1H NMR metabolomics analysis of leaf tissue from oil palms against Ganoderma boninense

doi:10.21203/rs.3.rs-3952665/v1

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¹H NMR metabolomics analysis of leaf tissue from oil palms against Ganoderma boninense

https://doi.org/10.21203/rs.3.rs-3952665/v1

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published 02 Aug, 2024

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Introduction

Breeding for oil palm resistance against basal stem rot caused by Ganoderma boninense is challenging and lengthy. Advanced oil palm gene pools are very limited hence it is assumed that parental palms have experienced genetic drift and lost their resistance genes against Ganoderma. High-throughput selection criteria should be developed. Metabolomic analysis using ¹H Nuclear Magnetic Resonance (NMR) is known to be easy to prepare and the produced metabolite could be used as a diagnostic tool for disease incidents in various host-pathogen combinations.

Objectives

The study objective is to identify metabolite variations of Dura (D) and Pisifera (P) parental palms with different resistance against Ganoderma and moderately resistant Tenera (DxP) using ¹H NMR analysis

Methods

Leaf tissues of seven different oil palm categories consisting of: resistant, moderate, and susceptible Dura (D), moderate and susceptible Pisifera (P), resistant Tenera/Pisifera (T/P) progenies, and moderately resistant DxP variety progenies were sampled and determined their metabolites using NMR spectroscopy.

Results

Twenty-nine types of metabolites were identified and most of the metabolites fall in the monosaccharides, amino acid, and fatty acids compound classes. The PCA, PLS-DA, and heatmap multivariate analysis indicated that there were two identified groups of resistances based on their metabolites. The first group consisted of resistant T/P, moderate P, resistant D, and moderately resistant DxP. In contrast, the second group consisted of susceptible P, moderate D, and susceptible D. Glycerol and ascorbic acid were detected as a biomarker candidate by OPLS-DA to differentiate moderately resistant DxP against susceptible D and P. The pathway analysis suggested that the glycine, serine, and threonine metabolism and taurine and hypotaurine metabolism were involved in the oil palm defense mechanism against Ganoderma.

Conclusion

Metabolomic study with ¹H NMR was able to describe the metabolites composition that could differentiate the characteristics of oil palm resistance against basal stem rot caused by Ganoderma boninense. These metabolites revealed in this study had enormous potential to become support tools to breed new oil palm varieties with higher resistance against basal stem rot.

1H NMR

Ganoderma

metabolite

moderately resistant DxP

parental palms

Oil palm is the most efficient oil crop compared to other commodities (Murphy 2014). Palm oil is used as a major ingredient in up to 50% of all products used daily, from cosmetics to food (Kaniapan et al. 2021). However, the greatest threat to the sustainability of producing oil palm is Ganoderma boninense, since it will decrease oil palm productivity significantly. Ganoderma as a pathogen for basal stem rot (BSR) in oil palm could decrease oil palm productivity mainly by lowering bunch number and weight, but also decreasing productive palm standing in heavily infested estates, Ganoderma could cause 80% oil palm mortality in the middle of their normal production cycle (Turner and Gilbanks 2003). The spread of Ganoderma attacks is very fast, through contact with root mycelium and basidiospores in the air (Hushiarian et al. 2013). Based on a survey by Mohd Shukri et al. (2020) from an oil palm plantation area of 37,351.81 ha, Ganoderma can attack 9.2% of the survey area.

Intensification through breeding for resistant superior planting material is one of the approaches to achieving a sustainable oil palm industry. Therefore, breeding for oil palm resistant to BSR caused by Ganoderma is crucial. This breeding was started with the identification of oil palm germplasm resistance against Ganoderma. The determination of oil palm germplasm resistance towards Ganoderma has been done through: nursery screening of progenies from high-yielding parental palms, and identification of resistant palm populations in endemic areas in the field (Wening et al. 2016). A lot of effort and resources were needed to conduct nursery screening, therefore metabolomic analysis could be used to accelerate the identification and selection process. The metabolomic analysis is one of the approaches to identify metabolite changes in cellular activity due to physiological influences or external stresses such as pathogen infections (Khan et al. 2017). Metabolomics approaches can assist crop breeding through the development of genetic markers obtained from metabolomics analysis as selection tools (Litvinov et al. 2021).

The latest method in the metabolomic analysis is hydrogen 1 nuclear magnetic resonance (¹H NMR) spectroscopy. This method has been used to identify oil palm resistance variation against Ganoderma previously (Isha et al. 2019; Isha et al. 2020; Pancoro et al. 2022). ¹H NMR approach is easier to conduct compared to other mass spectrometry, due to: easier sample preparations without derivatization, extraction, and separation; and the result can be quantified (Isha et al. 2019). This study belongs to untargeted metabolomics research because it identified the widest range of metabolites present in extracted samples without prior knowledge of the metabolite content (Johnson et al. 2016). Leaf tissue samples were chosen because they are not destructive without having to kill oil palm trees and are easy to obtain. The study objective is to identify metabolite variations of Dura (D) and Pisifera (P) parental palms with different resistance against Ganoderma and progenies of moderately resistant Tenera (DxP) using ¹H NMR analysis.

