Abstract:
Metabolomic data analysis involves assessing, identifying, and quantifying all metabolites, endogenous and exogenous, within biological samples. It allows the global assessment of the cellular state in the context of the immediate environment as it considers gene expression, genetic regulation, enzyme
regulation, altered kinetic activity as well as changes in metabolic reactions. Plants being sessile
organisms, depend heavily on metabolites to defend themselves against various pathogen attacks e.g., fungi. Metabolomic analysis has been used to determine the defensive metabolites associated with plant pathogens with the aim of understanding both the defense mechanisms of the plant, and infection mechanisms of the pathogen for better disease control and prevention.
This study aimed to assess whether metabolomic and chemical fingerprint analyses can be used in early disease diagnosis as it analyses the state of the plant’s physiological changes due to fungal pathogen infection, its proficiency in measuring disease severity, and identifying possible pathogen-related
biomarkers. The fungal pathogens that were a point of focus for this study were Cercospora zeina which causes grey leaf spot, a devastating maize foliar disease characterized by necrotic lesions, and Fusarium verticillioides, which produces fumonisin mycotoxins that can plant growth. Maize leaf samples showing
different stages of disease severity were collected from a field trial by Syngenta in Howick. Some samples were collected from plants grown in a glasshouse and inoculated with C. zeina in vitro. Cowpea seeds were inoculated in vitro with F. verticillioides and grown in a phytotron. Metabolites were extracted from
the leaf samples and analysed using NMR and GCMS to detect changes in the plants' metabolome, as these techniques encompass both spectroscopic and volatile organic compounds detection. Maize samples’ NMR results showed significant differences between the infected and healthy plants, in
both the field trial and glasshouse trial. The NMR data of cowpea samples showed minor differences. However, the GCMS data for both pathosystems showed significant differences between inoculated and uninoculated samples, and certain potential disease-related biomarkers were observed in the chromatograms. These biomarkers shared similarities to hexadecanoic acid, 1-(hydroxymethyl)-1,2-
ethanediyl ester, 9,12,15-octadecatrienoic acid, 1,3-dimethoxypropan-2-yl palmitate and butyl-9,12,15- octadecatrienoate. From the results obtained we can conclude that metabolomic and chemical fingerprint analyses are efficient tools in successfully diagnosing plant fungal diseases by indicating various diseaserelated biomarkers, that can be used for pathogen infection diagnosis
