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. 2024 Nov 1;72(45):24944–24952. doi: 10.1021/acs.jafc.4c03986

Metabolite Profiling of Annual Ryegrass Cultivars to Assess Disease Resistance and Susceptibility

Pushpendra Koli †,‡,⊥,*, Manjree Agarwal §, David Kessell ∥, Shalini Mahawar ⊥, Xin Du †, Yonglin Ren †,⊥,*, Simon J McKirdy †,*
PMCID: PMC11565740  PMID: 39486421

Abstract

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Direct immersion solid-phase microextraction coupled with gas chromatography–mass spectroscopy was used to create chemical fingerprints of annual ryegrass cultivars (Lolium rigidum). Extracts made of the inflorescences of four cultivars and one accession of annual ryegrass were assessed to identify differential metabolites between those resistant to and susceptible to bacterial galls associated with annual ryegrass toxicity (ARGT). Numerous compounds were identified. Principal component analysis showed distinct clustering of metabolites from disease-resistant and disease-susceptible cultivars. Partial least-squares-discriminant analysis identified sterols, esters, aldehydes, and terpenes that correlated with resistance to galls formation. Esters, sterols, phenols, heterocyclics, fatty acids, organofluorides, and siloxanes were predominant in resistant genotypes, whereas alcohols, aldehydes, terpenes, and hydrocarbons were predominant in susceptible genotypes. The identification of differentially expressed metabolites provides potential chemical markers to guide breeding strategies for ARGT resistance in ryegrass.

Keywords: annual ryegrass toxicity, chemometrics, ryegrass cultivars, DI-SPME, GC–MS, forage, metabolites, pattern recognition, principal component analysis

1. Introduction

Annual ryegrass (Lolium rigidum) is a forage grass that provides nutrition and high palatability to livestock.1,2 It is a major source of winter and spring forage, replacing oats and other forage crops because of its longer growing period and higher nutritional status.3 In Australia, ryegrass in some regions is associated with the livestock disease annual ryegrass toxicity (ARGT). This disease results after livestock ingest seed galls formed from mutual infections of a soil-borne pathogenic bacteria (Rathayibacter toxicus) and a plant-parasitic seed gall nematode (Anguina funesta).4 Widely grown cultivars of annual ryegrass such as Wimmera and Merredin are attacked by this nematode which carries the bacteria associated with ARGT.5 The cultivar Safeguard was bred for resistance to A. funesta infection, featuring early flowering, herbicide susceptibility, resistance to cereal root disease, and increased herbage production. Its dominant trait of nematode resistance will be introduced to other ARG varieties through interbreeding, providing the dual benefit of enhanced productivity and reduced risk of ARGT later in the season.6 However, the establishment of sufficient plant numbers for gene introgression can be difficult, limiting its widespread use.6 Annual ryegrass studies have focused on agronomic traits7 such as high biomass,3,8 seed production,9 forage quality,10 forage persistency,11 environmental and biofuel traits,12 and animal performance.13,14 Metabolomics studies on ryegrass addressed salinity stress,15 drought stress,16 and herbicide resistance.17 There have been no metabolomic studies of volatile and semivolatile organic compounds associated with resistance and susceptibility to the ARGT-associated bacterium R. toxicus and the nematode A. funesta. The use of solid-phase microfiber extraction SPME fiber is advantageous as it saves sample preparation costs and improves the detection limit. SPME is commonly used in pharmaceutical and toxicological metabolomics studies.18,19 Obtaining peaks using SPME fibers from the sample matrix is highly dependent on the sensitivity and selectivity of adsorption materials.20 Acetonitrile is chosen for pretreating stamens to detect metabolic components because of its high extraction efficiency, low interference, broad-spectrum extraction, purity, stability, and safety for solid-phase microextraction (SPME) fiber coating compared to other solvents. In this study, three-phase fiber [2 cm 50/30 μm divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS)] efficiently extracted several metabolites from samples, and metabolites varied by plant cultivar. The use of direct immersion SPME (DI-SPME) has been promoted since the late 1990s and used in quantitative analysis of biological samples including plant tissues,21,22 insects,23 pesticide residues,24,25 blood analysis,26 pharmaceuticals,27 and wine.28

In this study, we employed DI-SPME coupled with GC–MS to identify metabolic components, including semivolatile organic compounds, present in A. funesta-resistant and susceptible ryegrass cultivars. Total metabolites of samples were analyzed to distinguish metabolites linked to ARGT resistance and susceptibility. The aim of this study was to identify beneficial metabolites to inform breeding against A. funesta ARGT susceptibility and as a chemical signal for diagnosing.

