The changes in the energy metabolism of human muscle induced by training

Genomic signatures of a global fitness index in a multi-ethnic cohort of women

The rates of obesity and sedentary lifestyle are on a dramatic incline, with associated detrimental health effects among women in particular. Although exercise prescriptions are useful for overcoming these problems, success can be hampered by differential responsiveness among individuals in cardiovascular fitness indices (i.e. improvements in strength, lipids, VO(2) max). Genomic factors appear to play an important role in determining this inter-individual variation. We performed microarray analyses on mRNA in whole blood from 60 sedentary women from a multi-ethnic cohort who underwent 12 weeks of exercise, to identify gene subsets that were differentially expressed between individuals who experienced the greatest and least improvements in fitness. We identified 43 transcripts in 39 unique genes (FDR<10%; FC>1.5) whose expression increased the most in "high" versus "low" pre-menopausal female responders. These 39 genes were enriched in six biological pathways, including oxidative phosphorylation (p = 8.08 × 10(-3)). Several of the 39 genes (i.e. TIGD7, UQCRH, PSMA6, WDR12, TFB2M, USP15) have previously reported associations with fitness-related phenotypes. In summary, we identified gene signatures based on mRNA analysis that define responsiveness to exercise in a largely minority-based female cohort. Importantly, this study validates several genes/pathways previously associated with exercise responsiveness and extends these findings with additional novel genes.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

BACKGROUND

To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype-phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory.

RESULTS

To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data.

CONCLUSIONS

We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene--disease and gene--compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

Edelfosine-induced metabolic changes in cancer cells that precede the overproduction of reactive oxygen species and apoptosis

Background

Metabolic flux profiling based on the analysis of distribution of stable isotope tracer in metabolites is an important method widely used in cancer research to understand the regulation of cell metabolism and elaborate new therapeutic strategies. Recently, we developed software Isodyn, which extends the methodology of kinetic modeling to the analysis of isotopic isomer distribution for the evaluation of cellular metabolic flux profile under relevant conditions. This tool can be applied to reveal the metabolic effect of proapoptotic drug edelfosine in leukemia Jurkat cell line, uncovering the mechanisms of induction of apoptosis in cancer cells.

Results

The study of ¹³C distribution of Jukat cells exposed to low edelfosine concentration, which induces apoptosis in ≤5% of cells, revealed metabolic changes previous to the development of apoptotic program. Specifically, it was found that low dose of edelfosine stimulates the TCA cycle. These metabolic perturbations were coupled with an increase of nucleic acid synthesis de novo, which indicates acceleration of biosynthetic and reparative processes. The further increase of the TCA cycle fluxes, when higher doses of drug applied, eventually enhance reactive oxygen species (ROS) production and trigger apoptotic program.

Conclusion

The application of Isodyn to the analysis of mechanism of edelfosine-induced apoptosis revealed primary drug-induced metabolic changes, which are important for the subsequent initiation of apoptotic program. Initiation of such metabolic changes could be exploited in anticancer therapy.

Silencing of glycolysis in muscle: experimental observation and numerical analysis

The longstanding problem of rapid inactivation of the glycolytic pathway in skeletal muscle after contraction was investigated using (31)P NMR spectroscopy and computational modelling. Accumulation of phosphorylated glycolytic intermediates (hexose monophosphates) during cyclic contraction and subsequent turnover during metabolic recovery was measured in vivo in human quadriceps muscle using dynamic (31)P NMR spectroscopy. The concentration of hexose monophosphates in muscle peaked 40 s into metabolic recovery from maximal contractile work at 6.9 +/- 1.3 mm (mean +/- s.d.; n = 8) and subsequently declined at a rate of 0.009 +/- 0.001 mm s(1). It was next tested whether the current knowledge of the kinetic controls in the glycolytic pathway in muscle integrated in the Lambeth and Kushmerick computational model of skeletal muscle glycolysis explained the experimental data. It was found that the model underestimated the magnitude of deactivation of the glycolytic pathway in resting muscle, resulting in depletion of glycolytic intermediates and substrate for oxidative ATP synthesis. Numerical analysis of the model identified phosphofructokinase and pyruvate kinase as the kinetic control sites involved in deactivation of the glycolytic pathway. Ancillary 100-fold inhibition of both phosphofructokinase and pyruvate kinase was found necessary to predict glycolytic intermediate and ADP concentrations correctly in resting human muscle. Incorporation of this information into the model resulted in highly improved agreement between predicted and measured in vivo dynamics of hexose monophosphates in muscle following contraction. We concluded that silencing of the glycolytic pathway in muscle following contraction is most likely to be mediated by phosphofructokinase and pyruvate kinase inactivation on a time scale of seconds and minutes, respectively, and is necessary to prevent depletion of vital cellular substrates.