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PubMed2006

Catabolic pathways and biotechnological applications of microbial caffeine degradation.

Catabolism of caffeine (1,3,7-trimethylxanthine) in microorganisms commences via two possible mechanisms: demethylation and oxidation. Through the demethylation route, the major metabolite formed in fungi is theophylline (1,3-dimethylxanthine), whereas theobromine (3,7-dimethylxanthine) is the major metabolite in bacteria. In certain bacterial species, caffeine has also been oxidized directly to trimethyl uric acid in a single step. The conversion of caffeine to its metabolites is primarily brought about by N-demethylases (such as caffeine demethylase, theobromine demethylase and heteroxanthinedemethylase), caffeine oxidase and xanthine oxidase that are produced by several caffeine-degrading bacterial species such as Pseudomonas putida and species within the genera Alcaligenes, Rhodococcus and Klebsiella. Development of biodecaffeination techniques using these enzymes or using whole cells offers an attractive alternative to the present existing chemical and physical methods removal of caffeine, which are costly, toxic and non-specific to caffeine. This review mainly focuses on the biochemistry of microbial caffeine degradation, presenting recent advances and the potential biotechnological application of caffeine-degrading enzymes.

Bacterial Proteins↗
PubMed2006

Beyond blood sugar: the potential of NMR-based metabonomics for type 2 human diabetes, and the horse as a possible model.

Metabonomic analysis is a powerful tool for identifying and characterizing metabolic disorders, for example type 2 diabetes and the metabolic syndrome. Nuclear magnetic resonance (NMR) spectroscopy is an essential tool for such analysis, with special benefits. The review assesses the current status and potential of NMR-based metabonomics of type 2 diabetes. The horse is proposed as a possible model for studying this condition and disease. Some examples are shown of horse blood analyses by NMR.

Animals↗
PubMedDate not supplied

Principles of therapeutic liver repopulation.

Liver repopulation by transplanted hepatocytes is a promising approach for many inborn errors of metabolism. In this review, examples of liver repopulation in animals and the implications of these models for clinical cell transplantation will be discussed.

Cell Differentiation↗
PubMed2006

The linear allometric relationship between total metabolic energy per life span and body mass of mammals.

The aim of this study is to establish and calculate the exact allometric relationship between the total metabolic energy per life span and the body mass in a wide range of mammals with about six orders of magnitude variation of the body mass of animals. The study shows that it exists a linear relationship between the total metabolic energy per life span PT(ls) (kJ) and the body mass M (kg) of 95 mammals (3 monotremes, Subclass Prototheria, 16 marsupialis (Subclass Theria, Infraclass Metatheria) and 76 placentals (Subclass Theria, Infraclass Eutheria)) from type: PT(ls)=A(ls)(+)M(1.0511), where P (kJ/day) is the basal rate of metabolism and T(ls) (days) is the mean life span of animals. The linear coefficient A(ls)(+)=7.158x10(5) kJ/kg is the total metabolic energy, exhausted during the life span per 1 kg body mass of the animals. The mean values of the total metabolic energy per life span, per unit body mass (A(ls)) for orders from Subclass Prototheria and Theria (Infraclass Metatheria) and orders Xenarthra, Pholidota, Soricomorpha, Rodentia (Infraclass Eutheria) varied negligible in interval (4.656-5.80)x10(5) kJ/kg. The coefficient A(ls) grows from (7.68-8.36)x10(5) kJ/kg in Lagomorpha and Artiodactyla (Eutheria) to (10.58-12.64)x10(5) kJ/kg in orders Carnivora, Pinnipeda and Chiroptera (Eutheria). A(ls) grows maximum to 18.5x10(5) kJ/kg in Primates. Thus, the values of coefficient A(ls) differ maximum four-fold in all orders. Across the all species the values of A(ls) are changes about one order of magnitude. Consequently, our survey shows that the changes of the body mass, basal metabolic rate and the life span of animals are three mutually related parameters, so that the product A(ls)=(PT(ls))/M remains relatively constant in comparison to 1 million fold difference in body mass and total metabolic energy per life span between mammals.

Animals↗
PubMed2004

Mathematical and computational techniques to deduce complex biochemical reaction mechanisms.