2. Materials and Methods

2.1. Chemicals and Reagents

Saturated alkane standard mixture (C7–C40) was procured from Sigma-Aldrich (Castle Hill, Australia). Analytical-grade acetonitrile (Sigma-Aldrich) was purchased from Rowe Scientific Pty Ltd., Perth, Australia. The three-phase DVB/CAR/PDMS (Sigma-Aldrich) 2 cm 50/30 μm SPME fiber was procured from Bellefonte, PA, USA.

2.2. Collection of Plant Samples

Plant samples from four cultivars and one accession of annual ryegrass were collected during the flowering stage in a Western Australia farmer’s field in October 2021. According to the farmers, the sowing material history indicated that the ryegrass seeds were commercially obtained, including cv Safeguard from Irwin Hunter Seeds, Australia; cv Wimmera from Baker Seed Co., Australia; and cv Tetila from S&W Seed Company, Australia. Whereas the wild type of Italian ryegrass, collected from a field where farmers reported the presence of bacterial gall in the previous season, has been confirmed as susceptible to ARGT. Two Safeguard cultivars were present in the field, differentiated by their establishment periods: Safeguard-I, indicating specimen established in a grazing field for the past 3 years and Safeguard-II, denoting a 12 years establishment period. Descriptions of these ryegrass cultivars and their characteristics29−31 are presented in Table 1. The inflorescence portion was detached from the leaves and stems of each plant and stored in a freezer at −20 °C until further analysis.

Table 1. Annual Ryegrass Cultivars Used in This Study.

cultivar/accession scientific name characteristics location
safeguard-I Lolium rigidum diploid; ARGT resistant 31°40′33″S, 116°38′12″E
safeguard-II L. rigidum diploid; ARGT resistant 31°40′11″S, 116°38′14″E
Tetila L. multiflorum tetraploid; ARGT susceptible 32°87′80″S, 115°76′2″E
Wimmera L. rigidum diploid; ARGT susceptible 32°87′08″S, 115°76′74″E
wild type Lolium sp. ARGT susceptible 32°17′33″S, 115°50′54″E
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2.3. Sample Preparation and DI-SPME Extraction

To perform sample homogenization, 200 mg of the inflorescence portion was mixed with 1 mL of acetonitrile (HPLC grade) in a 2 mL microtube. The microtube was sealed with a screw cap and shaken in a ball mill (BeadBug-Microtube Homogenizer, D1030-E) using 2 milling balls for 2 min at 600g and again topped up with 1 mL of acetonitrile, and the process was repeated. The liquid portion was collected and centrifuged at 600g for 3 min (Dynamica, velocity 13 μL). The supernatant (1.5 mL) was collected into a 2 mL GC vial and stored at 4 °C until GC–MS analysis.

To perform the sample extraction using DI-SPME, a three-phase fiber (50/30 μm) with a combination coating of divinylbenzene/carboxen/poly(dimethylsiloxane) (DVB/CAR/PDMS, Stableflex 2 cm) was used. Before its use, all new fibers were preconditioned as per the manufacturer’s instructions (conditioned for 60 min at 270 °C). To optimize the extraction protocol, time for sample extraction (30, 45, 60, and 120 min), extraction temperature (25, 35, and 45 °C), and desorption time (3, 6, and 10 min) were evaluated based on the number of peaks and peak areas. The SPME fiber was directly immersed into the sample contained in the GC vial for 60 min at 35 °C (Temperature Oscillation Shaker, THZ-92AS) for extraction; thereafter, the fiber was taken out and directly injected into the GC–MS instrument and kept for 6 min for desorption to analyze the sample. Separate DI-SPME fibers were used to extract the three replicate sets of each sample.

