Poster Presentation Australian and New Zealand Metabolomics Conference 2018

Metabolite Profiling of Scytalidium parasiticum, an antifungus agent using LC-TOF-MS (#102)

SYARUL NATAQAIN BAHARUM 1 , Rafidah Ahmad 1 , Choon Kit Lim 2 3 , Nurul Fadhilah Marzuki 2 , Yit Kheng Goh 2 , Kamalrul Azlan Azizan 1 , You Keng Goh 3 , Kah Joo Goh 3
  1. Universiti Kebangsaan Malaysia, Bangi, SELANGOR, Malaysia
  2. Advanced Agriecological Research Sdn Bhd, Kuala Lumpur, Malaysia
  3. Advanced Agriecological Research Sdn Bhd, Kuala Lumpur, Malaysia

The oil palm, known as world’s major sources of edible oil, an economically important commodities especially in Malaysia and Indonesia, Regrettably, this important source of diesel biofuel facing the threat of basal stem rot disease.  Many study has been carried out on Ganoderma boninense as the main pathogen that cause this devastating disease. It has been estimated that the losses due to this pathogen is up to 500 million USD per year. Previously, our group had successfully isolated a mycoparasite, Scytalidium parasiticum sp. nov., that exhibited antifungus activity againts Ganoderma. In the current study, we had managed to profile group of metabolites that could be potential candidates to combat the ganoderma infection by using LC-TOF-MS.  S .parasiticum were grown in XP maize, XP rice and OPEM. In order to evaluate differences in the acquired LC-TOF-MS Base Peak Chromatograms, PCA was carried out on the pre-processed LC-TOF-MS data matrices. Multivariate analysis, PCA and PLS-DA loading plots described the potential biomarkers for distinguishing samples of all group. Spectra with similar profiles will plot closely together in PCA (normalized Pareto scaled) Scores plot. The PCA scores showed that none of the samples were outside the hoteling T2 95% confidence and required 7 principal components to described 60% of the variation in the X matrix (R2X=0.606) with an acceptable predictability of 10% (Q2X=0.104), meaning that 10% of the total variation of X matrix could be predicted by the model. The first two principal components (PC1 and PC2) described the variation in the X matrix (R2X=0.296) with an acceptable predictability of 19% (Q2X=0.187). The first component and the two components are accounting for 16% and 13% of total variation respectively separated the control groups and the treatment groups. The differential metabolites obtained from multivariate data analysis were then validated using ANOVA with post-hoc Tukey’s.  S. parasiticum that grown in OPEM showed distinct spectra from the other two substrates which belongs to prenylflavonoids, ketones and aldehydes.