Telmisartan as metabolic modulator: a new perspective in sports doping?

The World Antidoping Agency (WADA) has introduced some changes in the 2012 prohibited list. Among the leading innovations to the rules are that both 5-aminoimidazole-4-carboxamide-1-β-D-ribofuranoside (peroxisome proliferator-activated receptor-δ [PPAR-δ]-5' adenosine monophosphate-activated protein kinase [AMPK] agonist) and GW1516 (PPAR-δ-agonist) are no longer categorized as gene doping substances in the new 2012 prohibited list but as metabolic modulators in the class "Hormone and metabolic modulators." This may also be valid for the angotensin II receptor blocker telmisartan. It has recently been shown that telmisartan might induce similar biochemical, biological, and metabolic changes (e.g., mitochondrial biogenesis and changes in skeletal muscle fiber type) as those reported for the former call of substances. We suspect that metabolic modulators abuse such as telmisartan might become a tangible threat in sports and should be thereby targeted as an important antidoping issue. The 2012 WADA prohibited list does not provide telmisartan for a potential doping drug, but arguments supporting the consideration to include them among "metabolic modulators" are at hand.     

Quantitative analysis of Escherichia coli metabolic phenotypes within the context of phenotypic phase planes

In silico models of Escherichia coli metabolism have been developed to predict metabolic behavior and propose experimentally testable hypotheses. However, a thorough assessment of the metabolic phenotype requires well-designed experimentation and reproducible experimental techniques. A method for the quantitative analysis of E. coli metabolism in vivo within the framework of in silico phenotypic phase plane analysis is presented. Using this approach, we have quantitatively studied E. coli metabolism in various environmental conditions and nutritional media. Our experimental methodology, in combination with steady-state metabolic models, can be used to study biological properties and evaluate the metabolic capabilities of microbes.     

Quantitative elementary mode analysis of metabolic pathways: the example of yeast glycolysis

Background  

Elementary mode analysis of metabolic pathways has proven to be a valuable tool for assessing the properties and functions of biochemical systems. However, little comprehension of how individual elementary modes are used in real cellular states has been achieved so far. A quantitative measure of fluxes carried by individual elementary modes is of great help to identify dominant metabolic processes, and to understand how these processes are redistributed in biological cells in response to changes in environmental conditions, enzyme kinetics, or chemical concentrations.     

Coordinating environmental genomics and geochemistry reveals metabolic transitions in a hot spring ecosystem

We have constructed a conceptual model of biogeochemical cycles and metabolic and microbial community shifts within a hot spring ecosystem via coordinated analysis of the "Bison Pool" (BP) Environmental Genome and a complementary contextual geochemical dataset of ~75 geochemical parameters. 2,321 16S rRNA clones and 470 megabases of environmental sequence data were produced from biofilms at five sites along the outflow of BP, an alkaline hot spring in Sentinel Meadow (Lower Geyser Basin) of Yellowstone National Park. This channel acts as a >22 m gradient of decreasing temperature, increasing dissolved oxygen, and changing availability of biologically important chemical species, such as those containing nitrogen and sulfur. Microbial life at BP transitions from a 92 °C chemotrophic streamer biofilm community in the BP source pool to a 56 °C phototrophic mat community. We improved automated annotation of the BP environmental genomes using BLAST-based Markov clustering. We have also assigned environmental genome sequences to individual microbial community members by complementing traditional homology-based assignment with nucleotide word-usage algorithms, allowing more than 70% of all reads to be assigned to source organisms. This assignment yields high genome coverage in dominant community members, facilitating reconstruction of nearly complete metabolic profiles and in-depth analysis of the relation between geochemical and metabolic changes along the outflow. We show that changes in environmental conditions and energy availability are associated with dramatic shifts in microbial communities and metabolic function. We have also identified an organism constituting a novel phylum in a metabolic "transition" community, located physically between the chemotroph- and phototroph-dominated sites. The complementary analysis of biogeochemical and environmental genomic data from BP has allowed us to build ecosystem-based conceptual models for this hot spring, reconstructing whole metabolic networks in order to illuminate community roles in shaping and responding to geochemical variability.     

Some observations on the physiological effects of gaseous ions

This paper reviews some of the evidence pertaining to the biological effects of gaseous ions. It appears to us that experimental data now available warrant the conclusion that reproducible biological effects occur in a variety of living forms including microorganisms, higher plants, insects, animals and man. The responses generally are limited in extent and to indict air ions as their sole cause requires great care in defining the experimental conditions. Some progress has been made in detecting the fundamental biochemical reactions associated with ion-induced physiological changes. The ready availability of excellent apparatus for generation and measurement of air-ions, coupled with present-day knowledge about their properties, makes critical experimentation possible.     

Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry

Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography-mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography-MS (GC-MS) and ultraperformance liquid chromatography-MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control-based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.     

The influence of the cost of growth on ectotherm metabolism

A new model of ectothermic growth and metabolism is proposed. This model differs from most earlier models in representing explicitly the contribution of the "cost of growth" to ectotherm metabolism. It is shown that the cost of growth may constitute between 17 and 29% of the metabolism of an "average" ectotherm population. Furthermore, the metabolism of an "average" growing ectotherm may be between 40 and 79% greater than that of a non-growing ectotherm. As many environmental factors induce changes in metabolic rates of this magnitude, it is suggested that many factors which cause changes in metabolic rates do so indirectly by altering growth rates. In particular, it is suggested that body size, food availability and temperature often indirectly influence metabolic rates through their effects on growth rates, rather than by directly determining metabolic rates as has usually been assumed.     

代谢组学：一个迅速发展的新兴学科

对代谢组学的含义,中心任务,研究方法,样品要求,应用及其发展方向进行了简要综述. 系统生物学概念的诞生标志着研究哲学由“还原论”向“整体论”的变化. 系统生物学的中心任务就是要针对生物系统整体 (无论它是生物细胞,多细胞组织,器官还是生物整体),建立定量,普适,整体和可预测 (QUIP) 的认知. 具体而言,系统生物学研究就是要将给定生物系统的基因,转录,蛋白质和代谢水平所发生的事件,相关性及其对所涉及生物过程的意义进行整体性认识. 从而出现了许多的“组”和“组学”的新概念. 但是现已提出的一百多个“组”和“组学”,可以大体归纳为“基因组”/“基因组学”,“转录组”/“转录组学”,“蛋白质组”/“蛋白质组学”和“代谢组”/“代谢组学”四个方面. 显而易见,DNA,mRNA 以及蛋白质的存在为生物过程的发生提供了物质基础 (但这个过程有可能不发生!),而代谢物质所反映的是已经发生了的生物学事件. 因此代谢组学是对一个生物系统进行全面认识的不可缺少的一部分,是全局系统生物学 (global systems biology) 的重要基础,也是系统生物学的一个重要组成部分. 在现有的英文表述中,代谢组学同时存在两个不同的词汇和概念,即metabonomics 和 metabolomics. 尽管前者多用在动物系统而后者多用于植物和微生物系统,但这些概念的本质从他们的定义中能够得到较细致的了解. Metabonomics 的最初定义是就生物系统对生理和病理刺激以及基因改变的代谢应答的定量测定(“the quantitative measurement of the multi-parametric metabolic response of living systems to pathophysiological stimuli or geneticmodifications”). 我们认为这个定义现在可以更广泛地表述为:代谢组学是关于定量描述生物内源性代谢物质的整体及其对内因和外因变化应答规律的科学 (“Metabonomics is the branch of science concerned with the quantitative understandings of themetabolite complement of integrated living systems and its dynamic responses to the changes of both endogenous factors (such asphysiology and development) and exogenous factors (such as environmental factors and xenobiotics).”). 其中心任务包括 (1) 对内源性代谢物质的整体及其动态变化规律进行检测,量化和编录,(2) 确定此变化规律和生物过程的有机联系. Metabolomics 存在多个定义,但其精髓是:对一个生物系统的细胞在给定时间和给定条件下所有小分子代谢物质的定量分析(the quantitativemeasurement of all low molecular weight metabolites in an organism's cells at a specified time under specific environmentalconditions). 因此,metabolomics 可以译作“代谢物组学”. 不难看出,前者是对生物系统进行的整体和动态的认识 (不仅关心代谢物质的整体也关注其动态变化规律),而后者强调分析而且是个静态的认识概念. 因此可以认为,metabolomics 是metabonomics 的一个组成部分 (参看定义). 近年又有人提出了“dynamic metabolomics”的概念,这个概念所表达的含义十分接近“metabonomics”本身的含义. 所以,可以预见,随着这门新兴学科的发展和更深入讨论,这两个概念必将趋向一致. 因此我们建议,在中文表述中将“代谢组学”一词和英文中的 metabonomics 相对应,以避免不必要的混淆和争议. 就细胞系统而言,不仅存在细胞自身的代谢物质组成问题,存在细胞之间代谢物质交换的问题,也存在代谢过程所发生的位点问题. 因此,简单地分析代谢物质的总组成 (即代谢组) 缺乏“整体论”所要求的全面性,其意义有一定局限. 代谢组学属于全局系统生物学 (Global systems biology) 研究方法,便于对复杂体系的整体进行认识. 譬如,一个正常工作的人体包括“人体”本身和与之共同进化而来且共生的消化道微生物群体 (或称菌群),孤立地研究“人体”本身的基因,转录子以及蛋白质当然可以为人们认识人体生物学提供重要信息,但无法提供使人体正常工作不可缺少的菌群的信息. 人体血液和尿液的代谢组却携带着包括菌群在内的每一个细胞的信息,因此代谢组学方法对研究如人体这样复杂的进化杂合体十分有效. 正因如此,代谢组学已经广泛地应用到了包括药物研发,分子生理学,分子病理学,基因功能组学,营养学,环境科学等重要领域. 在代谢组学诞生的过去 6 年里,有关代谢组学的研究论文和专利以指数的形式逐年增长. 可以预见,这门新兴学科将应用到更为广泛的领域.     