2.4. GC–MS Analysis

An Agilent GC (7890B) coupled with a 5977B MSD mass spectrometer (Agilent Technologies, Santa Clara, CA, USA) with an Agilent HP-5 ms capillary column (30 m length, 0.25 mm internal diameter), 0.25 μm film thickness with a (5%-phenyl)-methylpolysiloxane stationary phase. The extracted samples were injected in splitless mode with ultrahigh purity helium (Air Liquide, Australia) as the carrier gas with a flow rate of 1 mL/min. The temperature program was adopted from our previous study,32 starting at 50 °C with a 5 min hold, then ramped at 6 °C/min to 90 °C, 8 °C/min to 140 °C, 6 °C/min to 190 °C, 4 °C/min to 240 °C, and finally at 50 °C/min to 300 °C followed by holding at 300 °C for 12 min. The total run time was 51.95 min to complete the elution of one sample. The detector operated in electron impact ionization mode at 70 eV, and the spectra were acquired at the frequency of three scans/s in scan mode to cover 50 to 550 atomic mass units (amu). The transfer line temperatures of the MSD and ion source temperature were 280 and 230 °C, respectively. Peaks were identified using experimentally obtained Kovats retention indices (RI) with a combination of alkane standards (C7–C40, catalogue number 49451-U; Castle Hill, NSW, Australia) and the database of mass spectra at the National Institute of Standards and Technology Mass Spectrometry (NIST MS).

2.5. Data Analysis

A total of 15 samples, including three replicates of each cultivar, were scanned over a range of 50 to 550 amu, and the peak areas were integrated based on abundance into the total ion chromatogram. MS data acquisition was completed using Mass Hunter Acquisition software (vB.06.00; Agilent Technologies, Santa Clara, CA, USA).19,32 Data arrangement and data preprocessing was performed in Microsoft Excel. To perform multivariate analysis, data were normalized using mean centered data scaling function in MetaboAnalyst 5.0 (2022) (https://www.metaboanalyst.ca/MetaboAnalyst/upload/StatUploadView.xhtml). Unsupervised multivariate techniques, such as clustering and principal component analysis (PCA), identify patterns and relationships within data without predefined labels, allowing for the exploration and discovery of hidden structures. In contrast, supervised multivariate techniques, like regression and classification, utilize labeled data to predict or classify outcomes based on known relationships, providing insights into specific associations and facilitating predictive modeling.33 PCA, partial least-squares-discriminant analysis (PLS-DA), sparse partial least-squares (sPLS), and K-means clustering techniques were utilized. Additionally, one-way analysis of variance (one-way ANOVA) was performed using MetaboAnalyst 5.0. PCA is an unsupervised classification method which helps to visualize covariance and correlation among big data sets without the need for prior knowledge.34 Tukey’s post hoc honestly significant difference was performed to calculate the statistical difference at p < 0.05. A cluster heat map was obtained to visualize a hierarchically clustered data matrix. The quality assessment (Q2) statistic, typically derived from cross-validation, serves as a qualitative measure of consistency between predicted and original data. R software, version 4.1.2 was used to generate figures.

