Abstract
Dereplication based on hyphenated techniques has been extensively applied in plant metabolomics, thereby avoiding re-isolation of known natural products. However, due to the complex nature of biological samples and their large concentration range, dereplication requires the use of chemometric tools to comprehensively extract information from the acquired data. In this work we developed a reliable GC-MS-based method for the identification of non-targeted plant metabolites by combining the Ratio Analysis of Mass Spectrometry deconvolution tool (RAMSY) with Automated Mass Spectral Deconvolution and Identification System software (AMDIS). Plants species from Solanaceae, Chrysobalanaceae and Euphorbiaceae were selected as model systems due to their molecular diversity, ethnopharmacological potential, and economical value. The samples were analyzed by GC-MS after methoximation and silylation reactions. Dereplication was initiated with the use of a factorial design of experiments to determine the best AMDIS configuration for each sample, considering linear retention indices and mass spectral data. A heuristic factor (CDF, compound detection factor) was developed and applied to the AMDIS results in order to decrease the false-positive rates. Despite the enhancement in deconvolution and peak identification, the empirical AMDIS method was not able to fully deconvolute all GC-peaks, leading to low MF values and/or missing metabolites. RAMSY was applied as a complementary deconvolution method to AMDIS to peaks exhibiting substantial overlap, resulting in recovery of low-intensity co-eluted ions. The results from this combination of optimized AMDIS with RAMSY attested to the ability of this approach as an improved dereplication method for complex biological samples such as plant extracts.
Highlights
Dereplication plays a crucial role in natural products discovery and plant metabolomics studies
A FAME mixture consisting of a set of 22 fatty acid methyl esters of chain lengths from C8–C30 was purchased in the form of the Fiehn GC/mass spectrum (MS) Metabolomics Standards Kit
The use of computational methods can assist the identification of known metabolites by extracting the signals from co-eluted Gas Chromatography Mass Spectroscopy (GC-MS) components (Du and Zeisel, 2013)
Summary
Dereplication plays a crucial role in natural products discovery and plant metabolomics studies. Dereplication utilizes orthogonal physicochemical characteristics, e.g., UV−Vis profiles, chromatographic retention times, molecular weight, NMR chemical shifts, or biological properties, in order to confirm the metabolic identification (Smith et al, 2005; Blunt and Munro, 2007; Lang et al, 2008). This approach has proven to be very efficient for rapid identification of compounds in mixtures, it has some analytical limitations. Such limitations are mainly related to detection limits, lack of chromatographic resolution, or the need for additional confirmatory data such as MS/MS and NMR
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