The metabolic environment of cancer

The tumor cell has a very distinctive metabolism. It acts as a metabolic trap for host nutrients thus taking vital compounds for the metabolism of the host. Depending on the particular tumor growing pattern, cancer cells use preferentially glucose or amino acids for their energetic or biosynthetic needs. Lipids, fatty acids in particular, can also be taken up by the tumor cell. In addition, it can also release some compounds into the host circulation which are not normally produced by the original cell before neoplastic transformation. Some of these compounds affect the metabolism of the host in an unfavorable way since they can oppose the host's metabolic responses, which sustain homeostasis. The final product is that the metabolic machinery of these cells allows them to grow continuously in an uncontrolled manner. The consequences of tumor invasion on the host's metabolism are varied. They have, however, one thing in common: the reduction of the metabolic efficiency of the host. Muscular protein depletion, increased gluconeogenesis, uncoupling of oxidative phosphorylation constitute the main metabolic responses of the host as a result of tumor invasion. The net result of all these metabolic changes is profound energy imbalance which normally ends with cachexia and, eventually, death.     

Robust metabolic adaptation underlying tumor progression

Tumor metabolism represents the end point of many signal cascades recruited by oncogenic activation. Energy metabolism of cancer cells attracted the attention of biochemists over eight decades ago. For example, high consume of glucose and high lactate production under aerobic conditions make up one of the most fundamental characteristics of cancer cells and has been exploited for diagnosis. At the same time, study of the metabolic status of tumor cells during tumor progression reveals characteristic adaptations during carcinogenesis. Although these metabolic adaptations are not the main defects that cause cancer, they may confer advantages to survive. In this review, we discuss the main metabolic hot spots and their relationship with main tumor progression events. An accurate metabolic map of the many tumor phenotypes could offer new options in the treatment of cancer.     

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.     

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.     

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.     

宏观生态过程的代谢调控研究进展

宏观（生态学）和微观（分子生物学）生命科学的交汇犹如两翼的联动将带动生命科学再次腾飞.综述了由宏观生态过程和代谢的个体大小依赖的定量规律为核心的代谢生态学相关研究进展.在综合分析最新动态和我们研究心得的基础上,建立了植物有效资源与耦合的光、水分和化学营养元素间关系的立方体模型,明确提出了生态过程（或代谢）速率是环境资源、现有生物量（反应器）和分子系统三要素相互作用结果的规律,并预测作为宏观生态过程与微观生物学的交汇点,代谢生态学的发展有可能带动生命科学的整合和进一步腾飞.     

Low metabolic rates in primitive hunters and weaver spiders

The rates of oxygen consumption and carbon dioxide release of primitive hunters and weaver spiders, the Chilean Recluse spider Loxosceles laeta Nicolet (Araneae: Sicariidae) and the Chilean Tiger spider Scytodes globula Nicolet (Araneae: Scytodidae), are analyzed, and their relationship with body mass is studied. The results are compared with the metabolic data available for other spiders. A low metabolic rate is found both for these two species and other primitive hunters and weavers, such as spiders of the families Dysderidae and Plectreuridae. The metabolic rate of this group is lower than in nonprimitive spiders, such as the orb weavers (Araneae: Araneidae). The results reject the proposition of a general relationship for metabolic rate for all land arthropods (related to body mass) and agree with the hypothesis that metabolic rates are affected not only by sex, reproductive and developmental status, but also by ecology and life style, recognizing here, at least in the araneomorph spiders, a group having low metabolism, comprising the primitive hunters and weaver spiders, and another group comprising the higher metabolic rate web building spiders (e.g. orb weavers).     