3. Results and Discussion

3.1. Identification of Metabolites

The 27 most abundant metabolites, based on a peak area of more than 2000 × 104 included semivolatile organic compounds extracted with DI-SPME and separated and identified by GC–MS in the five samples of annual ryegrass are presented in Table 2, whereas the list of all 108 compounds identified is given in Table S1. Compounds were from a diverse range of chemical groups, viz alcohols, aldehydes, esters, fatty acids, heterocyclics, hydrocarbons, phenols, terpenes, sterols, and others. 106 compounds were found significantly (p < 0.05) different among all five ryegrass plant samples. 19 compounds viz. dodecamethylpentasiloxane; myristic acid; neophytadiene; 7-octadecyne, 2-methyl-; 9-octadecyne; phytol; gamolenic acid; stearic acid; tricosyl pentafluoropropionate; carbonic acid, eicosyl vinyl ester; tetracosanal; heptacos-1-ene; tetracosanoic acid, methyl ester; hexacosanoic acid, methyl ester; cholest-4-en-3-one, 7-hydroxy-, (7β)- etc. were common to all samples. When we examined the unique and individual compounds in specific ryegrass cultivars, we found that 26 compounds were identified in resistant cultivars (including both Safeguard-I and Safeguard-II). Some important compounds were identified, such as decamethylcyclopentasiloxane; dodecamethylpentasiloxane; trimethylsilyl 3-methyl-4-[(trimethylsilyl)oxy]benzoate; 2,5-dihydroxybenzoic acid, 3TMS derivative; margaric acid; cis-10-heptadecenoic acid; hexadecanoic acid, 2-hydroxy-1-(hydroxymethyl)ethyl ester; erucamide; 2,3-bis(acetyloxy)propyl (9E,12E,15E)-9,12,15-octadecatrienoate; fumaric acid, 8-chlorooctyl tridecyl ester; ergosterol peroxide; 2-methoxy-4-vinylphenol; 2-chloropropionic acid, octadecyl ester; behenic alcohol; 13-docosenoic acid, methyl ester, (Z)-; 9,12,15-octadecatrienoic acid, 2,3-dihydroxypropyl ester, (Z,Z,Z) etc. Conversely, susceptible ryegrass cultivars showed a total of 24 compounds, with five in Tetila (naphthalene, 1,3-dimethyl-; 14-methyl-6-pentadecenoic acid; 1-decanol, 2-hexyl; methyl stearidonate and octadecane, 1-bromo-etc.), six in Wimmera (nonanal; (Z)-9-heptadecenoic acid methyl ester; hexacosanal; eicosanoic acid, phenylmethyl ester; benzyl n-eicosanoate; betulinaldehyde etc.), and 13 in wild (fumaric acid, tetradec-3-enyl undecyl ester; cholest-4-en-3-one, 7-hydroxy-, (7β)-; stigmastane-3,6-dione, (5α)-; phytyl hexanoate; tetracosyl acetate; cedran-diol, (8S,14)-; 2-heptadecanol; tridecanedial; β-ocimene etc.).

Table 2. Twenty-Seven Most Highly Expressed Compounds Identified in Five Annual Ryegrass Cultivarsa.

compound RT genotype (peak area × 104)
 RI molecular formula MW
    safeguard-I safeguard-II Tetila Wimmera wild type      
neophytadiene 29.446 13,397 13,534 6487 5768 8374 1841 C20H38 278.30
palmitic acid 31.981 10,275 1645 n.d. n.d. 753 1967 C16H32O2 256.24
hexanedioic acid, dioctyl ester 39.871 6427 n.d. 5459 5436 3350 2401 C22H42O4 370.31
tricosyl pentafluoropropionate 40.432 10,039 7873 7801 5930 1680 2483 C26H47F5O2 486.35
tricosyl trifluoroacetate 40.461 3545 n.d. 2614 2480 n.d. 2487 C25H47F3O2 436.35
2-chloropropionic acid, octadecyl ester 40.467 n.d. 4229 n.d. n.d. n.d. 2488 C21H41ClO2 360.28
carbonic acid, eicosyl vinyl ester 40.57 4616 5658 9546 9264 9266 2503 C23H44O3 368.32
heptacos-1-ene 41.771 11,997 1278 10,211 8574 4426 2682 C27H54 378.42
n-tetracosanol-1 41.834 7395 n.d. 6186 6073 10,645 2691 C24H50O 354.39
tetracosanoic acid, methyl ester 42.143 3051 796 318 826 295.84 2734 C25H50O2 382.38
tetracosyl acetate 42.752 n.d. n.d. n.d. n.d. 712.89 2814 C26H52O2 396.40
squalene 42.973 2880 4395 3817 n.d. 6880 2840 C30H50 410.39
hexacosanal 43.001 n.d. n.d. n.d. 4349 n.d. 2844 C26H52O 380.40
heptacosyl heptafluorobutyrate 43.619 4614 3424 13,968 11,790 8235 2915 C31H55F7O2 592.40
octacosanal 45.41 4730 n.d. n.d. n.d. 915 3084 C28H56O 408.43
myristic acid, 9-hexadecenyl ester, (Z)- 45 n.d. 5967 n.d. n.d. 2085 3085 C30H58O2 450.44
2,3-bis(acetyloxy)propyl (9E,12E,15E)-9,12,15-octadecatrienoate 45 7109 n.d. n.d. n.d. n.d. 3097 C25H40O6 436.28
9,12,15-octadecatrienoic acid, 2,3-dihydroxypropyl ester, (Z,Z,Z)- 45.571 n.d. 7063 n.d. n.d. n.d. 3099 C21H36O4 352.26
fumaric acid, 8-chlorooctyl tridecyl ester 45.719 1517 n.d. n.d. n.d. n.d. 3210 C25H45ClO4 444.30
brassicasterol acetate 45.902 1649 1044 588 n.d. n.d. 3223 C30H48O2 440.36
octacosanoic acid, methyl ester 46.085 n.d. n.d. 1362 3091 680 3236 C29H58O2 438.44
Stigmasterol 46.32 2592 1435 n.d. 1384 2013 3254 C29H48O 412.37
cholest-4-en-3-one, 7-hydroxy-, (7β)- 48.357 7748 6324 5302 2030 2650 3485 C27H44O2 400.33
stigmastane-3,6-dione, (5α)- 48.863 n.d. n.d. n.d. n.d. 3451 3613 C29H48O2 428.36
ergosterol peroxide 48.912 12,939 n.d. n.d. n.d. n.d. 3616 C28H44O3 428.33
ergosterol peroxide Ac (acetyl) derivative 48.941 n.d. 13,654 n.d. 2085 n.d. 3617 C30H46O4 470.34
methyl nomilinate (isomer 2) 49.198 1450 2493 n.d. n.d. n.d. 3630 C29H38O10 546.25
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a

