Regina Oliveira, Department of Chemistry, Universidade Federal de Sao Carlos, São Carlos, Brazil (oliveirarv@ufscar.br)
The untargeted metabolomic approach constitutes one of the most frequently applied method in metabolomics studies. It aims to measure the comprehensive metabolomic profles of various biological samples to discover novel biological markers as well as to gain new insights into mechanisms underlying the pathophysiology of human diseases. However, due to the diversity of physicochemical properties and concentration range of metabolites present in biological samples, the use of complementary analytical techniques is required to provide proper metabolome coverage. Among currently available analytical platforms, liquid chromatography coupled with mass spectrometry (LC-MS) allows determination of the highest number of metabolites. Given the fact that the successful application of untargeted metabolomics relies on eficient determination of the widest possible range of metabolites in biological matrices, chromatographic separation and sample preparation constitutes crucial steps in the analytical workfow since it may affect metabolite content, data quality and interpretation of any obtained results. Therefore, the scope of the present work was to study different conditions for sample preparation and chromatographic separations of human plasma from healthy men submitted to inspiratory muscle training (IMT). Inspiratory muscle training (IMT) has been extensively studied in healthy individuals and patients with chronic respiratory diseases, but the efficacy of this intervention remains controversial. Although IMT can reduce dyspnea during both performance-based and maximal incremental exercise tests in healthy subjects, the physiological mechanisms for this improvement have not been adequately studied. Serum samples from healthy males were used for method optimization in order to obtain the best resolution and detection of multiple compounds. The effect of chromatographic factors including stationay phase, temperature, pH, mobile phase compositions and sample preparations were investigated. DryLab as a traditional HPLC simulation software was used to find the best condition for separation of a wide variety of compunds. Finally, the results of optimization approach and multivariate curve resolution coupled with experimental design were obtained and compared. The optimized HILIC and RPLC analytical procedures were complementary and when combined they greatly expanded the possibilities for metabolome coverage compared with RPLC alone. We, therefore, believe that using optimized analytical procedures including analytical separation and sample preparation will enable more comprehensive untargeted metabolic studies of human body fluids and thus will be of great value for monitoring of metabolic markers and for providing insights into human physiology including IMT.
Acknowledgments: FAPESP 16/22215-7
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