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My research interests focus on MS-based metabolomics and its applications in early disease diagnosis, drug metabolism, and biological sciences. I am working closely with a number of biological researchers and clinical practitioners in various studies for identifying metabolic markers, which are useful for detecting cancers, investigating cancer mechanisms using cell/mitochondria models, examining metabolic remodeling during pressure-overload hypertrophy, comprehensively profiling age-dependent changes of cardiac metabolites, etc.
The Gu research group focuses on mass spectrometry (MS)-based metabolomics and its applications for biomarker discovery and systems biology research. We are skilled in the development, optimization, and applications of MS methods for both qualitative and quantitative measurements. We utilize a range of platforms, including LC- and GC-MS for global aqueous profiling and lipidomics. We have developed a number of targeted LC-MS/MS assays to detect panels of metabolites, including a 200+ aqueous metabolite assay that interrogates 35 metabolic pathways, as well as assays to measure bile acids, acyl carnitines, co-enzymes, and cardiolipins. Recent methods developed by our group include quantitative methods to measure metabolite concentrations, metabolic flux analysis approaches, and ratio analysis methods for unknown identification. In addition, we have extensive experience with advanced multivariate statistical analysis methods, including PCA, PLS-DA, SVM, etc. We can proficiently program in Matlab, R, and Pathyn to analyze MS and NMR data, both individually and in combination.
In addition to our methods development activities, we are working closely with a number of biological researchers and clinical practitioners in various studies. We have been active in the identification of biomarkers for early cancer detection, recurrence monitoring, and therapy prediction. We are also involved in studies of heart diseases, obesity, and diabetes, as well as nutrition based disease prevention and aging. Our group has a strong interest in expanding the understanding of disease risk factors using metabolite profiling approaches. My group has experience running large metabolomics sample sets that incorporate a number of quality control measures to ensure reliability of large datasets. Our work in metabolomics is thus applied to a broad range of applications in both basic and clinical metabolic research.