RI-retention index; RT-retention time; n.d.-not detected; MW-molecular weight. Values represent the mean of three replicates.

The presence of esters, fatty acids, and siloxane in ryegrass was consistent with previous studies on Italian ryegrass (Lolium multiflorum) and other forage crops.15,35,36 This is the first time that research has been undertaken to get the detailed differential metabolites present in various types of ryegrasses by using DI-SPME, in relation to their resistance and susceptibility to the ARGT-associated nematode.

3.2. Multivariate Analysis to Classify Ryegrass Cultivars

To see the difference in metabolites obtained by DI-SPME-GC–MS between the ryegrass samples, PCA was performed for 5 samples (with 3 replications each) and 108 variables, and the PCA plot is visualized in Figure 1. PC1 explained 42.1% of the total variability in the metabolite data, while PC2 explained 33.7%. Together, these cumulatively accounted for 75.8% of the total variance. The correlation provided additional information about how the metabolite profiles vary among the ryegrass samples. It was clearly visible that the susceptible cultivars Tetila, Wimmera, and the wild type accession were well separated from the resistant cultivars (Safeguard-I and Safeguard-II). This distinct separation grouped all susceptible cultivars into a single cluster, indicating the presence of common metabolites among them. The resistant cultivars Safeguard-I and -II remained separate from others, but both also did not fall in the same cluster, which is consistent and likely due to differences in their establishment periods of 3 and 12 years, respectively. Additionally, it is possible that disease-resistant plants may express a different metabolic profile over time, as plant metabolism is influenced by various factors such as environmental conditions and biotic interactions.37−39

Figure 1.

Figure 1

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PCA score plot for metabolite profiles of different cultivars of annual ryegrass.

The reason why the resistant cultivars (Safeguard-I and Safeguard-II), having establishment periods of 3 and 12 years, respectively, were separated from each other (Table 2), is that we found that certain compounds such as 2,3-bis(acetyloxy)propyl (9E,12E,15E)-9,12,15-octadecatrienoate; fumaric acid, 8-chlorooctyl tridecyl ester; and ergosterol peroxide, were only found in Safeguard-I. However, we could not detect these compounds in Safeguard II. This suggests that over time these compounds might have undergone transformation or degradation into their derivatives. It is possible that the 2,3-bis(acetyloxy)propyl (9E,12E,15E)-9,12,15-octadecatrienoate and ergosterol peroxide (found in Safeguard-I) could have transformed into 9,12,15-octadecatrienoic acid, 2,3-dihydroxypropyl ester, (Z,Z,Z)- and ergosterol peroxide Ac (acetyl) derivative (present in Safeguard-II). This conversion would show a tendency of compounds to adapt to a stable point in nature. This transformation process could explain why these two resistant variations of the same cultivar do not appear closer in Figure 1.