Insights into plant metabolic networks from steady-state metabolic flux analysis

Steady-state metabolic flux analysis (MFA) is an experimental approach that allows the measurement of multiple fluxes in the core network of primary carbon metabolism. It is based on isotopic labelling experiments, and although well established in the analysis of micro-organisms, and some mammalian systems, the extension of the method to plant cells has been challenging because of the extensive subcellular compartmentation of the metabolic network. Despite this difficulty there has been substantial progress in developing robust protocols for the analysis of heterotrophic plant metabolism by steady-state MFA, and flux maps have now been published that reflect the metabolic phenotypes of excised root tips, developing embryos and cotyledons, hairy root cultures, and cell suspensions under a variety of physiological conditions. There has been a steady improvement in the quality, extent and statistical reliability of these analyses, and new information is emerging on the performance of the plant metabolic network and the contributions of specific pathways.     

Studies of metabolic control using NMR and molecular genetics

The techniques of NMR spectroscopy and molecular genetics have provided new and powerful approaches to studying the control and organisation of cellular metabolism in vivo. We review here our recent applications of these methodologies to the study of energy metabolism in yeast and mammalian cells. © 1997 John Wiley & Sons, Ltd.     

Structural robustness of metabolic networks with respect to multiple knockouts

We present a generalised framework for analysing structural robustness of metabolic networks, based on the concept of elementary flux modes (EFMs). Extending our earlier study on single knockouts [Wilhelm, T., Behre, J., Schuster, S., 2004. Analysis of structural robustness of metabolic networks. IEE Proc. Syst. Biol. 1(1), 114-120], we are now considering the general case of double and multiple knockouts. The robustness measures are based on the ratio of the number of remaining EFMs after knockout vs. the number of EFMs in the unperturbed situation, averaged over all combinations of knockouts. With the help of simple examples we demonstrate that consideration of multiple knockouts yields additional information going beyond single-knockout results. It is proven that the robustness score decreases as the knockout depth increases.We apply our extended framework to metabolic networks representing amino acid anabolism in Escherichia coli and human hepatocytes, and the central metabolism in human erythrocytes. Moreover, in the E. coli model the two subnetworks synthesising amino acids that are essential and those that are non-essential for humans are studied separately. The results are discussed from an evolutionary viewpoint. We find that E. coli has the most robust metabolism of all the cell types studied here. Considering only the subnetwork of the synthesis of non-essential amino acids, E. coli and the human hepatocyte show about the same robustness.     

Predicting metabolic biomarkers of human inborn errors of metabolism

Early diagnosis of inborn errors of metabolism is commonly performed through biofluid metabolomics, which detects specific metabolic biomarkers whose concentration is altered due to genomic mutations. The identification of new biomarkers is of major importance to biomedical research and is usually performed through data mining of metabolomic data. After the recent publication of the genome‐scale network model of human metabolism, we present a novel computational approach for systematically predicting metabolic biomarkers in stochiometric metabolic models. Applying the method to predict biomarkers for disruptions of red‐blood cell metabolism demonstrates a marked correlation with altered metabolic concentrations inferred through kinetic model simulations. Applying the method to the genome‐scale human model reveals a set of 233 metabolites whose concentration is predicted to be either elevated or reduced as a result of 176 possible dysfunctional enzymes. The method's predictions are shown to significantly correlate with known disease biomarkers and to predict many novel potential biomarkers. Using this method to prioritize metabolite measurement experiments to identify new biomarkers can provide an order of a 10‐fold increase in biomarker detection performance.     

Identification of a four-gene metabolic signature predicting overall survival for hepatocellular carcinoma

While hundreds of consistently altered metabolic genes had been identified in hepatocellular carcinoma (HCC), the prognostic role of them remains to be further elucidated. Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas—Liver Hepatocellular Carcinoma and GSE14520 data set from the Gene Expression Omnibus database. Univariate Cox regression analysis and lasso Cox regression model established a novel four-gene metabolic signature (including acetyl-CoA acetyltransferase 1, glutamic-oxaloacetic transaminase 2, phosphatidylserine synthase 2, and uridine-cytidine kinase 2) for HCC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. The signature was significantly correlated with other negative prognostic factors such as higher α-fetoprotein. The signature was found to be an independent prognostic factor for HCC survival. Nomogram including the signature shown some clinical net benefit for overall survival prediction. Furthermore, gene set enrichment analyses revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four-gene metabolic signature for HCC prognosis prediction. The signature might reflect the dysregulated metabolic microenvironment and provided potential biomarkers for metabolic therapy and treatment response prediction in HCC.     