2.1 Sampling

Samples of oil palm leaf tissue used in this study were collected from oil palm field trials of the Indonesian Oil Palm Research Institute (IOPRI), Bah Jambi and Marihat, Simalungun Regency, North Sumatra, Indonesia. The sample consists of Dura (D) and Pisifera (P) oil palm parents, which were indicated as resistant, moderate, and susceptible to Ganoderma from nursery screening and field observations. The resistance characteristics of oil palm parents were identified through screening in nurseries in previous research with the categories: resistant, with a disease incidence index (DII) of below 70; moderate, with a DII of between 71 to 100; and susceptible, with a DII of above 100 (Supplementary Fig. 1). Meanwhile, the resistant Tenera/Pisifera (T/P) parents came from field trials in Bah Jambi, where the mortality rate based on observations in 2022 was only 9% compared to other crosses where the mortality rates were more than 50% (Fig. 1). Commercial DxP (Dura x Pisifera) progenies which were known to be moderately resistant to Ganoderma were also used in this study. All sample individuals were then categorized based on resistance characteristics and fruit type, hence there were 7 categories described in Table 1. Each category had 3 individuals as replications, so there were 21 experimental units.

Table 1

The sample set of 18 parental palms and 3 moderately resistant DxP progenies
No	Category	Location	Type	Resistance
1	T/P res	Bah Jambi	Tenera	Field resistant
2	T/P res	Bah Jambi	Tenera	Field resistant
3	T/P res	Bah Jambi	Pisifera	Field resistant
4	D res	Bah Jambi	Dura	Resistant, DII 29.4
5	D res	Marihat	Dura	Resistant, DII 43.1
6	D res	Bah Jambi	Dura	Resistant, DII 70.9
7	DxP modres	Bah Jambi	Tenera	Moderately resistant DxP
8	DxP modres	Bah Jambi	Tenera	Moderately resistant DxP
9	DxP modres	Bah Jambi	Tenera	Moderately resistant DxP
10	P mod	Bah Jambi	Pisifera	Moderate, DII 85.2
11	P mod	Bah Jambi	Pisifera	Moderate, DII 87.9
12	P mod	Bah Jambi	Pisifera	Moderate, DII 95.2
13	D mod	Marihat	Dura	Moderate, DII 84.5
14	D mod	Marihat	Dura	Moderate, DII 84.7
15	D mod	Marihat	Dura	Moderate, DII 84.7
16	P sus	Bah Jambi	Pisifera	Susceptible, DII 109.8
17	P sus	Bah Jambi	Pisifera	Susceptible, DII 109.8
18	P sus	Bah Jambi	Pisifera	Susceptible, DII 113.5
19	D sus	Marihat	Dura	Susceptible, DII 131.1
20	D sus	Marihat	Dura	Susceptible, DII 131.1
21	D sus	Marihat	Dura	Susceptible, DII 131.1
D sus = susceptible Dura, D mod = moderate Dura, D res = resistant Dura, P sus = susceptible Pisifera, P mod = moderate Pisifera, T/P res = resistant Tenera/Pisifera, and DxP modres = moderately resistant Dura x Pisifera

2.2 Sample preparation and ¹H NMR analysis

Leaves were taken from the same part (leaf number 17) to be used as samples. The sample was prepared using a quenching or freezing technique using liquid nitrogen in the field to stop the metabolic process (Bort et al. 2014). The sample was then ground using a mortar and liquid nitrogen. Sample extraction using a method with modification described previously by Rivera-P´erez et al. (2023). Briefly, 50 ± 0.1 mg of the freeze-dried leaves powder was weighed. Then 1.0 mL of Methanol-d4 containing 0.001% trimethylsilyl propionic acid-d4 sodium salt (TMSP) as an internal standard. The mixture was vortex-homogenized for 60 sec, sonicated for 20 min, and centrifuged for 5 min at 10,000 rpm. The collected supernatant was transferred into an NMR tube to be analyzed by ¹H NMR. ¹H NMR analysis was performed using JEOL JNM-ECZR NMR Spectrometer (Tokyo, Japan) which operated at a proton frequency of 500 MHz with a proton test carried out on suppressed signal H₂0 at a chemical shift of 4.98 ppm.

2.3 Preprocessing of ¹H NMR data for multivariate data analysis

¹H NMR data was processed using an untargeted metabolomics approach (Rivera-P´erez et al. 2023). NMR spectra were preprocessed for further multivariate data using Mestrenova software 14.3 for fourier transform stage, phase, and baseline correction (Pancoro et al. 2022). NMR spectra were calibrated to the TMSP-2,2,3,3-d4 signal at δ 0.0 ppm as a reference standard (Supplementary Fig. 2). To identify and quantify metabolite from ¹H NMR spectra, the ASICS package in R programming language was used (Lefort et al. 2019).

2.4 Multivariate data analysis

The normalized NMR data matrix was imported to MetaboAnalyst 5.0 for multivariate data analysis (Xia et al. 2009). Before multivariate statistical analysis, data were Auto-scaled. The analyses carried out principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), orthogonal projections to latent structures discriminant analysis (OPLS-DA), and heatmap to classify and differentiate samples based on the resulting multidimensional data. Variable importance in projection (VIP) is used to discriminate metabolites. The metabolites with a VIP value (> 1.0) were considered relevant for oil palms that are resistant and susceptible to Ganoderma. Analysis of candidate biomarkers was also measured by receiver operating characteristic (ROC) curves to evaluate their diagnostic significance.

