Analyzing the effect of decreasing cytosolic triosephosphate isomerase on Solanum tuberosum hairy root cells using a kinetic–metabolic model

A kinetic–metabolic model of Solanum tuberosum hairy roots is presented in the interest of understanding the effect on the plant cell metabolism of a 90% decrease in cytosolic triosephosphate isomerase (cTPI, EC 5.3.1.1) expression by antisense RNA. The model considers major metabolic pathways including glycolysis, pentose phosphate pathway, and TCA cycle, as well as anabolic reactions leading to lipids, nucleic acids, amino acids, and structural hexoses synthesis. Measurements were taken from shake flask cultures for six extracellular nutrients (sucrose, fructose, glucose, ammonia, nitrate, and inorganic phosphate) and 15 intracellular compounds including sugar phosphates (G6P, F6P, R5P, E4P) and organic acids (PYR, aKG, SUCC, FUM, MAL) and the six nutrients. From model simulations and experimental data it can be noted that plant cell metabolism redistributes metabolic fluxes to compensate for the cTPI decrease, leading to modifications in metabolites levels. Antisense roots showed increased exchanges between the pentose phosphate pathway and the glycolysis, an increased oxygen uptake and growth rate. Biotechnol. Bioeng. 2013; 110: 924–935. © 2012 Wiley Periodicals, Inc.     

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.     

An ecofriendly approach for bioremediation of contaminated water environment: Potential contribution of a coastal seaweed community to environmental improvement

High levels of heavy metals like copper ions in many industrial based effluents lead to serious environmental and health problems. Biosorption is a potential environmental biotechnology approach for biotreatment of aquatic sites polluted with heavy metal ions. Seaweeds have received great attention for their high bioremediation potential in recent years. However, the co-application of marine macroalgae for removal of heavy metals from wastewater is very limited. Thus, for the first time in literature, a coastal seaweed community composed of Chaetomorpha sp., Polysiphonia sp., Ulva sp. and Cystoseira sp. species was applied to remove copper ions from synthetic aqueous medium in this study. The biosorption experiments in batch mode were conducted to examine the effects of operating variables including pH, biosorbent amount, metal ion concentration and contact time on the biosorption process. The biosorption behavior of biosorbent was described by various equilibrium, kinetic and thermodynamic models. The biosorption of copper ions was strongly influenced by the operating parameters. The results indicated that the equilibrium data of biosorption were best modeled by Sips isotherm model. The values of mean free energy of biosorption computed from Dubinin-Radushkevich isotherm model and the standard Gibbs free energy change indicated a feasible, spontaneous and physical biotreatment system. The pseudo-second-order rate equation successfully defined the kinetic behavior of copper biosorption. The pore diffusion also played role in the control of biosorption process. The maximum copper uptake capacity of biosorbent was found to be greater than those of many other biosorbents. The obtained results revealed that this novel biosorbent could be a promising material for copper ion bioremediation implementations.     

Microbial community dynamics and evaluation of bioremediation strategies in oil-impacted salt marsh sediment microcosms

Microbial community dynamics in wetlands microcosms emended with commercial products (surfactant, a biological agent, and nutrients) designed to enhance bioremediation was followed for 3 months. The effectiveness of enhanced degradation was assessed by determining residual concentrations of individual petroleum hydrocarbons by GC/MS. The size and composition of the sediment microbial community was assessed using a variety of indices, including bacterial plate counts, MPNs, and DNA hybridizations with domain- and group-specific oligonucleotide probes. The addition of inorganic nutrients was the most effective treatment for the enhancement of oil degradation, resulting in marked degradation of petroleum alkanes and a lesser extent of degradation of aromatic oil constituents. The enhanced degradation was associated with increases in the amount of extractable microbial DNA and Streptomyces in the sediment, although not with increased viable counts (plate counts, MPN). Bacteria introduced with one of the proprietary products were still detected in the microcosms after 3 months, but were not a major quantitative constituent of the community. The biological product enhanced oil degradation relative to the control, but to a lesser extent than the nutrient additions alone. In contrast, application of the surfactant to the oil-impacted sediment decreased oil degradation. Journal of Industrial Microbiology & Biotechnology (2001) 27, 72–79. Received 18 March 2001/ Accepted in revised form 09 June 2001     

