心氨基酸的合成,从而有利于CPC生产。主成分分析(PCA)的载荷测定结果表明大多数氨基酸和CPC合成呈正相关,谷氨酸的胞内含量与CPC合成的关联性最强,而α-氨基己二酸和赖氨酸积累反而不利于CPC合成。细胞还原力方面,豆油和葡萄糖组合工艺有助于NADPH更多地用于CPC合成,使得CPC快速合成时对NADPH的大量需求得到了满足。这些结果表明改变底物能够对细胞内部的代谢产生全方位的影响,复合补料策略在优化CPC合成中具有巨大潜力。

FluxExplorer: A general platform for modeling and analyses of metabolic net-works based on stoichiometry

Stoichiometry-based analyses of meta- bolic networks have aroused significant interest of systems biology researchers in recent years. It is necessary to develop a more convenient modeling platform on which users can reconstruct their network models using completely graphical operations, and explore them with powerful analyzing modules to get a better understanding of the properties of metabolic systems. Herein, an in silico platform, FluxExplorer, for metabolic modeling and analyses based on stoichiometry has been developed as a publicly available tool for systems biology research. This platform integrates various analytic approaches, in- cluding flux balance analysis, minimization of meta- bolic adjustment, extreme pathways analysis, shadow prices analysis, and singular value decom- position, providing a thorough characterization of the metabolic system. Using a graphic modeling process, metabolic networks can be reconstructed and modi- fied intuitively and conveniently. The inconsistencies of a model with respect to the FBA principles can be proved automatically. In addition, this platform sup- ports systems biology markup language (SBML). FluxExplorer has been applied to rebuild a metabolic network in mammalian mitochondria, producing meaningful results. Generally, it is a powerful and very convenient tool for metabolic network modeling and analysis.

L-色氨酸生物合成的代谢流量分析

建立了谷氨酸棒杆菌合成L 色氨酸 (L Try)的代谢流量平衡模型 ,应用该模型计算出发酵中后期的代谢流分布并通过MATLAB软件线性规划得到Try理想代谢流分布。结果表明 75 1 5 %的碳架进入糖酵解 ,2 4 85 %的碳架进入HMP途径 ;但与理想代谢流相比 ,应从遗传改造和发酵控制方面降低TCA循环的代谢流 ,减少副产氨基酸的生成 ,摸索最适的溶氧控制对提高Try产率至关重要。

城市物质流分析框架及其指标体系构建

借鉴国内外物质流分析的研究成果,结合我国城市物质代谢特点,建立了城市物质流分析的框架及指标体系。该框架以城市社会经济系统物质通量的变化为核心,增加了对城市社会经济系统可持续能力的考察以及对城市和区域循环经济贡献的关注。论文识别了城市物质流分析中系统边界界定等关键问题,并提出了解决方法;指标体系在借鉴国家层面物质流分析指标体系的基础上,注重对城市经济系统内部循环及不同城市经济系统间的物质循环的考察,增加了再生资源输入量、内部资源回收量、可回收废物输出量、新鲜水输入量、中水回用量等指标,用于表征城市可持续发展的能力及实践成果。

产朊假丝酵母分批发酵生产谷胱甘肽的代谢通量分析

根据产朊假丝酵母利用葡萄糖作为惟一碳源生物合成谷胱甘肽的代谢网络,利用代谢通量分析方法分析了谷胱甘肽分批发酵不同阶段各代谢途径碳通量的分布和变化规律.在此基础上,通过分阶段温度控制和L-半胱氨酸添加等策略,调节谷胱甘肽分批发酵过程中的代谢通量分布,谷胱甘肽生物合成的产量明显提高,最大增加幅度分别达到23%和91%·同时对这些策略下谷胱甘肽的过量合成机理进行了解释.