Optimization of a GC/MS-based Screening of Plant Extracts using GNPS

An uncountable variety of plants and organisms inhabits an even bigger number of natural products [1]. These valuable compounds are still mostly unexplored, as research mainly focusses on thousands of molecules already identified and named.

Using GC/MS-analysis and GNPS [2], [3] different extracts of five plants (sea buckthorn, ginger, narrow-leafed purple coneflower, dogrose and pomegranate) were investigated in an untargeted approach.

The extraction of the plant material was carried out successively with supercritical carbon dioxide (kindly provided by Flavex Naturextrakte GmbH) and methanol. The methanolic extract was further purified via liquid-liquid extraction and the ethyl acetate phase as well as the carbon dioxide extract was further investigated using GC/MS. Therefore, the derivatization step and the GC/MS method were optimized. Subsequently, the data were prepared for application using Global Natural Products Social Molecular Networking (GNPS) and different parameters during network creation and annotation were evaluated.

The molecular networks visualized using Cytoscape show differences between different extracts as well as between different plants. Clusters were found which occurred in all samples as well as plant specific clusters ([Fig. 1]).

Fig. 1 Two clusters from the molecular network of plant extracts derivatized with TMSH, the left showing compound equally distributed over all samples and the right showing compounds specifically found in ginger.

Publikationsverlauf

Artikel online veröffentlicht:
12. Dezember 2022

• References

• 1 Veeresham C. Natural products derived from plants as a source of drugs. J Adv Pharm Technol Res 2012; 3: 200-201
• 2 Aksenov AA, Laponogov I, Zhang Z. et al. Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data. Nat Biotechnol 2021; 39: 169-173
• 3 Wang M, Carver JJ, Phelan VV. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol 2016; 34: 828-837