Biological Assay-Guided Fractionation and Mass Spectrometry-Based Metabolite Profiling of Annona muricata L. Cytotoxic Compounds against Lung Cancer A549 Cell Line.
Edcyl Lee O SalacMichael Russelle S AlvarezRnie Shayne GauranaSheryl Joyce B GrijaldoLuster Mae SerranoFlorence de JuanRowell AbogadoIsagani PadolinaFroila Marie DeniegaKimberly DelicaKimberly FernandezCarlito B LebrillaMarlon N ManaloFrancisco M Heralde IiiGladys Cherisse J CompletoRuel C NacarioPublished in: Plants (Basel, Switzerland) (2022)
Annona muricata L. (Guyabano) leaves are reported to exhibit anticancer activity against cancer cells. In this study, the ethyl acetate extract from guyabano leaves was purified through column chromatography, and the cytotoxic effects of the semi-purified fractions were evaluated against A549 lung cancer cells using in vitro MTS cytotoxicity and scratch/wound healing assays. Fractions F15-16C and F15-16D exhibited the highest anticancer activity in the MTS assay, with % cytotoxicity values of 99.6% and 99.4%, respectively. The bioactivity of the fractions was also consistent with the results of the scratch/wound healing assay. Moreover, untargeted metabolomics was employed on the semi-purified fractions to determine the putative compounds responsible for the bioactivity. The active fractions were processed using LC-MS/MS analysis with the integration of the following metabolomic tools: MS-DIAL (for data processing), MetaboAnalyst (for data analysis), GNPS (for metabolite annotation), and Cytoscape (for network visualization). Results revealed that the putative compounds with a significant difference between active and inactive fractions in PCA and OPLS-DA models were pheophorbide A and diphenylcyclopropenone.
Keyphrases
- mass spectrometry
- liquid chromatography
- data analysis
- wound healing
- high throughput
- high performance liquid chromatography
- multiple sclerosis
- gas chromatography
- high resolution
- capillary electrophoresis
- high resolution mass spectrometry
- tandem mass spectrometry
- ms ms
- electronic health record
- deep learning
- big data
- simultaneous determination
- high speed