2.5 Metabolic pathway analysis

Using MetaboAnalyst 5.0, metabolic pathway analysis incorporates pathway enrichment analysis was conducted to analyze the most important pathway-altered Ganoderma resistance. Based on the p-value (< 0.05) and impact value score (> 0.1), the possible target metabolic pathways were filtered out (Liu et al. 2019; Bhushan et al. 2021; Pancoro et al. 2022). The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used as a reference for the identification of metabolic pathways.

2.2 ¹H NMR metabolites profile of leaf tissue from parental oil palms

¹H NMR analysis was performed on the leaf tissue of palm oil samples (Table 1) to assess the differences in their metabolite compositions. In characterizing ¹H NMR spectra, the ASICS package was utilized to obtain metabolite data. Twenty-nine (29) types of metabolites were tentatively identified from 7 categories of disease resistance against Ganoderma. Each category has a different number of metabolites identified, there were 25 metabolites identified from susceptible P and moderate P, 24 metabolites from moderate D, 23 metabolites from resistant D, 21 metabolites from resistant T/P, and 19 metabolites from moderately resistant DxP and susceptible D (Supplementary Table 2). The number of types of metabolites discovered in this study was similar to Isha et al. (2019), which identified 22 metabolites from oil palm leaf tissue.

The metabolites relative concentrations were obtained through enrichment analysis and then classifying the metabolites in this study into 11 main metabolite classes of organic compounds: benzamides, fatty alcohols, organic dicarboxylic acids, alcohols, polyols, sulfonic acids, cholines, furanones, alkanolamines, fatty acids and conjugates, amino acids and peptides, and monosaccharides. The results showed monosaccharides, amino acid, and fatty acid compound classes were dominated in all categories of disease resistance (Fig. 2). Resistant T/P has the highest relative percentage of monosaccharides (37%) compared to other categories, while moderately resistant DxP has the lowest relative percentage of monosaccharides (19%). Susceptible P has the highest relative percentage of amino acids (23%) compared to other categories, while Resistant T/P has the lowest relative percentage (16%). Moderately resistant DxP. Resistant T/P has the highest relative percentage of fatty alcohols (16%) compared to other categories, both susceptible D and moderately resistant DxP have a relative percentage of fatty alcohols 13%, while other categories have a relatively lower percentage of fatty alcohols (5% for moderate D, resistant D, susceptible P, and moderate P). On the other hand, moderate D, resistant D, susceptible P, and moderate P equally have the highest relative percentage of fatty acids (19%) compared to moderately resistant DxP (6%) and susceptible D and resistant T/P were not present. Moderately resistant DxP did not have the highest relative percentage of each compound, but it has the most complete class of compounds.

Compared with previous research on ¹H NMR analysis of oil palm resistance to Ganoderma, this research revealed different relative metabolites. The results of ¹H NMR analysis of leaf tissue of oil palm seedlings infected with Ganoderma (14 dpi and 30 dpi) obtained 10 main metabolite classes of organic compounds: carboxylic acids and derivatives, organooxygen compounds, benzene and substituted derivatives, biotin and derivatives, organonitrogen compounds, hydroxy acids and derivatives, flavonoids, indoles and derivatives, prenol lipids, and steroids and steroid derivatives (Isha et al. 2019; Isha et al. 2020). Meanwhile, the results of ¹H NMR analysis of stem tissue of oil palm infected with Ganoderma in the fields obtained 10 main metabolite classes of organic compounds: polyketide, nucleic acids, organooxygen compounds, fatty acyls, organonitrogen compounds, organoheterocyclic compounds, benzenoids, carbohydrates, and organic acids (Pancoro et al. 2022).

Different developmental stages and different specific tissues will influence metabolite accumulation (Shen et al. 2023). Therefore, different tissues sampled in this study might affect the metabolite variation observed compared to previous research published. This phenomenon is caused by the spatiotemporal expression of metabolites, as occurs in some plants, such as barley, blueberries, poplar, and ginseng (Gupta et al. 2019; Günther et al. 2020; Balasubramanian et al. 2023; Sun et al. 2021). Other than that, the variances in the metabolites relative concentrations may be due to the solvent used. Only methanol was used in this study, while the previous research used a mixture of methanol and water. Methanol is effective in dissolving phenolics, flavonoids, saponins, and polar organic compounds. On the other hand, water is effective in dissolving salts, ions, carbohydrates, amino acids, and organic acids (Oh et al. 2021). This could be the reason why there were fewer organic acid compounds in this study compared to previous research.

2.3 Classification and identification of metabolites from parental palms and moderately resistant DxP

Multivariate data analysis was used to determine the compound variation of each parental palm and their resistance towards Ganoderma. The pairwise 3D and 2D PCA results of Fig. 3 and Supplementary Fig. 3 were able to classify visually between parental palms and their resistance level with principal components (PC1, PC2, and PC3) value of 63.5%. The paired PCA analysis successfully separated between resistant and susceptible parental palms. Resistant D, resistant T/P, moderate P, and moderately resistant DxP relatively in one group. The metabolite content of moderately resistant DxP showed similarities to the resistant parental palms group. The results were in line with Wening et al. (2020), in the construction of Ganoderma resistant oil palm material, the selected crosses were derived from a resistant female parent (Dura) and a resistant male parent (Pisifera). As supporting data, pairwise 2D PCA between susceptible D and moderately resistant DxP, a distinction was observed, although there was still an overlap between the two groups, the cumulative total of the principal components was 82.5%. Meanwhile, pairwise 2D PCA between susceptible P and moderately resistant DxP also observed a distinction with the cumulative total of the principal components was 69.4% (Supplementary Fig. 4).