Time series data can now be routinely collected for biochemical reaction pathways, and recently, several methods have been proposed to infer reaction mechanisms for metabolic pathways and networks. In this paper we provide a survey of mathematical techniques for determining reaction mechanisms for time series data on the concentration or abundance of different reacting components, with little prior information about the pathways involved.

Algorithms↗
PubMed2006

Investigating heavy metal resistance, bioaccumulation and metabolic profile of a metallophile microbial consortium native to an abandoned mine.

Contaminated sites represent new ecological niches where historical pollution has originated an unusual microbial biodiversity. The knowledge of these microorganisms contributes to the discovery of new pathways and metabolic networks and may offer potential solutions for damaged areas. In the present work seven microbial consortia have been isolated from an abandoned mine of blend and galena (Ingurtosu, Italy) through a selection for resistance to zinc (tested up to 40 mM in solution). All the consortia were able to accumulate zinc and the best accumulator, named Ing5, has been studied for the following characteristics: resistance and accumulation of Zn, Cd, Hg, bioaccumulation mechanisms of Zn, and influence of Zn and Cd on the metabolic profile. The results indicate that the consortium Ing5 bears resistance systems for Cd and Hg as well as Zn and that, for some of the 5 isolates belonging to Ing5, the resistance thresholds are higher in consortium than in pure culture. The prevalent mechanism for zinc accumulation can be reasonably considered to be metabolism-dependent, inducible and regulated by metal concentrations. The study on the metabolic profile, carried out by the Biolog system, shows that Zn exerts a very low influence on the metabolic profile and that this influence can also be positive; Cd has a stronger negative influence but that, despite this, the consortium is able to maintain a wide metabolic potential in the presence of heavy metals. These features of Ing5 make it a good candidate for biotechnological applications and for further investigation of the degradation of organic pollutants in the presence of metals.

Biodegradation, Environmental↗
PubMed2003

Role of duplicate genes in genetic robustness against null mutations.

Deleting a gene in an organism often has little phenotypic effect, owing to two mechanisms of compensation. The first is the existence of duplicate genes: that is, the loss of function in one copy can be compensated by the other copy or copies. The second mechanism of compensation stems from alternative metabolic pathways, regulatory networks, and so on. The relative importance of the two mechanisms has not been investigated except for a limited study, which suggested that the role of duplicate genes in compensation is negligible. The availability of fitness data for a nearly complete set of single-gene-deletion mutants of the Saccharomyces cerevisiae genome has enabled us to carry out a genome-wide evaluation of the role of duplicate genes in genetic robustness against null mutations. Here we show that there is a significantly higher probability of functional compensation for a duplicate gene than for a singleton, a high correlation between the frequency of compensation and the sequence similarity of two duplicates, and a higher probability of a severe fitness effect when the duplicate copy that is more highly expressed is deleted. We estimate that in S. cerevisiae at least a quarter of those gene deletions that have no phenotype are compensated by duplicate genes.

Evolution, Molecular↗
PubMed2003

Endogenous cannabinoid system as a modulator of food intake.

The ability of Cannabis sativa (marijuana) to increase hunger has been noticed for centuries, although intensive research on its molecular mode of action started only after the characterization of its main psychoactive component Delta(9)-tetrahydrocannabinol in the late 1960s. Despite the public concern related to the abuse of marijuana and its derivatives, scientific studies have pointed to the therapeutic potentials of cannabinoid compounds and have highlighted their ability to stimulate appetite, especially for sweet and palatable food. Later, the discovery of specific receptors and their endogenous ligands (endocannabinoids) suggested the existence of an endogenous cannabinoid system, providing a physiological basis for biological effects induced by marijuana and other cannabinoids. Epidemiological reports describing the appetite-stimulating properties of cannabinoids and the recent insights into the molecular mechanisms underlying cannabinoid action have proposed a central role of the cannabinoid system in obesity. The aim of this review is to provide an extensive overview on the role of this neuromodulatory system in feeding behavior by summarizing the most relevant data obtained from human and animal studies and by elucidating the interactions of the cannabinoid system with the most important neuronal networks and metabolic pathways involved in the control of food intake. Finally, a critical evaluation of future potential therapeutical applications of cannabinoid antagonists in the therapy of obesity and eating disorders will be discussed.

Animals↗
PubMed1987

Selection by differential molecular survival: a possible mechanism of early chemical evolution.

