Journal of Oil Palm Research Vol. 33 (3) September 2021, p. 555-564



Received: 2 June 2020   Accepted: 14 September 2020   Published Online: 20 November 2020

Liquid chromatography-mass spectrometry (LC-MS) has become a powerful analytical technique for studying broad coverage of chemical datasets describing complex biological systems and events. In order to interpret the underlying information in such datasets, multivariate analysis method such as principal component analysis (PCA) is crucial for multiple sample comparisons and multivariate data reduction. PCA has been used for evaluation of large-scale datasets derived from LC-MS analysis of fungal metabolites for many applications. Therefore, in this study, we describe on PCA as a descriptive tool to cope with large LC-MS datasets of intracellular metabolites of oil palm basal stem rot (BSR) fungal pathogen, Ganoderma boninense from in vitro liquid culture system. The results revealed a classification and grouping of G. boninense intracellular metabolites according to time trend, where the primary metabolites, i.e. glucose, gluconic acid, mannitol and malic acid were found differentially expressed in G. boninense. The presented findings suggest that the PCA model provides a general approach for handling, analysis and interpretation of large LC-MS datasets to reveal time-dependent changes of intracellular metabolites that may indicate G. boninense developmental process in vitro.



1 Malaysian Palm Oil Board, 6 Persiaran Institusi, Bandar Baru Bangi, 43000 Kajang, Selangor, Malaysia.

* Corresponding author e-mail:

Alonso, A; Marsal, S and Julia, A (2015). Analytical methods in untargeted metabolomics: State of the art in 2015. Front. Bioeng. Biotechnol., 3: 23-23.

Andersen, M R (2014). Elucidation of primary metabolic pathways in Aspergillus species: Orphaned research in characterizing orphan genes. Brief Funct. Genomics, 13: 451-455.

Bro, R and Smilde, A K (2014). Principal component analysis. Anal. Methods, 6: 2812-2831.

Chen, F; Ma, R and Chen, X L (2019). Advances of metabolomics in fungal pathogen–plant interactions. Metabolites, 9(8): 169.

Chong, K P; Dayou, J and Alexander, A (2017). Pathogenic nature of Ganoderma boninense and basal stem rot disease. Detection and Control of Ganoderma boninense in Oil Palm Crop. Springer. p. 5-12.

De Falco, B; Manzo, D; Incerti, G; Garonna, A P; Ercolano, M and Lanzotti, V (2019). Metabolomics approach based on NMR spectroscopy and multivariate data analysis to explore the interaction between the leafminer Tuta absoluta and tomato (Solanum lycopersicum). Phytochem. Anal., 30: 556-563.

DesRochers, N; Walsh, J P; Renaud, J B; Seifert, K A; Yeung, K K-C and Sumarah, M W (2020). Metabolomic profiling of fungal pathogens responsible for root rot in American ginseng. Metabolites, 10: 35.

Diana, G and Tommasi, C (2002). Cross-validation methods in principal component analysis: A comparison. Stat. Methods Appl., 11: 71-82.

Gika, H G;   Theodoridis,   G   A;   Plumb,   R   S and Wilson, I D (2014). Current practice of liquid chromatography–mass spectrometry in metabolomics and metabonomics. J. Pharm. Biomed. Anal., 87: 12-25.

Gotthardt, M; Kanawati, B; Schmidt, F; Asam, S; Hammerl, R; Frank, O; Hofmann, T; Schmitt- Kopplin, P and Rychlik, M (2019). Comprehensive analysis of the Alternaria mycobolome using mass spectrometry based metabolomics. Mol. Nutr. Food Res., 64(3): 1900558.

Hayden, H L; Rochfort, S J; Ezernieks, V; Savin, K W and Mele, P M (2019). Metabolomics approaches for the discrimination of disease suppressive soils for Rhizoctonia solani AG8 in cereal crops using 1H NMR and LC-MS. Sci. Total. Environ., 651(1): 1627-1638.

Holmes, E and Antti, H (2002). Chemometric contributions to the evolution of metabonomics: Mathematical solutions to characterising and interpreting complex biological NMR spectra. Analyst, 127: 1549-1557.

Ivosev, G; Burton, L and Bonner, R (2008). Dimensionality reduction and visualization in principal component analysis. Anal. Chem., 80: 4933- 4944.

Jolliffe, I T and Cadima, J (2016). Principal component analysis: A review and recent developments. Phil. Trans. R. Soc. A., 374: 20150202.

Jonsson, P; Bruce, S J; Moritz, T; Trygg, J; Sjöström, M; Plumb, R; Granger, J; Maibaum, E; Nicholson, J K; Holmes, E and Antti, H (2005). Extraction, interpretation and validation of information for comparing samples in metabolic LC/MS data sets. Analyst, 130: 701-707.

Kos, G; Lohninger, H and Krska, R (2003). Validation of chemometric   models   for   the   determination of deoxynivalenol on maize by mid-infrared spectroscopy. Mycotoxin Res., 19: 149-153.

Lazar, A G; Romanciuc, F; Socaciu, M A and Socaciu, C (2015). Bioinformatics tools for metabolomic data processing and analysis using untargeted liquid chromatography coupled with mass spectrometry. Bull. Univ. Agric. Sci., 72: 103-115.