Bacterial infection reinforces host metabolic flux from arginine to spermine for NLRP3 inflammasome evasion

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布朗葡萄藻的培养基选择及其产物代谢规律

通过对布朗葡萄藻分别在Chu13、Chu13×2和BG-11培养基中培养结果的比较,发现在气升式光照生物反应器中Chu13培养基最有利于布朗葡萄藻的生长和烃的合成,培养15d后,其生物量和粗烃质量分数分别为1.82g/L和58.7%;棕榈酸、油酸和亚麻酸是布朗葡萄藻的主要脂肪酸组成,Chu13培养获得的藻体不饱和脂肪酸比例最高。Chu13培养基中布朗葡萄藻代谢规律的研究表明:粗烃含量随着生物量的增加而逐渐增大,15d后粗烃产量达到最大值1.07g/L,不同生长周期烃的组成保持一致,布朗葡萄藻的烃主要由C33H56和C34H58组成;在布朗葡萄藻生长周期中,不饱和脂肪酸的比例显著上升,培养15d达到64%以上。     

Kinetic metabolic modelling for the control of plant cells cytoplasmic phosphate

A previously developed kinetic metabolic model for plant metabolism was used in a context of identification and control of intracellular phosphate (Pi) dynamics. Experimental data from batch flask cultures of Eschscholtiza californica cells was used to calibrate the model parameters for the slow dynamics (growth, nutrition, anabolic pathways, etc.). Perturbation experiments were performed using a perfusion small-scale bioreactor monitored by in vivo³¹P NMR. Parameter identification for Pi metabolism was done by measuring the cells dynamic response to different inputs for extracellular Pi (two pulse-response experiments and a step-response experiment). The calibrated model can describe Pi translocation between the cellular pools (vacuole and cytoplasm). The effect of intracellular Pi management on ATP/ADP and phosphomonoesters concentrations is also described by the model. The calibrated model is then used to develop a control strategy on the cytoplasmic Pi pool. From the identification of the systems dynamics, a proportional-integral controller was designed and tuned. The closed-loop control was implemented in the small-scale NMR bioreactor and experimental results were in accordance with model predictions. Thus, the calibrated model is able to predict cellular behaviour for phosphate metabolism and it was demonstrated that it is possible to control the intracellular level of cytoplasmic Pi in plant cells.     

代谢工程在微生物法生产番茄红素中的应用

番茄红素作为强抗氧化剂因具有防癌与抗癌等多种生理功能而广受关注。本文综述了代谢工程在微生物法生产番茄红素中的应用,主要讨论了代谢工程方法在扩展构建新的代谢流、增强番茄红素合成代谢流,阻断竞争支路来提高番茄红素代谢流等方面的应用。     

NAD+ metabolism: A therapeutic target for age-related metabolic disease

Abstract

Nicotinamide adenine dinucleotide (NAD) is a central metabolic cofactor by virtue of its redox capacity, and as such regulates a wealth of metabolic transformations. However, the identification of the longevity protein silent regulator 2 (Sir2), the founding member of the sirtuin protein family, as being NAD⁺-dependent reignited interest in this metabolite. The sirtuins (SIRT1-7 in mammals) utilize NAD⁺ to deacetylate proteins in different subcellular compartments with a variety of functions, but with a strong convergence on optimizing mitochondrial function. Since cellular NAD⁺ levels are limiting for sirtuin activity, boosting its levels is a powerful means to activate sirtuins as a potential therapy for mitochondrial, often age-related, diseases. Indeed, supplying excess precursors, or blocking its utilization by poly(ADP-ribose) polymerase (PARP) enzymes or CD38/CD157, boosts NAD⁺ levels, activates sirtuins and promotes healthy aging. Here, we discuss the current state of knowledge of NAD⁺ metabolism, primarily in relation to sirtuin function. We highlight how NAD⁺ levels change in diverse physiological conditions, and how this can be employed as a pharmacological strategy.