ABSTRACT
A metabolic profiling analysis method for metabolomic studies of rice leaf was established based on HSS T3 combined with XBridge Amide Q-TOF LC/MS by comparing the influences of different extraction methods in rice leaves of metabolites. The extraction and separation of rice leaf metabolites using three different methods including methanol-chloroform-water,methanol-chloroform-ammonia,methanol-methyl tert-butyl ether -water and different chromatographic systems were compared by the numbers of peaks, identified metabolites and the metabolic pathways. The results showed that the method of methanol-chloroform-water reached the highest coverage rate of metabolites in rice leaves,and the maximum number of unique metabolites including prephenic acid, luteolin, α-linolenic acid, aconitic acid, gibberellin A12 aldehyde, isovitexin, L-Glutamate were detected. Metabolites with different polarity in rice leaf could be detected by HSS T3 and XBridge Amide. A total of 16 kinds of organic acids, 17 kinds of nucleotides, 21 kinds of amino acids, 66 kinds of fatty acids,11 kinds of phospholipids and 7 kinds of sphingolipids were identified. XBridge Amide had an absolute advantage in detecting phospholipids and sphingolipids. The metabolic pathways involved purine metabolism, pyrimidine metabolism, tricarboxylic acid cycle, arginine metabolism, fatty acid metabolism, phospholipid metabolism, sphingolipid metabolism, phenylalanine metabolism and vitamin B2 synthesis. It showed certain complementarity between the two columns in identifying metabolites and involved the metabolic pathways. The established method is expected to be useful for the metabolomic studies of rice.
ABSTRACT
Objective · To explore the change of metabolomic profiling after erlotinib (anepithelial growth factor receptor tyrosine kinase inhibitor)resistance of lung adenocarcinoma cells (PC9-ER), and find the differential metabolome associated witherlotinib resistance. Methods · Metabolic profiling of PC9-ER cells and homologous parent PC9 cells was acquired by the ultraperformance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS). The data were analyzed by multi-dimensional statistical methods, such as partial least squares projection to latent structures-discriminant analysis (PLS-DA), to select and identify differential metabolites associated with erlotinib resistance. Results · A total of 14 differential metabolites were identified in PC9-ER cells. Seven up-regulated metabolites included N-acetylspermidine, phosphatidylethanolamine, AMP, pantothenic acid,proline, glutamate, and histidine, while seven down-regulated metabolites included citrulline, phosphorylcholine, glutathione, cysteinylglycine, glutathione oxidized, NAD, and S-adenosylmethionine, mainly participating in glutathione metabolism, glutamate metabolism, ammonia recycling, and protein biosynthesis. Conclusion · Metabolic profiling of erlotinib-resistant lung adenocarcinoma cells was changed. The information of differential metabolites associated with erlotinib resistance could provide clues for new resistance mechanisms and potential metabolism-related drug targets.