BACKGROUND

The science of metabolomics offers the possibility to measure full secondary plant metabolomes with limited experimental effort to allow identification of metabolome differences using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) of liquid chromatography mass spectrometry (LC-MS) data.

OBJECTIVE

To demonstrate a bioinformatics driven hypothesis generator for identification of biologically active compounds in plant crude extracts, which is validated by activity guided fractionation.

METHODS

Crude extracts of Rhododendron leaves were tested for their antibacterial activity using agar diffusion and minimum inhibitory concentration assays. Extracts were profiled by LC-MS. PCA and PLS-DA were used for differentiation of bioactive and inactive extracts and their metabolites. Preparative-high performance liquid chromatography (HPLC) and nuclear magnetic resonance (NMR) spectroscopy were used for separation and structure elucidation of pure compound(s) respectively.

RESULTS

An antibacterial Rhododendron collettianum was compared to a series of inactive extracts. Three metabolites were found to distinguish R. collettianum from other species indicating the ability of PCA and PLS-DA to suggest potential bioactive substances. An activity-guided fractionation of R. collettianum extracts was carried out and cannabiorcichromenic acid (CCA) was identified as antibacterial compound thereby validating the PCA and PLS-DA generated hypothesis. Four mammalian cell lines were used to estimate possible cytotoxicity of CCA.

CONCLUSIONS

It was shown that bioinformatics tools facilitate early stage identification of a biologically active compound(s) using LC-MS data, which reduce complexity and number of separation steps in bioactive screening. Copyright © 2017 John Wiley & Sons, Ltd.