A model is proposed to account for selective chemical evolution, progressing from a relatively simple initial set of abiotic synthetic phenomena up to the elaborately sophisticated processes that are almost certainly required to produce the complex molecules, such as replicatable RNA-like oligonucleotides, needed for a Darwinian form of selection to start operating. The model makes the following assumptions: (i) that a small number of micromolecular substances were present at high concentration; (ii) that a random assembly mechanism combined these molecules into a variety of multimeric compounds comprising a wide repertoire of rudimentary catalytic activities; and (iii) that a lytic system capable of breaking down the assembled products existed. The model assumes further that catalysts supplied with substrates were significantly protected against breakdown. It is shown that, by granting these assumptions, an increasingly complex network of metabolic pathways would progressively be established. At the same time, the catalysts concerned would accumulate selectively to become choice substrates for elongation and other modifications that could enhance their efficiency, as well as their survival. Chemical evolution would thus proceed by a dual process of metabolic extension and catalytic innovation. Such a process should be largely deterministic and predictable from initial conditions.

Biochemical Phenomena↗
PubMed2004

Biochemical regulation of mammalian AMP-activated protein kinase activity by NAD and NADH.

AMP-activated protein kinase (AMPK) serves as an energy-sensing protein kinase that is activated by a variety of metabolic stresses that lower cellular energy levels. When activated, AMPK modulates a network of metabolic pathways that result in net increased substrate oxidation, generation of reduced nucleotide cofactors, and production of ATP. AMPK is activated by a high AMP:ATP ratio and phosphorylation on threonine 172 by an upstream kinase. Recent studies suggest that mechanisms that do not involve changes in adenine nucleotide levels can activate AMPK. Another sensor of the metabolic state of the cell is the NAD/NADH redox potential. To test whether the redox state might have an effect on AMPK activity, we examined the effect of beta-NAD and NADH on this enzyme. The recombinant T172D-AMPK, which was mutated to mimic the phosphorylated state, was activated by beta-NAD in a dose-dependent manner, whereas NADH inhibited its activity. We explored the effect of NADH on AMPK by systematically varying the concentrations of ATP, NADH, peptide substrate, and AMP. Based on our findings and established activation of AMPK by AMP, we proposed a model for the regulation by NADH. Key features of this model are as follows. (a) NADH has an apparent competitive behavior with respect to ATP and uncompetitive behavior with respect to AMP resulting in improved binding constant in the presence of AMP, and (b) the binding of the peptide is not significantly altered by NADH. In the absence of AMP, the binding constant of NADH becomes higher than physiologically relevant. We conclude that AMPK senses both components of cellular energy status, redox potential, and phosphorylation potential.

AMP-Activated Protein Kinases↗
PubMed2005

Biological master games: using biologists' reasoning to guide algorithm development for integrated functional genomics.

We review some powerful new algorithms that build on the intuitive biological interpretation techniques for statistical analysis of functional genomics experiments. Although they were originally designed for transcriptomics, we argue that these algorithms are applicable to any type of -omics study (transcriptomics, proteomics, metabolomics). Rank Products (RP), a strictly non-parametric test statistic to detect differentially regulated elements (genes, proteins, metabolites) in genome-wide screens. RP is particularly powerful for noisy data and low numbers of replicates and makes full use of the availability of a large number of parallel measurements that is typical of modern large-scale experiments. Iterative Group Analysis (iGA), a statistical method that makes the transition from regulated single elements to significant classes of elements, and thus provides an automatic functional annotation of an experiment. Graph-based iGA (GiGA), an extension of iGA that combines experimental data with a broad variety of biological annotations to highlight physiologically relevant regions in a given "evidence graph" (e.g., metabolic networks, signaling pathway diagrams, protein interaction maps). The sequential application of these techniques yields an increasingly abstract interpretation of experimental data that is at the same time quantitative, statistically rigorous, and biologically significant. The results can be used either as helpful tools to guide data visualization and exploration, or as the input for downstream computational applications in a systems biology framework.

Algorithms↗
PubMed2005

A new dynamical layout algorithm for complex biochemical reaction networks.

