Integration Of Metabolic Data Research Articles

Untargeted Lipidomic Approach for Studying Different Nervous System Tissues of the Murine Model of Krabbe Disease.

Krabbe disease is a rare neurodegenerative disease with an autosomal recessive character caused by a mutation in the GALC gene. The mutation leads to an accumulation of psychosine and a subsequent degeneration of oligodendrocytes and Schwann cells. Psychosine is the main biomarker of the disease. The Twitcher mouse is the most commonly used animal model to study Krabbe disease. Although there are many references to this model in the literature, the lipidomic study of nervous system tissues in the Twitcher model has received little attention. This study focuses on the comparison of the lipid profiles of four nervous system tissues (brain, cerebellum, spinal cord, and sciatic nerve) in the Twitcher mouse compared to the wild-type mouse. Altogether, approximately 230 molecular species belonging to 19 lipid classes were annotated and quantified. A comparison at the levels of class, molecular species, and lipid building blocks showed significant differences between the two groups, particularly in the sciatic nerve. The in-depth study of the lipid phenotype made it possible to hypothesize the genes and enzymes involved in the changes. The integration of metabolic data with genetic data may be useful from a systems biology perspective to gain a better understanding of the molecular basis of the disease.

Myocardial Metastasis or Benign Brown Fat?

Noninvasive positron emission tomography (PET) imaging of cell glucose metabolism has become an essential tool for assessing tissue viability in cardiac (hibernating myocardium) and malignant disease (tumor regression or recurrence). However, PET studies alone provide information regarding tissue and organ metabolism but may not provide enough spatial resolution for accurate assessment of critical structural changes that are the cause or consequence of metabolic perturbations. The following case illustrates the pitfalls of relying on a single cardiac imaging modality and demonstrates how integration of metabolic data from PET with structural data from echocardiography, computed tomography (CT), or MRI studies can be used to correctly identify and manage a diagnostic dilemma. A 63-year-old woman with a medical history of chronic obstructive pulmonary disease and tobacco use was diagnosed with a left lung mass on a chest x-ray examination. A combined CT/fluorodeoxyglucose (FDG)-PET scan at another institution revealed the left lung mass (Figure 1) as well as a cardiac mass with increased uptake of radiotracer concerning for a cardiac metastasis (Figure 2). The CT Hounsfield unit (HU) density in the cardiac mass was −57 HU. She was referred for transesophageal echocardiography to further assess the cardiac mass (Figure 3). She had marked thickening of the interatrial septum with sparing of the fossa ovalis. A cardiac MRI (Figure …

Bringing metabolic networks to life: integration of kinetic, metabolic, and proteomic data

BackgroundTranslating a known metabolic network into a dynamic model requires reasonable guesses of all enzyme parameters. In Bayesian parameter estimation, model parameters are described by a posterior probability distribution, which scores the potential parameter sets, showing how well each of them agrees with the data and with the prior assumptions made.ResultsWe compute posterior distributions of kinetic parameters within a Bayesian framework, based on integration of kinetic, thermodynamic, metabolic, and proteomic data. The structure of the metabolic system (i.e., stoichiometries and enzyme regulation) needs to be known, and the reactions are modelled by convenience kinetics with thermodynamically independent parameters. The parameter posterior is computed in two separate steps: a first posterior summarises the available data on enzyme kinetic parameters; an improved second posterior is obtained by integrating metabolic fluxes, concentrations, and enzyme concentrations for one or more steady states. The data can be heterogenous, incomplete, and uncertain, and the posterior is approximated by a multivariate log-normal distribution. We apply the method to a model of the threonine synthesis pathway: the integration of metabolic data has little effect on the marginal posterior distributions of individual model parameters. Nevertheless, it leads to strong correlations between the parameters in the joint posterior distribution, which greatly improve the model predictions by the following Monte-Carlo simulations.ConclusionWe present a standardised method to translate metabolic networks into dynamic models. To determine the model parameters, evidence from various experimental data is combined and weighted using Bayesian parameter estimation. The resulting posterior parameter distribution describes a statistical ensemble of parameter sets; the parameter variances and correlations can account for missing knowledge, measurement uncertainties, or biological variability. The posterior distribution can be used to sample model instances and to obtain probabilistic statements about the model's dynamic behaviour.