Changes in bird populations as criteria of environmental changes

Järvinen, O. and Väisänen, R. A. 1979. Changes in bird populations as criteria of environmental changes. – Holarct. Ecol. 2: 75-80.
Birds are a powerful tool of environmental monitoring, on account of their ecological diversity. Because of this, bird populations seem best suited for monitoring biological, possibly non-linear effects of specified environmental changes, such as habitat modification, and for "general monitoring" aimed at detecting unexpected environmental changes as they occur. Because many population changes have multiple causes, monitoring specific environmental changes is most rewarding if birds are grouped by e.g. habitat, major strategy (e.g. resident vs. migrant species), or feeding guild. A blueprint for a Nordic monitoring system based on breeding land birds is presented. Local trends may be atypical, and representative coverage of the major habitats in large areas should thus be ensured. As annual population fluctuations usually give little information in environmental monitoring, long-term projects are necessary. The line transect method seems applicable to many monitoring purposes, as it is rapid, inexpensive, relatively accurate, and suitable for sampling the northern terrestrial biota of the Holarctic region, probably including the temperate deciduous forests.     

Metabolic engineering in the -omics era: elucidating and modulating regulatory networks.

The importance of regulatory control in metabolic processes is widely acknowledged, and several enquiries (both local and global) are being made in understanding regulation at various levels of the metabolic hierarchy. The wealth of biological information has enabled identifying the individual components (genes, proteins, and metabolites) of a biological system, and we are now in a position to understand the interactions between these components. Since phenotype is the net result of these interactions, it is immensely important to elucidate them not only for an integrated understanding of physiology, but also for practical applications of using biological systems as cell factories. We present some of the recent "-omics" approaches that have expanded our understanding of regulation at the gene, protein, and metabolite level, followed by analysis of the impact of this progress on the advancement of metabolic engineering. Although this review is by no means exhaustive, we attempt to convey our ideology that combining global information from various levels of metabolic hierarchy is absolutely essential in understanding and subsequently predicting the relationship between changes in gene expression and the resulting phenotype. The ultimate aim of this review is to provide metabolic engineers with an overview of recent advances in complementary aspects of regulation at the gene, protein, and metabolite level and those involved in fundamental research with potential hurdles in the path to implementing their discoveries in practical applications.     

Mixture models with multiple levels, with application to the analysis of multifactor gene expression data

Model-based clustering is a popular tool for summarizing high-dimensional data. With the number of high-throughput large-scale gene expression studies still on the rise, the need for effective data- summarizing tools has never been greater. By grouping genes according to a common experimental expression profile, we may gain new insight into the biological pathways that steer biological processes of interest. Clustering of gene profiles can also assist in assigning functions to genes that have not yet been functionally annotated. In this paper, we propose 2 model selection procedures for model-based clustering. Model selection in model-based clustering has to date focused on the identification of data dimensions that are relevant for clustering. However, in more complex data structures, with multiple experimental factors, such an approach does not provide easily interpreted clustering outcomes. We propose a mixture model with multiple levels, , that provides sparse representations both "within" and "between" cluster profiles. We explore various flexible "within-cluster" parameterizations and discuss how efficient parameterizations can greatly enhance the objective interpretability of the generated clusters. Moreover, we allow for a sparse "between-cluster" representation with a different number of clusters at different levels of an experimental factor of interest. This enhances interpretability of clusters generated in multiple-factor contexts. Interpretable cluster profiles can assist in detecting biologically relevant groups of genes that may be missed with less efficient parameterizations. We use our multilevel mixture model to mine a proliferating cell line expression data set for annotational context and regulatory motifs. We also investigate the performance of the multilevel clustering approach on several simulated data sets.     