To go into further detail of the relationship between resistance/susceptibility and the metabolites detected, heat mapping and cluster analysis were performed (Figure 2). This analysis showed that resistant and susceptible cultivars clustered independently, illustrating the similarities and dissimilarities in their metabolomic composition. Cross-validation was performed to assess the model’s substantial prediction ability, yielding Q2 values of 0.72, 0.90, and 0.97 with R2 of 0.84, 0.96, and 0.99, for the first, second, and third components, respectively, indicating its predictive ability. It is generally recommended that a Q2 value of ≥0.4 is acceptable for biological models.40 Additionally, PLS-DA with variance 75.8% (PLS component-1 and PLS component-2) represented in Figure S1; sPLS 57.5% variance (PC1 and PC2) and K-means clustering technique were also performed, and the pictorial diagrams are depicted in Figures S2 and S3, respectively. Each analysis split the ryegrass cultivars into ARGT-resistant and nonresistant groups.

Figure 2.

Figure 2

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Heat and cluster mapping of metabolites obtained from five annual ryegrass cultivars. The symbols in each group mean n = 3 biological replicates.

3.3. Differential Metabolite Response in ARGT-Resistant Versus Susceptible Cultivars

The Venn diagram showed differentially expressed metabolites among different data sets for all 108 metabolites identified. The overlapping section of the Venn diagram exhibited 19 metabolites, which were common in all samples. Independent unique metabolites were found in the samples of Safeguard-I (13), Safeguard-II (13), the wild type accession (13), cv Tetila (5), and cv Wimmera (6) (Figure 3). Based on the distribution of different metabolites among the cultivars, it is observed that 31 metabolites were associated with ARGT-resistant cultivars (Safeguard-I and Safeguard-II), and 32 metabolites were present only in susceptible cultivars (Figure 3).

Figure 3.

Figure 3

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Venn diagram of differential metabolites in the inflorescence part of five annual ryegrass cultivars tested.

By analyzing the relative abundance of each chemical group and performing a chi-square test with a significance level of p < 0.001, differentiating between ARGT-resistant and susceptible cultivars is possible (Figure 4), yet the current distinction is derived from tests conducted on only one susceptible and three resistant cultivars. Additionally, testing of other cultivars is necessary to validate these findings. In the resistant group, the most abundant chemical class was esters, which represent 55% of the total chemical composition. Sterols were the second most abundant, at 26%, followed by terpenes (4.5%), siloxane (3.8%), alcohols (2%), phenols (1.3%), heterocyclic (3%), fatty acids (1.9%), hydrocarbons (0.1%), and organofluorine (1.9%). On the other hand, in the susceptible group, the most abundant chemical class was esters, constituting 32% of the total chemical composition. The second most abundant class was aldehydes (29%), followed by sterols (17%), hydrocarbons (8%), terpenes (6.2%), siloxane (1.5%), phenols (0.7%), heterocyclic (0.2%), alcohols (5.7%), and fatty acids (0.2%). Overall, esters were abundant in the resistant group, while aldehydes were more abundant in the nonresistant group.

Figure 4.

Figure 4

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Most significant chemical groups with their relative abundance distinguishing resistant and susceptible. Percentages are the ratios of the peak area of individual chemical group to the total peak area of all chemical groups.

Resistant cultivars contained notably higher concentrations of phenolic compounds which are known to have a role in resistance against plant pathogens like nematodes.41−43 Most of the steroids identified do not have recognized roles in plant growth but play important roles in defense.44 The furan- and pyran-derived heterocyclic compounds are inducers of resistance in plants.45 The role of esters and fatty acids groups contribute to plant resistance by various modalities.46,47

Compounds that show predominance in the resistant and susceptible cultivars were correlated with previous studies. During infection by pathogenic microbes, plants emit a large variety of volatile organic compounds that stimulate immune responses to enhance resistance and perform direct defensive functions, like acting as antioxidants, antibacterial, or antifungal agents, or indirectly signal the activation of the plant’s defensive responses.48 For example, tomato plants that are resistant to Melidogyne incognita infection showed significantly higher levels of plant sterols compared to susceptible plants, while phenolic compounds were found to be associated with resistance to root-knot nematodes in pepper.49,50 Furthermore, the abundance of several long-chain fatty acid molecules was higher in a wheat genotype that was resistant to the root-lesion nematode Pratylenchus thornei compared to a susceptible one.51