Heatmap was used to picture the different concentrations of each metabolite from different types of parental palms and their resistance levels against Ganoderma. The heatmap results showed the same pattern as PCA, moderately resistant DxP grouped with resistant parental palms (Fig. 4), although the individual heatmap results showed that there were a few outliers found, such as one replication of resistant T/P which was grouped with moderate and susceptible groups (Supplementary Fig. 3). There were upregulated metabolites such as taurine, glycerol, D-mannose, D-glucose, guanidino acetic acid, xylitol, and ascorbic acid in the resistant palm group, while in the susceptible palms, the metabolites were down-regulated. Consistent with Pancoro et al. (2022), taurine, D-mannose, xylitol, guanidino acetic acid, and ascorbic acid were up-regulated in healthy palms compared to severe palms caused by Ganoderma. On the contrary, threonic acid, L-arabitol, and L-serine were down-regulated in resistant palms compared to susceptible palms.

PLS-DA analysis was utilized to differentiate significant metabolites in resistant, susceptible parental palms, and moderately resistant DxP. A total of 11 metabolites were identified as significant with VIP scores > 1 (Fig. 5). The following metabolites were identified: taurine, betaine, TMAO, glycerol, threonic acid, and L-serine, which belong to the class of monosaccharide and amino acid compounds. Three significant metabolites (taurine, D-mannose, and threonic acid) were also detected in the PLS-DA results of Pancoro et al. (2022) in the response of oil palm to Ganoderma. Taurine plays a role as an anti-stress agent in several plants (Lee et al. 2015; Hafeez et al. 2022). D-mannose belongs to the lectin family, responsible for the action of resistance when infected with pathogens (Hwang and Hwang 2011). Threonic acid and myo-inositol were reported to play a role in the plant response to the endophytic fungus Cladosporium tenuissimum (Zhou et al. 2022). In this study, two categories of resistance were also compared using OPLS-DA. The aim was to identify significant metabolites for both categories (Supplementary Fig. 5). Based on the analysis, 8, 12, 10, 8, 11, and 12 significant metabolites were identified for categories D sus – D mod, D sus – D res, D sus – DxP modres, P sus – P mod, P sus – P res, and P sus – DxP modres respectively (VIP score > 1.0).

2.4 Biomarkers and pathway analysis for oil palm resistance to Ganoderma

The use of biomarkers is becoming more critical in the early detection of diseases (Wang and Huang 2019). This study used OPLS-DA and ROC to analyze the oil palm metabolites that can differentiate oil palm resistance against Ganoderma. The OPLS-DA results and the ROC curve (AUC) = 1 can be utilized as biomarkers. An AUC score of 1 indicates that the classifier can effectively distinguish between positive and negative classes, without allowing for false positives (Pancoro et al. 2022). Several metabolites were indicated as significant biomarkers (Table 2). Interestingly, glycerol and ascorbic acid were found to have a significant on susceptible D and susceptible P. They can be used as potential biomarkers for screening moderately resistant DxP. Furthermore, the box plot visualization was used to highlight a significant difference in concentrations of ascorbic acid and glycerol (Fig. 6).

Glycerol is known to be involved in the plant defense system, mainly in the form of glycerol-3-phosphate (G3P). G3P acts as a regulator that moves freely in the systemic acquired resistance (SAR), which will give broader spectrum immunity against pathogen infection in soybeans (Shine et al. 2019). In Arabidopsis increased G3P levels are associated with enhanced resistance to Colletotrichum higgisianum fungi (Chanda et al. 2008). Even the application of glycerol in wheat could increase their resistance to powdery mildew (Li et al. 2020). The same role is also played by ascorbic acids. Ascorbic acid is known to have a role in protecting plant cells from pathogen attacks that induce oxidative stress (Boubakri 2017). In vitro studies, showed the growth of colonies of the soil-borne hemibiotrophic fungus Macrophomina on media containing ascorbic acid decreased significantly compared to media that did not contain ascorbic acid (Noor and Little 2022).

Different biomarker results were obtained when using LC-MS, which identified pyridine, chelidonic acid, and asparagine as metabolites that determined the resistance of oil palm to Ganoderma (Nusaibah et al. 2016; Dzulkafli et al. 2019). Metabolomic studies using GC-MS also discovered different metabolites that determine oil palm resistance against Ganoderma, namely steroidal compounds and fatty acids with their derivatives and mannose with xylose by (Isha et al. 2020; Rozali et al. 2017). These differences can be caused by measuring instruments or differences in the tissue being analyzed.