Computational identification of altered metabolism using gene expression and metabolic pathways

Understanding altered metabolism is an important issue because altered metabolism is often revealed as a cause or an effect in pathogenesis. It has also been shown to be an important factor in the manipulation of an organism's metabolism in metabolic engineering. Unfortunately, it is not yet possible to measure the concentration levels of all metabolites in the genome‐wide scale of a metabolic network; consequently, a method that infers the alteration of metabolism is beneficial. The present study proposes a computational method that identifies genome‐wide altered metabolism by analyzing functional units of KEGG pathways. As control of a metabolic pathway is accomplished by altering the activity of at least one rate‐determining step enzyme, not all gene expressions of enzymes in the pathway demonstrate significant changes even if the pathway is altered. Therefore, we measure the alteration levels of a metabolic pathway by selectively observing expression levels of significantly changed genes in a pathway. The proposed method was applied to two strains of Saccharomyces cerevisiae gene expression profiles measured in very high‐gravity (VHG) fermentation. The method identified altered metabolic pathways whose properties are related to ethanol and osmotic stress responses which had been known to be observed in VHG fermentation because of the high sugar concentration in growth media and high ethanol concentration in fermentation products. With the identified altered pathways, the proposed method achieved best accuracy and sensitivity rates for the Red Star (RS) strain compared to other three related studies (gene‐set enrichment analysis (GSEA), significance analysis of microarray to gene set (SAM‐GS), reporter metabolite), and for the CEN.PK 113‐7D (CEN) strain, the proposed method and the GSEA method showed comparably similar performances. Biotechnol. Bioeng. 2009;103: 835–843. © 2009 Wiley Periodicals, Inc.     

黄土高原石油污染土壤微生物群落结构及其代谢特征

针对污染胁迫下土壤微生物群落变化和代谢变异等问题,基于平板稀释法和Biolog微平板分析方法,研究了陕北黄土高原石油污染土壤微生物群落结构、代谢特征及其功能多样性。结果表明,不同类群的土壤微生物对石油污染胁迫的响应不同,污染土壤细菌和真菌数量高出清洁土壤1个数量级,而污染土壤的放线菌数量极显著减少(P0.01);污染土壤和清洁土壤微生物对糖类和多聚物类碳源较易利用,污染土壤微生物总体上代谢碳源的种类和活性均低于清洁土壤。微生物群落主成分分析(PCA)表明,石油污染土壤和清洁土壤的微生物群落存在显著差异(P0.01),起分异作用的碳源主要为糖类,其次是羧酸类和氨基酸类;随着土壤石油含量增加,典型变量值变异(离散)增大,土壤微生物群落结构稳定性降低。微生物群落多样性分析表明,Shannon丰富度指数(H)、McIntosh均一度指数(U)和Simpson优势度指数(1/D)均达到极显著差异(P0.01),污染土壤微生物群落H和U低于清洁土壤,但是一定浓度的石油污染可以刺激土壤微生物群落中优势种群的生长,1/D增高。研究结果为陕北黄土高原石油污染区土壤微生物修复提供理论基础。     

Basic and applied aspects of metabolic diversity: The phosphoenolpyruvate node

The phosphoenolpyruvate (PEP) node represents a metabolic crossroad where carbon is distributed into several metabolic pathways. This node is specially important for the industrial production of several metabolites. Depending on the organism and its habitat, the enzymes that utilize PEP are regulated by different effectors, and each branch of the node is important in PEP consumption. In this review we will focus our attention on the metabolic diversity of this node.     

Development and assessment of a preliminary kinetic model for in situ pah bioremediation

Polycyclic aromatic hydrocarbons (PAHs) are a group of organic nonaqueous‐phase liquid (NAPL) contaminants of critical environmental concern. The treatment method used for a contaminated soil depends primarily on the nature and extent of the contamination as well as the cost effectiveness of the method. Current research has shown that bioremediation is perhaps the simplest and most economic process for the treatment of large contaminated areas. Although bioremediation feasibility and effectiveness has been well documented, additional information is required to fully understand subsurface kinetics. Specifically, the importance and effect of interactions between bacteria, supplemental nutrients, oxygen source, contaminant, and soil type must be understood. Preliminary respirometer experiments have been conducted to address these factors for the development of a kinetic model for both steady‐state and unsteady‐state conditions.     