Variable importance in projection (VIP) scores were considered for the metabolites mostly responsible for the dissimilarity between resistant and susceptible cultivars in the PLS-DA model. Key resistance-associated metabolites had a VIP score greater than 1.0. Here, nine compounds had VIP scores above 1.0 (Figure 5).52 The presence of ergosterol peroxide in ryegrass was found to be coincidental with ryegrass endophytes,53 and the toxic nature was described in tall fescue infested by endophytes.54,55 Metabolites 9,12,15-octadecatrienoic acid, 2,3-dihydroxypropyl ester, (Z,Z,Z)-, and 2,3-bis(acetyloxy)propyl (9E,12E,15E)-9,12,15-octadecatrienoate may be derived from 9,12,15-octadecatrienoic acid; evidence of its existence under stressed ryegrass and other forage crops was previously reported,56,57 and its antibacterial and nematocidal nature could be a factor in resistance acquisition in ryegrass cultivars.58−60 The ethanolic extract where tricosyl acetate was the major chemical constituent showed a high antioxidant potential,61 suggesting a potential correlation with its efficacy against nematodes. The compound identified in Chenopodium album was 2-chloropropionic acid hexadecyl ester, which is closely related to 2-chloropropionic acid octadecyl ester. This compound exhibited significant antibacterial and antifungal properties,59 indicating its antimicrobial nature. It could be a crucial component potentially effective against A. funesta, although further research is warranted for confirmation. There are no previous reports of methyl nomilinate in ryegrass, but it is common in the citrus family, exhibiting strong cytotoxicity.62 Identification of these metabolites in ryegrass may serve as markers for ARGT-resistance and provide a basis for further research into prevention of nematode and/or bacteria infecting ryegrass. However, it is important to exercise caution in drawing conclusions as this study only examined three cultivars, and a more comprehensive analysis involving a greater number of genotypes is essential for a thorough understanding.

Figure 5.

Figure 5

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VIP scores indicating the top nine important metabolites identified by PLS-DA contributing to the separation of metabolic profiles in resistant and susceptible annual ryegrass (Lolium rigidum) cultivars. The colored box on the right indicates the relative concentration of corresponding metabolite in resistant and susceptible groups.

The overall advancement of coupling DI-SPME with GC–MS has proven invaluable in obtaining the metabolic profiles of different ryegrass cultivars. This metabolite profiling has provided significant biomarkers that may be responsive to nematodes and bacteria associated with ARGT. Furthermore, insights into the temporal transformation of metabolite compositions in resistant cultivars have been derived. These findings lay the foundation for future strategies to enhance plant disease resistance to minimize ARGT.

Glossary

Abbreviations

ARGT

annual ryegrass toxicity

VOCs

volatile organic compounds

HPLC

high-performance liquid chromatography

RI

retention index

DI-SPME

direct immersion solid-phase microextraction

GC–MS

gas chromatography–mass spectrometry

VIP

variable importance in projection

PCA

principal component analysis

PLS-DA

partial least-squares-discriminant analysis

sPLS

sparse partial least-squares

ANOVA

analysis of variance

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jafc.4c03986.

  • List of all 108 metabolites identified by SPME-GC–MS analysis of ryegrass cultivars and pictorial diagrams for PLS-DA, sPLS, and K-means clustering technique (PDF)

Author Contributions

Conceptualization: P.K., S.J.M., Y.R., M.A.; data curation: P.K.; methodology: P.K., Y.R., M.A., D.K., X.D.; resources: P.K., Y.R., D.K., S.M., X.D.; supervision: S.J.M., Y.R., M.A., D.K.; validation: P.K., Y.R., S.J.M. S.M.; writing-original draft: P.K.; writing-review and editing: P.K., S.J.M., Y.R., M.A., D.K., S.M. All authors have read and agreed to the published version of the manuscript.

The authors declare no competing financial interest.

Supplementary Material

jf4c03986_si_001.pdf (264.4KB, pdf)

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