| Metabonomics is a well-established technique in the post-genomic era. It is based on the analysis of group indicators, by means of high-throughput detection and data processing, which focuses on the information modeling and system integration. It is an important part of the systems biology. This technique basically measures the total metabolome (all low-molecular-weight metabolites) qualitatively and quantitatively in a biological system to analyse the relationship between metabolome and physiological or pathological situations. NMR and MS are the most useful analytical techniques in metabonomics.While in data analysis, pattern recognition, such as PCA (Principal Components Analysis), HCA (Hirarchical Clustering Analyis), PLS (Partial Least Square) and ANN (Artifical Neural Net) are applied to the data reduction.In this work, gadolium was used as a model toxicant in rats. Male Wistar rats were treated with various doses of GdCl3 and the extracts of liver and kidney from the Gdcl3-treatd rats were analysed by NMR based metabonomics. Some significant metabolic biomarkers were found, which were related with the toxicity of gadolinium.Three typical pattern recognition methods (PCA, hierarchical cluster analysis and fuzzy c-means) were performed to detect the metabolic biomarkers indicating the toxicity of GdCl3 in rat liver and kidney. The biomarkers of liver toxicity included increased succinate, lactate, alanine, betaine, as well as the decreased glycogen. And increased creatine, lactate and the decreased glutamate, alanine, phosphocholine, glycophosphocholine (GPC), betaine, myo-inositol, trimethylamine N-oxide(TMAO) were discover as biomarkers of renal injuries .Our work showed the high reliability and potential of metabonomics in the toxicology.
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