| Abstract:Metabolomics is a newly emerging "omics" after genomics, proteomics and transcriptomics. It has been widely applied on disease diagnosis, drug development, drug effect and toxicity evaluation, plant metabolomics and other fields. This paper can be divided into four parts as follows:Serum is an ideal bio-fluid for biomarker discovery. However, little has been studied to characterize the effects of sampling, handling and storage procedures on serum metabolic profiling. Firstly, in current study, GC-MS-based metabolomics method was used to systematically evaluate the effect of every single factor on global metabolic profiling. The sampling procedures in study were included the selection on blood collection tubes, anticoagulants, variation in clotting time or time lag before centrifugation, repeated freeze/thaw cycles and so on. The results showed that blood-based metabolic profiling was differently effected by sampling, handling and storage with most change from variation associated with sampling procedures.Secondly, blood matrix of serum or plasma nowadays has been arbitrarily applied to biomarker discovery in metabolomics and biological study without knowing the effects caused by the either bio-fluid. Considering this, systematically comparison of the metabolic profiles between rat plasma and serum using two complementary platforms of gas chromatography-mass spectrometry (GC-MS) and liquid chromatography quadruple time-of-flight mass spectrometry (LC-QTOF-MS) was conducted. As a result, in GC-MS analysis:(1)25.8%of the defined metabolites differed serum from plasma profiles (t-test, p<0.05);(2) serum possessed higher sensitivity than plasma for its generally higher peak area in metabolic profiles, making the serum more suitable to biomarker discovery study. In LC-QTOF-MS analysis:13(in positive ion mode) and7(in negative ion mode) important metabolites were identified for mainly contributing to the separation between serum and plasma metabolomics profiles. Based on these basis researches, the domestic standard operation procedures (SOP) for the reliability of biomarker discovery, continuance and expandability in the laboratory has been built.Thirdly, metabolic syndrome (MetS) is a constellation of the most dangerous heart attack risk factors:diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure. Analysis and representation the variances of metabolic profiles is urgently needed for early diagnosis and treatment of MetS. Herein, based on the built domestic SOP, a metabolomics approach for analyzing MetS based on GC-MS profiling and random forest models was proposed. Serum samples from healthy and MetS patients were initially characterized by GC-MS. Then, random forest model was used to visually discriminate the changes in MetS serum based on these GC-MS profiles. Afterwards, some informative metabolites have been successfully discovered by means of variable importance ranking in random forest models. The metabolites such as2-hydroxybutyric acid, inositol and d-glucose, were defined as potential biomarkers to diagnose the MetS. These results obtained by proposed method showed that combining GC-MS profiling with random forest method was a useful approach to analyze metabolites variances and further screen the potential biomarkers for MetS diagnosis. |
PDF Full Text Request