| Small-molecule profiling, termed metabolomics, is a valuable tool to study phenotype and changes in phenotype caused by environmental influences, disease, or changes in genotype. The metabolome can be defined as the complete complement of all small molecule (<1500 Da) metabolites found in a specific cell, organ or organism. The metabolome represents a vast number of components that belong to a wide variety of compound classes, and these compounds are very diverse in their physical and chemical properties and occur in a wide concentration range. Consequently, studying the metabolome is a major challenge to analytical chemistry.;Mass spectrometry (MS) is used in metabolomics to detect, quantify, and identify enzymatic substrates and byproducts from biological and clinical samples. MS-based metabolomics offers qualitative and quantitative analyses with high selectivity and sensitivity, wide dynamic range, and the ability to analyze biofluids with extreme molecular complexity. The combination of liquid chromatography with MS reduces the complexity of the mass spectra, decreases ion suppression, provides isobar resolution, and delivers information on the properties of the metabolites.;In this study, the hypothesis that detectable changes will occur in the blood plasma metabolic profile of healthy female and male adults before and after a ketogenic diet has been tested. In addition, changes in the plasma metabolome of piglets between days 2 through 8 of life have been evaluated.;Novel complementary chromatographic approaches--reversed phase and hydrophilic interaction liquid chromatography, directly coupled to a time-of-flight mass spectrometer operating under electrospray conditions in positive ion mode, have been developed and optimized. The performance/contribution of each separation strategy, identification of unique m/z features, and technical variability have been evaluated. The studies involved a large number of samples that required powerful data processing/analysis capabilities. In this sense, the raw data were processed using commercial instrument software. From the obtained chromatograms, features were extracted, aligned, normalized, filtered, and then analyzed by different statistical methods, including analysis of variance, principal component analysis, and volcano plots. Finally, using the accurate mass criterion of 2 ppm mass error, putative biomarkers responsible for the metabolic differences in the samples were identified using several databases. |