Table 2

List of metabolite compounds from OPLS-DA analysis of oil palm leaves samples of *Ganoderma* resistant, moderate, and susceptible parental palms and moderately resistant DxP. The asterisk (*) indicates that the metabolite has AUC = 1 and has the potential as a candidate biomarker.
No.	Metabolite	VIP scores between Dsus with			VIP scores between Psus with
No.	Metabolite	Dmod	Dres	DxP modres	Pmod	T/Pres	DxP modres
1	2-Oxoisovalerate	1.70*	1.16*	-	1.50	1.46	1.50*
2	2-Propanol	0.93	1.36*	1.35*	1.16	0.52	1.08
3	alpha-Hydroxyisobutyric Acid	0.46	0.65	0.95	0.81	1.57	1.18
4	Ascorbic Acid	0.52	1.56*	1.44*	1.32	0.86	1.67*
5	Betaine	0.32	0.56	0.68	1.38	1.13	0.99
6	Choline Chloride	0.92	0.05	0.74	0.02	0.02	0.04
7	D-Sorbitol	0.22	1.11	1.04	0.84	0.19	1.40*
8	Ethanolamine	0.35	0.53	0.14	0.83	0.95	0.13
9	Galactitol	0.60	0.29	0.94	0.56	0.59	0.38
10	Glycerol	1.22	0.57	1.51*	0.87	0.35	1.69*
11	Guanidinoacetic Acid	0.22	0.32	0.62	0.23	0.67	1.12
12	Isovaleric Acid	-	1.16*	-	1.17	0.52	0.29
13	L-Alanine	-	-	-	0.42	0.52	0.39
14	L-Arabitol	0.27	1.41*	1.10	1.03	1.03	1.56*
15	L-Serine	0.54	0.57	1.30*	1.34	1.50	1.24
16	Malonate	0.64	0.56	0.41	0.91	0.54	0.80
17	Methylmalonic Acid	0.98	0.90	0.34	1.29	1.02	0.01
18	Myo-Inositol	0.77	0.25	0.79	0.31	0.87	0.11
19	N-Acetylglycine	1.57*	1.16*	-	0.27	0.52	0.29
20	Propylene Glycol	1.70*	1.29*	1.36*	0.54	1.34	0.61
21	Taurine	1.78*	1.42*	1.10*	0.84	1.70	1.33
22	Threitol	1.47	1.18	0.95	0.27	1.05	0.93
23	Threonic Acid	1.03	1.69*	1.20*	0.88	0.62	0.53
24	TMAO	0.42	0.49	0.12	1.40	1.07	0.18
25	Xylitol	0.01	1.11	1.12	0.63	0.29	1.39*

Pathway analysis was used to determine the metabolite pathways involved in oil palm resistance to Ganoderma. Based on pathway analysis using the KEGG database (p-value < 0.05 and pathway impact > 0.1), BSR disease may affect two metabolite pathways: glycine, serine, and threonine metabolism and the taurine and hypotaurine metabolism (Fig. 7). Pathway visualization using KEGG of the metabolites involved was presented in (Supplementary Fig. 6). The discovery of the glycine, serine, and threonine metabolism pathways in this study was in line with the findings of Pancoro et al. (2022). However, L-serine and threonic acid as stated earlier were lower in resistant parental palms. In contrast, taurine was higher in the resistant parent plants. This may be in pathway analysis that integrates all metabolites produced without considering the categories of resistant and susceptible plants.

The glycine, serine, and threonine metabolism function as the basis of protein synthesis and plays an important role in plant responses against environmental stress (Trovato et al. 2021). This was also observed in the metabolomic analysis of mandarin fruit preventively applied with cyclic lipopeptides (CLP) from Bacillus subtilis and showed that the glycine, serine, and threonine metabolism pathway had the highest score and indeed had an important role in the mandarin fruit response towards post-harvest stresses (Tunsagool et al. 2019). Several proteins that fell into the glycine-rich protein (GRP) superfamily were involved in the signaling and plant response towards stress cellularly (Czolpinska and Rurek 2018). Meanwhile, the taurine and hypotaurine metabolism pathways were the most notably enriched in the resistant interaction during Heterodera glycines infection in soybeans (Song et al. 2019). The taurine and hypotaurine metabolism were also the top pathways in resistant and susceptible tomatoes infected by Fusarium oxysporum (Zhao et al. 2017). The amino acid derivatives taurine and hypotaurine induce an efficient detoxifying enzymatic action and scavenge singlet oxygen in many eukaryotes (Pitari et al. 2022). The metabolite pathways that were identified in this study can be used as a reference for further research on oil palm defense mechanisms against Ganoderma boninense.

Metabolomic study with ¹H NMR was able to describe the metabolites composition that could differentiate the characteristics of oil palm resistance against basal stem rot caused by Ganoderma boninense. The PCA results could classify resistant D, resistant T/P, moderate P, and moderately resistant DxP relatively in one group. PLS-DA analysis was successfully used to identify 11 significant metabolites. OPLS-DA and ROC analysis discovered that glycerol and ascorbic acid could be used as potential biomarkers to screen moderately resistant DxP. The pathway analysis showed that glycine, serine, and threonine metabolism and the taurine and hypotaurine metabolism have roles in the oil palm resistance against Ganoderma. These metabolites revealed in this study had enormous potential to become support tools to breed new oil palm varieties with higher resistance against basal stem rot.

Author contributions HYR, MS, WDD, BAS, and SRW designed the study. HYR conducted and monitored the experiment at IOPRI. SN and ANS contributed to sampling and metabolite data analysis. HYR and SN drafted the manuscript. All authors participated in the critical reading and writing of the final manuscript.

Funding The research is supported by SEARCA Doctoral scholarship and IOPRI internal research funds

Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical approval This article does not contain any studies with human and/or animal participants performed by any of the authors.