The identification of enzyme targets for the optimization of a valine producing Corynebacterium glutamicum strain using a kinetic model

The enzyme targets for the rational optimization of a Corynebacterium glutamicum strain constructed for valine production are identified by analyzing the control of flux in the valine/leucine pathway. The control analysis is based on measurements of the intracellular metabolite concentrations and on a kinetic model of the reactions in the investigated pathway. Data‐driven and model‐based methods are used and evaluated against each other. The approach taken gives a quantitative evaluation of the flux control and it is demonstrated how the understanding of flux control is used to reach specific recommendations for strain optimization. The flux control coefficients (FCCs) with respect to the valine excretion rate were calculated, and it was found that the control is distributed mainly between the acetohydroxyacid synthase enzyme (FCC = 0.32), the branched chain amino acid transaminase (FCC = 0.27), and the exporting translocase (FCC = 0.43). The availability of the precursor pyruvate has substantial influence on the valine flux, whereas the cometabolites are less important as demonstrated by the calculation of the respective response coefficients. The model is further used to make in‐silico predictions of the change in valine flux following a change in enzyme level. A doubling of the enzyme level of valine translocase will result in an increase in valine flux of 31%. By optimizing the enzyme levels with respect to valine flux it was found that the valine flux can be increased by a factor 2.5 when the optimal enzyme levels are implemented. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration

Over the last decade, the field of cancer metabolism has mainly focused on studying the role of tumorigenic metabolic rewiring in supporting cancer proliferation. Here, we perform the first genome‐scale computational study of the metabolic underpinnings of cancer migration. We build genome‐scale metabolic models of the NCI‐60 cell lines that capture the Warburg effect (aerobic glycolysis) typically occurring in cancer cells. The extent of the Warburg effect in each of these cell line models is quantified by the ratio of glycolytic to oxidative ATP flux (AFR), which is found to be highly positively associated with cancer cell migration. We hence predicted that targeting genes that mitigate the Warburg effect by reducing the AFR may specifically inhibit cancer migration. By testing the anti‐migratory effects of silencing such 17 top predicted genes in four breast and lung cancer cell lines, we find that up to 13 of these novel predictions significantly attenuate cell migration either in all or one cell line only, while having almost no effect on cell proliferation. Furthermore, in accordance with the predictions, a significant reduction is observed in the ratio between experimentally measured ECAR and OCR levels following these perturbations. Inhibiting anti‐migratory targets is a promising future avenue in treating cancer since it may decrease cytotoxic‐related side effects that plague current anti‐proliferative treatments. Furthermore, it may reduce cytotoxic‐related clonal selection of more aggressive cancer cells and the likelihood of emerging resistance.     

不同腐熟程度有机物料对土壤微生物群落功能多样性的影响

室内培养条件下,施用有机物料初期土壤微生物群落代谢功能Shannon多样性指数降低,中期又提高。有机物料种类和腐熟水平可明显影响土壤微生物群落对Biolog微平板中碳源的利用能力,土壤微生物群落利用各类碳源的能力随培养试验的延长而降低,在25d内新鲜有机物处理对碳源的利用率的下降速度低于同类腐熟有机物料处理。糖类是各处理土壤微生物群落的主要利用碳源。土壤微生物群落主成分分析表明,在施用有机物料后25d内腐熟水平是影响土壤微生物群落的主要因素,新鲜有机物处理的土壤微生物群落相似,腐熟有机物处理的土壤微生物群落相似,培养50d后各处理的土壤微生物群落无差异。     

Microbial community dynamics during assays of harbour oil spill bioremediation: a microscale simulation study

