Metabolite profiling of Dioscorea (yam) species reveals underutilised biodiversity and renewable sources for high-value compounds

Yams (Dioscorea spp.) are a multispecies crop with production in over 50 countries generating ~50 MT of edible tubers annually. The long-term storage potential of these tubers is vital for food security in developing countries. Furthermore, many species are important sources of pharmaceutical precursors. Despite these attributes as staple food crops and sources of high-value chemicals, Dioscorea spp. remain largely neglected in comparison to other staple tuber crops of tropical agricultural systems such as cassava (Manihot esculenta) and sweet potato (Ipomoea batatas). To date, studies have focussed on the tubers or rhizomes of Dioscorea, neglecting the foliage as waste. In the present study metabolite profiling procedures, using GC-MS approaches, have been established to assess biochemical diversity across species. The robustness of the procedures was shown using material from the phylogenetic clades. The resultant data allowed separation of the genotypes into clades, species and morphological traits with a putative geographical origin. Additionally, we show the potential of foliage material as a renewable source of high-value compounds

Deciphering lipid structures based on platform-independent decision rule sets

We developed decision rule sets for Lipid Data Analyzer (LDA; http://genome.tugraz.at/lda2), enabling automated and reliable annotation of lipid species and their molecular structures in high-throughput data from chromatography-coupled tandem mass spectrometry. Platform independence was proven in various mass spectrometric experiments, comprising low- and high-resolution instruments and several collision energies. We propose that this independence and the capability to identify novel lipid molecular species render current state-of-the-art lipid libraries now obsolete

NMR Metabolomics Protocols for Drug Discovery

Drug discovery is an extremely difficult and challenging endeavor with a very high failure rate. The task of identifying a drug that is safe, selective and effective is a daunting proposition because disease biology is complex and highly variable across patients. Metabolomics enables the discovery of disease biomarkers, which provides insights into the molecular and metabolic basis of disease and may be used to assess treatment prognosis and outcome. In this regard, metabolomics has evolved to become an important component of the drug discovery process to resolve efficacy and toxicity issues, and as a tool for precision medicine. A detailed description of an experimental protocol is presented that outlines the application of NMR metabolomics to the drug discovery pipeline. This includes: (1) target identification by understanding the metabolic dysregulation in diseases, (2) predicting the mechanism of action of newly discovered or existing drug therapies, (3) and using metabolomics to screen a chemical lead to assess biological activity. Unlike other OMICS approaches, the metabolome is “fragile”, and may be negatively impacted by improper sample collection, storage and extraction procedures. Similarly, biologically-irrelevant conclusions may result from incorrect data collection, pre-processing or processing procedures, or the erroneous use of univariate and multivariate statistical methods. These critical concerns are also addressed in the protocol

Identification of fungal metabolites from inside Gallus gallus domesticus eggshells by non-invasively detecting volatile organic compounds (VOCs)

The natural porosity of eggshells allows hen eggs to become contaminated with microbes from the nesting material and environment. Those microorganisms can later proliferate due to the humid ambient conditions while stored in refrigerators, causing a potential health hazards to the consumer. The microbes’ volatile organic compounds (mVOCs) are released by both fungi and bacteria. We studied mVOCs produced by aging-eggs likely contaminated by fungi and fresh-eggs using the non-invasive detection method of gas-phase sampling of volatiles followed by gas chromatography/mass spectrometry (GC/MS) analysis. Two different fungal species (Cladosporium macrocarpum and Botrytis cinerea) and two different bacteria species (Stenotrophomas rhizophila and Pseudomonas argentinensis) were identified inside the studied eggs. Two compounds believed to originate from the fungi itself were identified. One fungus-specific compound was found in both egg and the fungi: trichloromethane