Ganoderma boninense (G. boninense) is a polyporoid fungus belonging to the family Ganodermataceae and classified under Basidiomycetes [1]. It is the primary causal agent of basal stem rot (BSR) and upper stem rot (USR), which together represent the most destructive diseases affecting oil palm plantations in Southeast Asia. These infections lead to direct losses in palm stands, reduced productivity of diseased palms, and the need for premature replanting, resulting in substantial economic impact [2]. Although G. boninense has been unequivocally identified as the main pathogen responsible for these diseases, effective strategies for early detection and control remain limited and insufficiently developed [3].

In recent years, metabolomics has emerged as a promising approach for understanding plant–pathogen interactions at the molecular level. Gas chromatography–mass spectrometry (GC–MS), in particular, offers high sensitivity and analytical throughput, making it a robust platform for untargeted metabolomics studies across diverse biological systems [4]. However, reliable GC–MS-based metabolomics requires careful standardization of the workflow, encompassing optimized pre-analytical procedures, analytical conditions, and computational data processing to ensure reproducibility and meaningful biological interpretation.

Plant metabolites play a pivotal role in host defense and pathogen recognition, reflecting biochemical changes that occur during infection [5]. Despite this, the metabolic responses of oil palm roots to G. boninense infection remain poorly characterized. To address this knowledge gap, a comparative GC–MS-based metabolomics approach was applied to non-infected and G. boninense-infected oil palm roots at 14 and 30 days post-infection (dpi). This strategy aimed to identify metabolite alterations associated with pathogen invasion and to establish a principal component analysis (PCA)-based model capable of discriminating infection status.

Using 80% (v/v) methanol for metabolite extraction, distinct metabolic profiles were obtained for non-infected and infected roots at both time points. Multivariate data analysis using PCA revealed clear clustering and separation between treatment groups, indicating pronounced metabolic reprogramming following G. boninense infection. Loading bi-plots further enabled the identification of metabolites contributing most strongly to sample differentiation, confirming the robustness of the analytical approach. 

Notably, several steroidal compounds—including stigmasterol, stigmast-5-en-3-ol (3β), and ergost-5-en-3-ol (3β)—as well as multiple fatty acid derivatives such as methyl hexadecanoate, methyl octadecanoate, methyl (9Z,12Z)-octadeca-9,12-dienoate, dimethyl benzene-1,4-dicarboxylate, methyl (Z)-octadec-6-enoate, methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl)propanoate, and 5-eicosene were consistently more abundant in G. boninense-infected oil palm roots. The increased presence of these metabolites suggests their involvement in host responses to pathogen infection and highlights their potential utility as diagnostic indicators.

In conclusion, this GC–MS-based untargeted metabolomics approach provides clear evidence of infection-induced metabolic alterations in oil palm roots during early and intermediate stages of G. boninense infection. The identified metabolites represent promising candidates for the development of chemical markers for early BSR detection. Given the current absence of a single definitive biomarker for G. boninense, the use of a metabolite signature or panel, supported by multivariate analysis, offers a practical and informative strategy for improving early disease diagnosis and management in oil palm plantations.

References:

• S. Idris, Basal Stem Rot in Malaysia-Biology, Economic Importance, Epidemiology, Detection and control, In: Proceeding of the International Workshop on Awareness, Detection and Control of Oil Palm Devastating Diseases, Kuala Lumpur Convention Centre, Malaysia, 2009.
• Flood, Y. Hasan, H. Foster. Ganoderma diseases of oil palm-an interpretation from Bah Lias Research Station, Planter, 78 (921), 689–710, 2002.
• Hushiarian, N. A. Yusof, S. W. Dutse, Detection and control of Ganoderma boninense: Strategies and perspectives, Springerplus, 2(555), 1-12, 2013.
• Zhou, D.Q. Qin, P.W. Zhang, X.T. Chen, B.J. Liu, D.M. Cheng, Z.X. Zhang. Integrated LC–MS and GC–MS-based untargeted metabolomics studies of the effect of azadirachtin on Bactrocera dorsalis larvae. Sci. Rep., 10, 1-11, 2020.
• Hu, X. Chang, T. Dai, L. Li, P. Liu, G. Wang, P. Liu, Z. Huang, X. Liu. Metabolic profiling to identify the latent infection of strawberry by Botrytis cinerea. Evolutionary Bioinformatics, 15 (2019), 1-7, 2019.

Tarikh Input: 17/12/2025 | Kemaskini: 17/12/2025 | azah