METABOLRAT · Development of parallel analytical strategies for the generation and interpretation of metabolic data of diabetic rats

Final Activity Report Summary - METABOLRAT (Development of parallel analytical strategies for the generation and interpretation of metabolic data of diabetic rats)

Recent estimates and projections suggest an epidemic expansion in the diabetes group of diseases. For type I diabetes investigations, the classical strategy is via the streptozotocin (STZ) treated rat model. For such samples, markers of oxidative stress are increased which are generally associated with most of the complications which occur alongside (or as a consequence of) diabetes, such as cardiovascular disease, nephropathy or cataracts. Antioxidant interventions are acknowledged to be complementary to medical treatments, but scientific evidence is still necessary before this is an accepted fact based on proven well controlled investigations and peer reviewed evidence. It is important that any successful therapy for chronic diseases should normalise a targeted aspect of metabolism without disrupting the regulation of other metabolic pathways essential for maintaining health. Use of a limited number of single molecule surrogates for disease, or biomarkers, to monitor the efficacy of a therapy may fail to predict undesirable side effects. A comprehensive metabolomic assessment of metabolites is therefore necessary to determine the specific effects of the treatments. Metabolomics has excellent potential for developing clinical assessments of metabolic response to any treatment. Metabolomics has the ultimate goal of unbiased identification and quantification of all the metabolites present in a certain biological sample (which must be obtained from accurately defined and controlled experimental conditions). Both sample preparation and data acquisition must aim at including all classes of compounds, while at the same time assuring high recovery, and experimental robustness and reproducibility. The first requirement is to have available techniques that are as comprehensive as possible for metabolic analyses. Multivariate statistical methods can be employed in order to mine additional information from the data. We have explored the benefits of Capillary Electrophoresis (CE) for such analyses (which still remain to be exploited fully). Our experience has shown that the acute streptozoticin induced diabetic model (without insulin treatment) generates urine samples with such a high glucose content, and that peaks in the vicinity of glucose in the profile are difficult to interpret thus necessitating tedious sample pre-treatments before profiles can be compared. Using a simple modified electrolyte optimisation strategy this problem has been resolved and sample pre-treatment has been completely circumvented. This methodology has been optimised, validated and subsequently applied to urine samples coming from control and diabetic rats and the same groups treated with antioxidants (vitamin E and C). Data have been aligned normalised and baseline corrected. After that, Multidimensional Scaling was applied and results compared with individual target compounds previously determined for these animals. In summary, diabetic and control groups were clearly separated with diabetic animals showing a high dispersion, associated to a poor metabolic control. Data from diabetic animals treated with antioxidants clustered together and tended to be closer to controls than diabetics. These animals showed a higher metabolic control. Moreover, two diabetic animals with 'different' profiles to the rest of the animals (when measuring target parameters), were closer to controls after the statistical treatment. As a conclusion we have proved that urine fingerprinting by capillary electrophoresis is a cost effective tool for monitoring diabetes response to antioxidant treatment, and is highly valuable for further studies.

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