Lewis, D and Smith, D (1967). Sugar alcohols (polyols) in fungi and green plants. I. Distribution, physiology and metabolism. New Phytol., 66: 143-184.

Liu, H; Zhao, X; Guo, M; Liu, H and Zheng, Z (2015). Growth and metabolism of Beauveria bassiana spores and mycelia. BMC Microbiol., 15: 267 pp.

Mamat, S F; Azizan, KA; Baharum, S N; Noor, N M and Aizat, W M (2018). ESI-LC-MS based-metabolomics data of mangosteen (Garcinia mangostana Linn.) fruit pericarp, aril and seed at different ripening stages. Data Brief, 17: 1074-1077.

Mazlan, O; Aizat, W M; Aziz Zuddin, N S; Baharum, S N and Noor, N M (2018). LC-MS data for metabolomics analysis of Garcinia mangostana L. seed germination. Data Brief, 21: 2221-2223.

Othman, A; Abd Rasid, O; Nagappan, J; Leslie Low, E T; Fook Hwa, L; Nurazah, Z; Syahanim, S; Dzulkafli, S B; Rozali, N L; Bohari, B; Angel, L P L; Tahir, N I; Idris, A S; Marjuni, M; Sundram, S; Mohd Din, A; Ramli, U S and Mohamad Arif, A M (2019). Molecular characterisation of oil palm responses to Ganoderma infection. International Seminar on Breeding for Ganoderma Tolerance in Oil Palm. Kuala Lumpur Convention Centre, Kuala Lumpur. p. 137-146.

Patel, T K and Williamson, J D (2016). Mannitol in plants, fungi, and plant-fungal interactions. Trends Plant Sci., 21: 486-497.

Rees, R W (2006). Ganoderma stem rot of oil palm (Elaeis guineensis): Mode of infection, epidemiology and biological control. Ph.D thesis, University of Bath, United Kingdom.

Robison, F M; Turner, M F; Jahn, C E; Schwartz, H F; Prenni, J E; Brick, M A and Heuberger, A L (2018). Common bean varieties demonstrate differential physiological and metabolic responses to the pathogenic fungus Sclerotinia sclerotiorum. Plant Cell Environ., 41: 2141-2154.

Sanchez, S and Demain, A L (2008). Metabolic regulation and overproduction of primary metabolites. Microb. Biotechnol., 1: 283-319.

Siless, G E; Gallardo, G L; Rodriguez, M A; Rincon, Y A; Godeas, AM and Cabrera, G M (2018). Metabolites from the dark septate endophyte Drechslera sp. evaluation by LC/MS and principal component analysis of culture extracts with histone deacetylase inhibitors. Chem. Biodivers., 15: e1800133.

Son, S Y; Lee, S; Singh, D; Lee, N-R; Lee, D-Y and Lee, C H (2018). Comprehensive secondary metabolite profiling toward delineating the solid and submerged-state fermentation of Aspergillus oryzae KCCM 12698. Front. Microbiol., 9: 1076.

Sumner, L W; Amberg, A; Barrett, D; Beale, M H; Beger, R; Daykin, C A; Fan, T W; Fiehn, O; Goodacre, R; Griffin, J L; Hankemeier, T; Hardy, N; Harnly, J; Higashi, R; Kopka, J; Lane, A N; Lindon, J C; Marriott, P; Nicholls, A W; Reily, M D; Thaden, J J and Viant, M R (2007). Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics, 3(3): 211-221.

Tahir, N I; Shaari, K; Abas, F; Ishak, Z and Tarmizi, A (2016). Metabolome analysis of oil palm clone P325 of different planting trials. J. Oil Palm Res., 28: 431- 441.

Tugizimana, F; Djami-Tchatchou, A T; Fahrmann, J F; Steenkamp, P A; Piater, LA and Dubery, I A (2019). Time-resolved decoding of metabolic signatures of in vitro growth of the hemibiotrophic pathogen Colletotrichum sublineolum. Sci. Rep., 9: 3290.

Veeramohan, R; Azizan, K A; Aizat, W M; Goh, H-H; Mansor, S M; Yusof, N S M; Baharum, S N and Ng, C L (2018). Metabolomics data of Mitragyna speciosa leaf using LC-ESI-TOF-MS. Data Brief, 18: 1212-1216.

Wiemken, V (2007). Trehalose synthesis in ectomycorrhizas-a driving force of carbon gain for fungi? New Phytol., 174: 228-230.

Wisecaver, J H; Slot, J C and Rokas, A (2014). The evolution of fungal metabolic pathways. PLoS Genet., 10: e1004816.

Yin, P; Peter, A; Franken, H; Zhao, X; Neukamm, S S; Rosenbaum, L; Lucio, M; Zell, A; Haring, H U; Xu, G and Lehmann, R (2013). Preanalytical aspects and sample quality assessment in metabolomics studies of human blood. Clin. Chem., 59: 833-845.

Yuan, S; Yan, J; Wang, M; Ding, X; Zhang, Y; Li, W; Cao, J and Jiang, W (2019). Transcriptomic and metabolic profiling reveals ‘Green Ring’ and ‘Red Ring’ on jujube fruit upon postharvest Alternaria alternata infection. Plant Cell Physiol., 60: 844-861.