Significance of anomalous thermoregulation in the pre-diabetic spiny mouse (Acomys cahirinus): oxygen consumption and temperature regulation

In keeping with studies of other desert rodents, the potentially diabetic spiny mouse has been demonstrated to have a very low basal metabolic rate, disproportionate to its body weight. The maintenance of a lower body temperature in response to high environmental temperatures and a lack of increase in metabolic rate in response to cooling have also been demonstrated. Assessments of "mechanical efficiency" have shown that spiny mice carry a potential selective advantage under fasting conditions. The findings, some of which are similar to those noted in other desert rodents, and in other species showing either spontaneous or induced hyperglycaemia, suggest that the low metabolic rate is at least partly based on ineffective glucose utilization. This phenomenon may be the common denominator of the survival advantage which has allowed both the successful evolution of species inhabiting warm, arid climes, and the persistence of the diabetic genotype in animal and human populations.     

Biological versus technical variability in 2-D DIGE experiments with environmental bacteria

Two-dimensional difference gel electrophoresis (2-D DIGE) allows for reliable quantification of global protein abundance changes. The threshold of significance for protein abundance changes depends on the experimental variation (biological and technical). This study estimates biological, technical and total variation inherent to 2-D DIGE analysis of environmental bacteria, using the model organisms "Aromatoleum aromaticum" EbN1 and Phaeobacter gallaeciensis DSM 17395. Of both bacteria the soluble proteomes were analyzed from replicate cultures. For strains EbN1 and DSM 17395, respectively, CV revealed a total variation of below 19 and 15%, an average technical variation of 12 and 7%, and an average biological variation of 18 and 17%. Multivariate analysis of variance confirmed domination of biological over technical variance to be significant in most cases. To visualize variances, the complex protein data have been plotted with a multidimensional scaling technique. Furthermore, comparison of different treatment groups (different substrate conditions) demonstrated that variability within groups is significantly smaller than differences caused by treatment.     

Modifications to husbandry and housing conditions of laboratory rodents for improved well-being

For a change to be considered enriching, the change must enhance animal welfare and improve biological functioning of the animals. A review of the literature shows that a consensus on the definition of changes constituting "environmental enrichment" has yet to be reached. For this reason, the results of studies on the effects of rodent enrichment are inconsistent. In many cases, changes have not been shown to be real improvements. However, enrichment is increasingly appreciated as a way to improve the well-being of rodents, providing them with opportunities for species-specific behaviors that might be available to them in the wild. Frequently defined as "change to the environment," enrichment can be as complex as devices (frequently termed "toys") or as simple as the provision of tissues from which mice readily construct nests. Nest making is a learned behavior in rats, and laboratory rats do show preferences for chewable objects in their environment. Rather than attempting a comprehensive review of the entire literature on environmental enrichment and its effects on rodent physiology and behavior, this paper focuses on husbandry and housing alterations that may improve the welfare of laboratory rodents. The effects of beneficial changes in housing and husbandry on rodent well-being and on experimental variability--and thus cost--are discussed. Areas that require more research are suggested. Also suggested are possible inexpensive and effective enrichment schemes for laboratory mice that might include reducing the cage floor space per mouse combined with providing nesting material.     

Variation in tissue-specific gene expression among natural populations

Background

Variation in gene expression is extensive among tissues, individuals, strains, populations and species. The interactions among these sources of variation are relevant for physiological studies such as disease or toxic stress; for example, it is common for pathologies such as cancer, heart failure and metabolic disease to be associated with changes in tissue-specific gene expression or changes in metabolic gene expression. But how conserved these differences are among outbred individuals and among populations has not been well documented. To address this we examined the expression of a selected suite of 192 metabolic genes in brain, heart and liver in three populations of the teleost fish Fundulus heteroclitus using a highly replicated experimental design.

Results

Half of the genes (48%) were differentially expressed among individuals within a population-tissue group and 76% were differentially expressed among tissues. Differences among tissues reflected well established tissue-specific metabolic requirements, suggesting that these measures of gene expression accurately reflect changes in proteins and their phenotypic effects. Remarkably, only a small subset (31%) of tissue-specific differences was consistent in all three populations.

Conclusions

These data indicate that many tissue-specific differences in gene expression are unique to one population and thus are unlikely to contribute to fundamental differences between tissue types. We suggest that those subsets of treatment-specific gene expression patterns that are conserved between taxa are most likely to be functionally related to the physiological state in question.