Supplementary Information The online version contains supplementary material available at …

Balasubramanian, V. K., Velickovic, D., Rubio Wilhelmi, M. D. M., Anderton, C. R., Stewart, C. N., DiFazio, S., Blumwald, E., and Ahkami, A. H. (2023). Spatiotemporal Metabolic Responses to Water Deficit Stress in Distinct Leaf Cell-types of Poplar (preprint), Plant Biology. https://doi.org/10.1101/2023.11.30.569476
Bhushan, B., Upadhyay, D., Sharma, U., Jagannathan, N., Singh, S. B., and Ganju, L. (2021). Urine metabolite profiling of Indian Antarctic Expedition members: NMR spectroscopy-based metabolomic investigation, Heliyon, 7(5), e07114. https://doi.org/10.1016/j.heliyon.2021.e07114
Boubakri, H. (2017). The Role of Ascorbic Acid in Plant–Pathogen Interactions, 255–271 in M. A. Hossain, S. Munné-Bosch, D. J. Burritt, P. Diaz-Vivancos, M. Fujita, and A. Lorence, eds., Ascorbic Acid in Plant Growth, Development and Stress Tolerance, Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-74057-7_10
Chanda, B., Venugopal, S. C., Kulshrestha, S., Navarre, D. A., Downie, B., Vaillancourt, L., Kachroo, A., and Kachroo, P. (2008). Glycerol-3-Phosphate Levels Are Associated with Basal Resistance to the Hemibiotrophic Fungus Colletotrichum higginsianum in Arabidopsis, Plant Physiology, 147(4), 2017–2029. https://doi.org/10.1104/pp.108.121335
Czolpinska, M., and Rurek, M. (2018). Plant Glycine-Rich Proteins in Stress Response: An Emerging, Still Prospective Story, Frontiers in Plant Science, 9, 302. https://doi.org/10.3389/fpls.2018.00302
Dzulkafli, S. B., Othman, A., Shahwan, S., Nurazah, Z., Manaf, M. A. A., Idris, A. S., Amiruddin, M. D., Tahir, N. I., Ramli, U. S. (2019). Identification of chelidonic acid and asparagine in ganoderma boninense- inoculated oil palm seedlings. Journal of Oil Palm Research. 31(1).53–66. https://doi.org/10.21894/jopr.2019.0008
Günther, C. S., Dare, A. P., McGhie, T. K., Deng, C., Lafferty, D. J., Plunkett, B. J., Grierson, E. R. P., Turner, J. L., Jaakola, L., Albert, N. W., and Espley, R. V. (2020). Spatiotemporal Modulation of Flavonoid Metabolism in Blueberries, Frontiers in Plant Science, 11, 545. https://doi.org/10.3389/fpls.2020.00545
Gupta, S., Rupasinghe, T., Callahan, D. L., Natera, S. H. A., Smith, P. M. C., Hill, C. B., Roessner, U., and Boughton, B. A. (2019). Spatio-Temporal Metabolite and Elemental Profiling of Salt Stressed Barley Seeds During Initial Stages of Germination by MALDI-MSI and µ-XRF Spectrometry, Frontiers in Plant Science, 10, 1139. https://doi.org/10.3389/fpls.2019.01139
Hafeez, A., Rasheed, R., Ashraf, M. A., Rizwan, M., Ali, S. (2022). Effects of exogenous taurine on growth, photosynthesis, oxidative stress, antioxidant enzymes and nutrient accumulation by Trifolium alexandrinum plants under manganese stress, Chemosphere, 308(3), https://doi.org/10.1016/j.chemosphere.2022.136523
Hernández Bort, J. A., Shanmukam, V., Pabst, M., Windwarder, M., Neumann, L., Alchalabi, A., Krebiehl, G., Koellensperger, G., Hann, S., Sonntag, D., Altmann, F., Heel, C., and Borth, N. (2014). Reduced quenching and extraction time for mammalian cells using filtration and syringe extraction, Journal of Biotechnology, 182–183, 97–103. https://doi.org/10.1016/j.jbiotec.2014.04.014
Hushiarian, R., Yusof, N. A., and Dutse, S. W. (2013). Detection and control of Ganoderma boninense: strategies and perspectives, SpringerPlus, 2(1), 555. https://doi.org/10.1186/2193-1801-2-555
Hwang, I. S., and Hwang, B. K. (2011). The Pepper Mannose-Binding Lectin Gene CaMBL1 Is Required to Regulate Cell Death and Defense Responses to Microbial Pathogens, Plant Physiology, 155(1), 447–463. https://doi.org/10.1104/pp.110.164848Z
Isha, A., Akanbi, F. S., Yusof, N. A., Osman, R., Mui-Yun, W., and Abdullah, S. N. A. (2019). An NMR Metabolomics Approach and Detection of Ganoderma boninense -Infected Oil Palm Leaves Using MWCNT-Based Electrochemical Sensor, Journal of Nanomaterials, 2019, 1–12. https://doi.org/10.1155/2019/4729706
Isha, A., Yusof, N. A., Osman, R., Wong, M.-Y., and Abdullah, S. N. A. (2020). NMR-based metabolomics reveals effect of Ganoderma boninense infection on oil palm leaf at 30 days post-infection, Plant Omics, (13(01). 15–20. https://doi.org/10.21475/POJ.13.01.20.p2071
Johnson, C. H., Ivanisevic, J., and Siuzdak, G. (2016). Metabolomics: beyond biomarkers and towards mechanisms, Nature Reviews Molecular Cell Biology, 17(7), 451–459. https://doi.org/10.1038/nrm.2016.25
Kaniapan, S., Hassan, S., Ya, H., Patma Nesan, K., and Azeem, M. (2021): The Utilisation of Palm Oil and Oil Palm Residues and the Related Challenges as a Sustainable Alternative in Biofuel, Bioenergy, and Transportation Sector: A Review, Sustainability, 13(6), 3110. https://doi.org/10.3390/su13063110
Khan N, Ali S, Shahid MA, Kharabian-Masouleh A. (2017). Advances in detection of stress tolerance in plants through metabolomics approaches. Plant Omics, 10(3). 153–63. https://doi.org/doi: 10.21475/poj.10.03.17.pne600
Lee, D.-H. (2015). In Vitro Analysis of Taurine as Anti-stress Agent in Tomato (Solanum Lycopersicum)-Preliminary Study (Advances in Experimental Medicine and Biology), 75–85 in J. Marcinkiewicz and S. W. Schaffer, eds., Taurine 9, Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-15126-7_7
Lefort, G. et al. (2019). ASICS: an R package for a whole analysis workfow of 1D 1H NMR spectra. Bioinformatics 35, 4356–4363. https://doi.org/10.1093/bioinformatics/btz248
Li, Y., Qiu, L., Liu, X., Zhang, Q., Zhuansun, X., Fahima, T., Krugman, T., Sun, Q., and Xie, C. (2020). Glycerol-Induced Powdery Mildew Resistance in Wheat by Regulating Plant Fatty Acid Metabolism, Plant Hormones Cross-Talk, and Pathogenesis-Related Genes, International Journal of Molecular Sciences, 21(2), 673. https://doi.org/10.3390/ijms21020673
Litvinov, D. Y., Karlov, G. I., and Divashuk, M. G. (2021). Metabolomics for Crop Breeding: General Considerations, Genes, 12(10), 1602. https://doi.org/10.3390/genes12101602
