Annotation of Plant and Fungal Metabolites in MS-Based Metabolomics

Dear Colleagues,

In mass spectrometry (MS)-based metabolomics, annotation (= putative identification) of components responsible for discriminating sample groups has been considered the bottleneck in the workflow. This bottleneck is usually discussed within the context of body fluid metabolomics, where the number of known metabolites numbers in the tens of thousands (ca. 23,000 detected metabolites listed in the Human Metabolome Database). This problem is exacerbated in plants and fungi, for which the number of known metabolites is an order of magnitude higher (230,000 natural products listed in the Dictionary of Natural Products) and estimates of the expected total range from half to one million. Annotation of plant and fungal metabolites, particularly after analysis by liquid chromatography–mass spectrometry (LC–MS), is therefore a considerable challenge and one which can result in misleading annotations appearing in the literature.

This Special Issue of Metabolites, “Annotating Plant and Fungal Metabolites in MS-Based Metabolomics”, seeks contributions and opinions on how to tackle this problem. Most papers are likely to cover broad or specific compound annotation methods, such as those using databases (e.g., MS libraries), bioinformatics approaches, or expert interpretation. Papers covering annotation of metabolites in specific taxa might be relevant for taxa of importance. Strongly encouraged are contributions dealing with wider aspects that impact on the annotation goal. For example, methods for LC–MS data acquisition will affect not only what metabolites are detected but also the type and quality of data available to attempt annotation. Consideration of what MS data is optimal, either for ‘global’ metabolomics or specific metabolite classes, is therefore relevant to this Special Issue, as are new or improved technologies or methods that lead toward truly global plant and fungal metabolomic annotation, including the interaction with other ‘omics’ methods such as genomics. Finally, manuscripts dealing with the confidence level placed on an annotation of a plant or fungal metabolite are highly desired, bearing in mind that an annotation of a ‘multi-unit’ plant metabolite can vary from implying just one chemical structure (e.g., annotation as rutin) through several (e.g., as a quercetin 3-O-rhamnosylhexoside) and to many (e.g., as a quercetin O-glycoside).

Dr. Geoffrey Kite
Guest Editor

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• metabolomics
• plants
• fungi
• mass spectrometry
• annotation
• putative identification
• MS libraries
• bioinformatics
• MS interpretation
• natural product databases
• multi-omics
• emerging technologies
• confidence levels