Metabolite Profiling of Alangium salviifolium Bark Using Advanced LC/MS and GC/Q-TOFTechnology

Chandranayaka Siddaiah, Anil Kumar Bm, Saligrama Adavigowda Deepak, Syed Salman Lateef, Saurabh Nagpal, Kanchugarakoppal S Rangappa, Chakrabhavi D Mohan, Shobith Rangappa, Madan Kumar S, Minaxi Sharma, Vijai Kumar Gupta

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5 Citations (Scopus)
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There is an urge for traditional herbal remedies as an alternative to modern medicine in treating several ailments. Alangium salviifolium is one such plant, used traditionally to treat several diseases. In several reports, there are findings related to the use of this plant extract that demonstrate its therapeutic value. However, very few attempts have been made to identify the extensive metabolite composition of this plant. Here, we performed metabolite profiling and identification from the bark of A. salviifolium by extracting the sample in organic and aqueous solvents. The organic and aqueous extracts were fraction-collected using the Agilent 1260 Analytical Scale Fraction Collection System. Each of the fractions was analyzed on Liquid Chromatogaphy/Quadrupole Time-of-Flight LC/Q-TOF and Gas Chromatography/Quadrupole Time-of-Flight GC/instruments. The Liquid Chromatography/Mass Spectrometry (LC/MS) analyses were performed using Hydrophilic Ineraction Liquid Chromatography (HILIC), as well as reversed-phase chromatography using three separate, orthogonal reverse phase columns. Samples were analyzed using an Agilent Jet Stream (AJS) source in both positive and negative ionization modes. The compounds found were flavonoids, fatty acids, sugars, and terpenes. Eighty-one secondary metabolites were identified as having therapeutic potential. The data produced was against the METLIN database using accurate mass and/or MS/MS library matching. Compounds from Alangium that could not be identified by database or library matching were subsequently searched against the ChemSpider) database of over 30 million structures using MSMS data and Agilent MSC software.In order to identify compounds generated by GC/MS, the data were searched against the AgilentFiehn GCMS Metabolomics Library as well as the Wiley/NIST libraries.

Original languageEnglish
Article number1
Number of pages11
Issue number1
Early online date22 Dec 2020
Publication statusFirst published - 22 Dec 2020


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