Liu, G., Lee, D. P., Schmidt, E., and Prasad, G. (2019). Pathway Analysis of Global Metabolomic Profiles Identified Enrichment of Caffeine, Energy, and Arginine Metabolism in Smokers but Not Moist Snuff Consumers, Bioinformatics and Biology Insights, 13, https://doi.org/10.1177/1177932219882961
Mohd Shukri, I., Izzuddin, M. A., Mohd Hefni, R., and Idris, A. S. (2020). Geostatistics of Oil Palm Trees Affected by Ganoderma Disease in Low and High Planting Density, IOP Conference Series: Earth and Environmental Science, 540(1), 012065. https://doi.org/10.1088/1755-1315/540/1/012065
Murphy, D. J. (2014). The Future of Oil Palm As a Major Global Crop : Opportunities and Challenges. Journal of Oil Palm Research, 26, 1-24
Noor, A., and Little, C. R. (2022). Evaluating the Role of Exogenously Applied Ascorbic Acid in Rescuing Soybean Plant Health in The Presence of Pathogen-Induced Oxidative Stress, Pathogens, 11(10), 1117. https://doi.org/10.3390/pathogens11101117
Nusaibah, S. A., Siti Nor Akmar, A., Idris, A. S., Sariah, M., and Mohamad Pauzi, Z. (2016). Involvement of metabolites in early defense mechanism of oil palm (Elaeis guineensis Jacq.) against Ganoderma disease, Plant Physiology and Biochemistry, 109, 156–165. https://doi.org/10.1016/j.plaphy.2016.09.014
Oh, S.-W., Imran, M., Kim, E.-H., Park, S.-Y., Lee, S.-G., Park, H.-M., Jung, J.-W., and Ryu, T.-H. (2023). Approach strategies and application of metabolomics to biotechnology in plants, Frontiers in Plant Science, 14, 1192235. https://doi.org/10.3389/fpls.2023.1192235
Pancoro, A., Karima, E., Apriyanto, A., and Effendi, Y. (2022). 1H NMR metabolomics analysis of oil palm stem tissue infected by Ganoderma boninense based on field severity Indices, Scientific Reports, 12(1), 21087. https://doi.org/10.1038/s41598-022-25450-5
Pitari, G., Duprè, S., Spirito, A., Antonini, G., and Amicarelli, F. (2002). Hypotaurine Protection on Cell Damage by Singlet Oxygen (Advances in Experimental Medicine and Biology), 157–162 in L. Della Corte, R. J. Huxtable, G. Sgaragli, and K. F. Tipton, eds., Taurine 4, Springer US, Boston, MA. https://doi.org/10.1007/0-306-46838-7_16
Rivera-Pérez, A., Romero-González, R., and Garrido Frenich, A. (2023). Untargeted 1H NMR-based metabolomics and multi-technique data fusion: A promising combined approach for geographical and processing authentication of thyme by multivariate statistical analysis, Food Chemistry, 420, 136156. https://doi.org/10.1016/j.foodchem.2023.136156
Rozali, N. L., Yarmo, M. A., Idris, A. S., Kushairi, A., Ramli, U. S. (2017). Metabolomics differentiation of oil palm (Elaeis guineensis Jacq.) spear leaf with contrasting susceptibility to Ganoderma boninense, Plant Omics, 10(02), 45–52. https://doi.org/10.21475/poj.10.02.17.pne364
Shen, S., Zhan, C., Yang, C., Fernie, A. R., and Luo, J. (2023). Metabolomics-centered mining of plant metabolic diversity and function: Past decade and future perspectives, Molecular Plant, 16(1), 43–63. https://doi.org/10.1016/j.molp.2022.09.007
Shine, M. B., Gao, Q., Chowda-Reddy, R. V., Singh, A. K., Kachroo, P., and Kachroo, A. (2019). Glycerol-3-phosphate mediates rhizobia-induced systemic signaling in soybean, Nature Communications, 10(1), 5303. https://doi.org/10.1038/s41467-019-13318-8
Song, W., Qi, N., Liang, C., Duan, F., and Zhao, H. (2019). Soybean root transcriptome profiling reveals a nonhost resistant response during Heterodera glycines infection, PLOS ONE, 14(5), e0217130. https://doi.org/10.1371/journal.pone.0217130
Sun, C., Ma, S., Li, L., Wang, D., Liu, W., Liu, F., Guo, L., and Wang, X. (2021). Visualizing the distributions and spatiotemporal changes of metabolites in Panax notoginseng by MALDI mass spectrometry imaging, Journal of Ginseng Research, 45(6), 726–733. https://doi.org/10.1016/j.jgr.2021.04.001
Trovato, M., Funck, D., Forlani, G., Okumoto, S., and Amir, R. (2021). Editorial: Amino Acids in Plants: Regulation and Functions in Development and Stress Defense, Frontiers in Plant Science, 12, 772810. https://doi.org/10.3389/fpls.2021.772810
Tunsagool, P., Wang, X., Leelasuphakul, W., Jutidamrongphan, W., Phaonakrop, N., Jaresitthikunchai, J., Roytrakul, S., Chen, G., and Li, L. (2019). Metabolomic study of stress responses leading to plant resistance in mandarin fruit mediated by preventive applications of Bacillus subtilis cyclic lipopeptides, Postharvest Biology and Technology, 156, 110946. https://doi.org/10.1016/j.postharvbio.2019.110946
Turner, P., and Gilbanks, R. (2003). Nursery techniques. Oil Palm Cultivation and Management. 2nd Ed Kuala Lumpur: The Incorporated Society of Planters. 173–254.
Wang, L., and Huang, Y. (2019). Evaluating classification performance of biomarkers in two‐phase case‐control studies, Statistics in Medicine, 38(1), 100–114. https://doi.org/10.1002/sim.7966
Wening, S., Rahmadi, H. Y., Arif, M., Supena, N., Siregar, H. A., Prasetyo, A. E., Setiowati, R. D., Yenni, Y., Suprianto, E., Ernayunita, Faizah, R., Sujadi, Susanto, A., Purba, A. R. (2016). Construction of Ganoderma resistant oil palm planting material: progress in IOPRI. Sixth IOPRI-MPOB Int. Semin. Curr. approaches Res. Manag. pests Ganoderma Dis. oil palm high Product. 54–66
Xia, J., Psychogios, N., Young, N., and Wishart, D. S. (2009). MetaboAnalyst: a web server for metabolomic data analysis and interpretation, Nucleic Acids Research, 37 (Web Server), W652–W660. https://doi.org/10.1093/nar/gkp356
Zhao, M., Ji, H.-M., Gao, Y., Cao, X.-X., Mao, H.-Y., Liu, P., Ouyang, S.-Q. (2017). Comparative transcriptome profiling conferring of resistance to Fusarium oxysporum infection between resistant and susceptible tomato. bioRxiv. https://doi.org/10.1101/116988
Zhou, Y., Guo, L., Liang, C., Liu, L., Wang, J., Yang, L., et al. (2022). First report of coriander leaf blight diseases caused by Cladosporium tenuissimum in China and analysis of metabolic alterations related to pathogenic processes, Plant Pathology, 71(5), 1092-1102. https://doi.org/10.1111/ppa.13542

