Guan-yuan Chen1, Huai-hsuan Chiu1, Shu-wen Lin2, Yufeng Jane Tseng3, Sung-jeng Tsai1, Ching-hua Kuo4. 1. School of Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan; The Metabolomics Core Laboratory, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan. 2. School of Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Pharmacy, National Taiwan University Hospital, Taipei, Taiwan; Graduate Institute of Clinical Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan. 3. School of Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan; The Metabolomics Core Laboratory, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan. 4. School of Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan; The Metabolomics Core Laboratory, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan; Department of Pharmacy, National Taiwan University Hospital, Taipei, Taiwan. Electronic address: kuoch@ntu.edu.tw.
Abstract
BACKGROUND: As fatty acids play an important role in biological regulation, the profiling of fatty acid expression has been used to discover various disease markers and to understand disease mechanisms. This study developed an effective and accurate comparative fatty acid analysis method using differential labeling to speed up the metabolic profiling of fatty acids. METHODS: Fatty acids were derivatized with unlabeled (D0) or deuterated (D3) methanol, followed by GC-MS analysis. The comparative fatty acid analysis method was validated using a series of samples with different ratios of D0/D3-labeled fatty acid standards and with mouse liver extracts. RESULTS: Using a lipopolysaccharide (LPS)-treated mouse model, we found that the fatty acid profiles after LPS treatment were similar between the conventional single-sample analysis approach and the proposed comparative approach, with a Pearson's correlation coefficient of approximately 0.96. We applied the comparative method to investigate voriconazole-induced hepatotoxicity and revealed the toxicity mechanism as well as the potential of using fatty acids as toxicity markers. CONCLUSIONS: In conclusion, the comparative fatty acid profiling technique was determined to be fast and accurate and allowed the discovery of potential fatty acid biomarkers in a more economical and efficient manner.
BACKGROUND: As fatty acids play an important role in biological regulation, the profiling of fatty acid expression has been used to discover various disease markers and to understand disease mechanisms. This study developed an effective and accurate comparative fatty acid analysis method using differential labeling to speed up the metabolic profiling of fatty acids. METHODS:Fatty acids were derivatized with unlabeled (D0) or deuterated (D3)methanol, followed by GC-MS analysis. The comparative fatty acid analysis method was validated using a series of samples with different ratios of D0/D3-labeled fatty acid standards and with mouse liver extracts. RESULTS: Using a lipopolysaccharide (LPS)-treated mouse model, we found that the fatty acid profiles after LPS treatment were similar between the conventional single-sample analysis approach and the proposed comparative approach, with a Pearson's correlation coefficient of approximately 0.96. We applied the comparative method to investigate voriconazole-induced hepatotoxicity and revealed the toxicity mechanism as well as the potential of using fatty acids as toxicity markers. CONCLUSIONS: In conclusion, the comparative fatty acid profiling technique was determined to be fast and accurate and allowed the discovery of potential fatty acid biomarkers in a more economical and efficient manner.