AIMS: Microcosm experiments simulating an oil spill event were performed to evaluate the response of the natural microbial community structure of Messina harbour seawater following the accidental load of petroleum. METHODS AND RESULTS: An experimental harbour seawater microcosm, supplemented with nutrients and crude oil, was monitored above 15 days in comparison with unpolluted ones (control microcosms). Bacterial cells were counted with a Live/Dead BacLight viability kit; leucine aminopeptidase, beta-glucosidase, alkaline phosphatase, lipase and esterase enzymes were measured using fluorogenic substrates. The microbial community dynamic was monitored by isolation of total RNA, RT-PCR amplification of 16S rRNA, cloning and sequencing. Oil addition stimulated an increase of the total bacterial abundance, leucine aminopeptidase and phosphatase activity rates, as well as a change in the community structure. This suggested a prompt response of micro-organisms to the load of petroleum hydrocarbons. CONCLUSIONS: The present study on the viability, specific composition and metabolic characteristics of the microbial community allows a more precise assessment of oil pollution. Both structural and functional parameters offer interesting perspectives as indicators to monitor changes caused by petroleum hydrocarbons. SIGNIFICANCE AND IMPACT OF THE STUDY: A better knowledge of microbial structural successions at oil-polluted sites is essential for environmental bioremediation. Data obtained in microcosm studies improve our understanding of natural processes occurring during oil spills.     

Fungi as a toolbox for sustainable bioremediation of pesticides in soil and water

Pesticides can help reduce yield losses caused by pests, pathogens, and weeds, but their overuse causes serious environmental pollution. They are persistent in the environment and are biomagnified through the food chain, becoming a serious health hazard for humankind. Bioremediation, where microbes are used to degrade pesticides in situ, is a useful technology. This review summarizes data on the fungi involved in the biodegradation of chemical pesticides and their application in soil and water bioremediation. Indications for future studies in this field are given.     

SNPeffect: identifying functional roles of SNPs using metabolic networks

Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome‐wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single‐nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype‐to‐phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme‐coding genes. To this end, we also constructed a genome‐scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth‐determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.     

SWI/SNF染色质重塑复合物亚基Baf60a调控能量代谢稳态的研究进展

代谢综合征是一种全球性的慢性流行病,其发病机理由遗传与环境等因素共同决定.表观遗传修饰是在基因的核苷酸序列不发生改变的情况下,通过可遗传的变化调控基因表达.近年来研究发现,表观遗传修饰能够应答于环境因素、调控基因表达和信号转导进程.其中,染色质重塑复合物SWI/SNF(SWItch/Sucrose non fermen...     

Bacterial community structure of a pesticide-contaminated site and assessment of changes induced in community structure during bioremediation

The introduction of culture-independent molecular screening techniques, especially based on 16S rRNA gene sequences, has allowed microbiologists to examine a facet of microbial diversity not necessarily reflected by the results of culturing studies. The bacterial community structure was studied for a pesticide-contaminated site that was subsequently remediated using an efficient degradative strain Arthrobacter protophormiae RKJ100. The efficiency of the bioremediation process was assessed by monitoring the depletion of the pollutant, and the effect of addition of an exogenous strain on the existing soil community structure was determined using molecular techniques. The 16S rRNA gene pool amplified from the soil metagenome was cloned and restriction fragment length polymorphism studies revealed 46 different phylotypes on the basis of similar banding patterns. Sequencing of representative clones of each phylotype showed that the community structure of the pesticide-contaminated soil was mainly constituted by Proteobacteria and Actinomycetes. Terminal restriction fragment length polymorphism analysis showed only nonsignificant changes in community structure during the process of bioremediation. Immobilized cells of strain RKJ100 enhanced pollutant degradation but seemed to have no detectable effects on the existing bacterial community structure.     

A method for analysis and design of metabolism using metabolomics data and kinetic models: Application on lipidomics using a novel kinetic model of sphingolipid metabolism

We present a model-based method, designated Inverse Metabolic Control Analysis (IMCA), which can be used in conjunction with classical Metabolic Control Analysis for the analysis and design of cellular metabolism. We demonstrate the capabilities of the method by first developing a comprehensively curated kinetic model of sphingolipid biosynthesis in the yeast Saccharomyces cerevisiae. Next we apply IMCA using the model and integrating lipidomics data. The combinatorial complexity of the synthesis of sphingolipid molecules, along with the operational complexity of the participating enzymes of the pathway, presents an excellent case study for testing the capabilities of the IMCA. The exceptional agreement of the predictions of the method with genome-wide data highlights the importance and value of a comprehensive and consistent engineering approach for the development of such methods and models. Based on the analysis, we identified the class of enzymes regulating the distribution of sphingolipids among species and hydroxylation states, with the D-phospholipase SPO14 being one of the most prominent. The method and the applications presented here can be used for a broader, model-based inverse metabolic engineering approach.