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You are reading this latest preprint version

¹H NMR metabolomics analysis of leaf tissue from oil palms against Ganoderma boninense

Status:

Journal Publication

Version 1

Abstract

Introduction

Objectives

Methods

Results

Conclusion

Figures

1 Introduction

2 Material and Methods

2.1 Sampling

2.2 Sample preparation and ¹H NMR analysis

2.3 Preprocessing of ¹H NMR data for multivariate data analysis

2.4 Multivariate data analysis

2.5 Metabolic pathway analysis

3 Result and Discussion

2.2 ¹H NMR metabolites profile of leaf tissue from parental oil palms

2.3 Classification and identification of metabolites from parental palms and moderately resistant DxP

2.4 Biomarkers and pathway analysis for oil palm resistance to Ganoderma

4 Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1

1H NMR metabolomics analysis of leaf tissue from oil palms against Ganoderma boninense

Status:

Journal Publication

Version 1

Abstract

Introduction

Objectives

Methods

Results

Conclusion

Figures

1 Introduction

2 Material and Methods

2.1 Sampling

2.2 Sample preparation and 1H NMR analysis

2.3 Preprocessing of 1H NMR data for multivariate data analysis

2.4 Multivariate data analysis

2.5 Metabolic pathway analysis

3 Result and Discussion

2.2 1H NMR metabolites profile of leaf tissue from parental oil palms

2.3 Classification and identification of metabolites from parental palms and moderately resistant DxP

2.4 Biomarkers and pathway analysis for oil palm resistance to Ganoderma

4 Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1

¹H NMR metabolomics analysis of leaf tissue from oil palms against Ganoderma boninense

2.2 Sample preparation and ¹H NMR analysis

2.3 Preprocessing of ¹H NMR data for multivariate data analysis

2.2 ¹H NMR metabolites profile of leaf tissue from parental oil palms