BACKGROUND: To study complex biochemical reaction networks in living cells researchers more and more rely on databases and computational methods. In order to facilitate computational approaches, visualisation techniques are highly important. Biochemical reaction networks, e.g. metabolic pathways are often depicted as graphs and these graphs should be drawn dynamically to provide flexibility in the context of different data. Conventional layout algorithms are not sufficient for every kind of pathway in biochemical research. This is mainly due to certain conventions to which biochemists/biologists are used to and which are not in accordance to conventional layout algorithms. A number of approaches has been developed to improve this situation. Some of these are used in the context of biochemical databases and make more or less use of the information in these databases to aid the layout process. However, visualisation becomes also more and more important in modelling and simulation tools which mostly do not offer additional connections to databases. Therefore, layout algorithms used in these tools have to work independently of any databases. In addition, all of the existing algorithms face some limitations with respect to the number of edge crossings when it comes to larger biochemical systems due to the interconnectivity of these. Last but not least, in some cases, biochemical conventions are not met properly. RESULTS: Out of these reasons we have developed a new algorithm which tackles these problems by reducing the number of edge crossings in complex systems, taking further biological conventions into account to identify and visualise cycles. Furthermore the algorithm is independent from database information in order to be easily adopted in any application. It can also be tested as part of the SimWiz package (free to download for academic users at 1). CONCLUSION: The new algorithm reduces the complexity of pathways, as well as edge crossings and edge length in the resulting graphical representation. It also considers existing and further biological conventions to create a drawing most biochemists are familiar with. A lot of examples can be found on 2.

Algorithms↗
PubMed2004

The promise of metabolomics in cancer molecular therapeutics.

A systematic elucidation of cancer cell dysfunction and therapeutic mechanisms seems within reach with modern functional genomics and proteomics tools. However, for this potential to be realized, the metabolic consequences of gene expression and protein activity must be understood. 'Metabolomics' is currently a major missing component. It differs from classical metabolic studies by its greater breadth, depth and speed, enabled by the huge advances in analytical instrumentation (especially nuclear magnetic resonance and mass spectrometry) over the past two decades. Multiple metabolic pathways and networks can now be traced by the flow of atoms through metabolites, known as isotopomer analyses. Thus, metabolomics demands both high-throughput and high-information content analyses, and interpretation: the latter is currently a bottleneck. There are currently very few metabolomic studies in cancer therapeutics, despite this great need and potential.

Animals↗
PubMed2002

Maneuvering in the complex path from genotype to phenotype.

Human disease phenotypes are controlled not only by genes but by lawful self-organizing networks that display system-wide dynamics. These networks range from metabolic pathways to signaling pathways that regulate hormone action. When perturbed, networks alter their output of matter and energy which, depending on the environmental context, can produce either a pathological or a normal phenotype. Study of the dynamics of these networks by approaches such as metabolic control analysis may provide new insights into the pathogenesis and treatment of complex diseases.

Alzheimer Disease↗
PubMed2000

Assessment of the metabolic capabilities of Haemophilus influenzae Rd through a genome-scale pathway analysis.

The annotated full DNA sequence is becoming available for a growing number of organisms. This information along with additional biochemical and strain-specific data can be used to define metabolic genotypes and reconstruct cellular metabolic networks. The first free-living organism for which the entire genomic sequence was established was Haemophilus influenzae. Its metabolic network is reconstructed herein and contains 461 reactions operating on 367 intracellular and 84 extracellular metabolites. With the metabolic reaction network established, it becomes necessary to determine its underlying pathway structure as defined by the set of extreme pathways. The H. influenzae metabolic network was subdivided into six subsystems and the extreme pathways determined for each subsystem based on stoichiometric, thermodynamic, and systems-specific constraints. Positive linear combinations of these pathways can be taken to determine the extreme pathways for the complete system. Since these pathways span the capabilities of the full system, they could be used to address a number of important physiological questions. First, they were used to reconcile and curate the sequence annotation by identifying reactions whose function was not supported in any of the extreme pathways. Second, they were used to predict gene products that should be co-regulated and perhaps co-expressed. Third, they were used to determine the composition of the minimal substrate requirements needed to support the production of 51 required metabolic products such as amino acids, nucleotides, phospholipids, etc. Fourth, sets of critical gene deletions from core metabolism were determined in the presence of the minimal substrate conditions and in more complete conditions reflecting the environmental niche of H. influenzae in the human host. In the former case, 11 genes were determined to be critical while six remained critical under the latter conditions. This study represents an important milestone in theoretical biology, namely the establishment of the first extreme pathway structure of a whole genome.

Genome, Bacterial↗