Comparative metabolite profiling reveals differential metabolic responses of guava roots to single and sequential infections by Meloidogyne enterolobii and Fusarium spp.

Introduction

Guava is one of the most important horticultural crops in India. Native to South America, it was introduced to India in the 17th century by the Portuguese. This fruit belongs to the Myrtaceae family and is characterised by short evergreen shrubby trees. The branches are wide-spreading and twisted, featuring square, fleece-covered twigs. The leaves are dark green, tough, simple, elliptical to ovate, and arranged oppositely, measuring 5-15 cm in length. The flowers consist of five white petals and numerous needle-like white stamens. The fruits are many-seeded berries. Unripe fruits are small and vary in shape, appearing ovoid, round, or pear-like, and generally measuring 3-6 cm long (Morton, 1987) [12] . The skin of the fruit can vary in taste from severely unpleasant to sweet. The skin colour changes from unripe to ripe fruits, typically starting out green before turning yellow, maroon, or remaining green. The pulp of the fruit also varies in colour, ranging from yellow and pink to red, depending on the species (Gupta et al. 2018) [7] . It is also considered a poor man's apple.

Fruit production in India has increased from 76.42 million Tonnes to 102.48 million tonnes between 2011-12 and 2020-21. The total horticultural crop production in 2020-21 was highest in the state of Uttar Pradesh (408.13 lakh tonnes), followed by West Bengal (348.62 lakh tonnes) and Madhya Pradesh (337.04 lakh tonnes). In the same year, the production of fruits was highest in the state of Andhra Pradesh (177.08 lakh tonnes), followed by Maharashtra (117.38 lakh tonnes) and Uttar Pradesh (112.31 lakh tonnes). India is the largest producer of guava, contributing 45% of global production. A significant portion, approximately 25 million tonnes, is harvested annually, followed by China and Indonesia. The area under guava production has increased from 265 hectares in 2017-18 to 308 hectares in 2020-21. The output of guava has also risen from 4054 tonnes in 2017-18 to 4582 tonnes in 2020-21. According to statistics for 2020-21, the state with the highest guava production in India was Uttar Pradesh (983.06 tonnes) with a productivity of 18.82 tonnes per hectare, followed by Madhya Pradesh (875.66 tonnes) with a productivity of 19.01 tonnes per hectare, Bihar (434.41 tonnes) with a productivity of 14.58 tonnes per hectare, and Andhra Pradesh (330.51 tonnes) with a productivity of 23.00 tonnes per hectare (Horticultural Statistics at a Glance, 2022, MoA&FW, GoI).

Materials and Methods

Quantification of secondary metabolites in guava roots and their relation with the disease

A study was conducted to quantify the secondary metabolites and to determine their association with the disease. Three-month-old guava plants were inoculated individually or in combination with the isolated and cultured Meloidogyne spp. and Fusarium spp. in sterilized soil.

Procedure of infecting the guava plants

The nematode suspension containing Meloidogyne juveniles was inoculated (1000 J2) on week-old transplanted guava plants by creating three holes in the soil near the root zone. After the suspension was poured, the holes were covered with soil. The colonized wheat seeds were prepared using isolated Fusarium spp. and incorporated into sterilized soil at a ratio of 10 g/kg (Gomes et al., 2013) [5] one week prior to transplanting. This allowed the mycelia to disperse within the soil during incubation. Two slits were made in the collar region of the plant during transplantation, as the fungus requires an injury to facilitate entry into the plant. Inoculating the Meloidogyne spp. and the Fusarium spp. isolated from the same guava plant as described in the aforementioned procedures will induce a complex disease. A control, consisting of a guava plant that was not inoculated, was maintained. All treatments were conducted under greenhouse conditions for 90 days. Each treatment was replicated three times (Table 1).

Collection and analysis of secondary metabolites

Guava plants were carefully uprooted 90 days after inoculation. The roots were thoroughly washed, and the secondary metabolites were extracted using the protocol described by Schliemann et al. (2008) [13] , and subsequently sent for GC-MS analysis. The results obtained were analyzed regarding the concentration of secondary metabolites present in each treatment, variations in the secondary metabolites produced across different treatments, and the potential role of these metabolites in relation to the disease, if applicable.

Result 3.1 GC-MS profiling of secondary metabolites in guava roots under different pathogen treatments

Gas chromatography-mass spectrometry (GC-MS) analysis showed clear differences in the metabolite composition of guava roots among various inoculation treatments (I1-I8). This suggests that infection with Meloidogyne enterolobii, Fusarium spp., or both significantly changed the root metabolite profile. Most of the identified compounds were fatty acids, fatty acid esters, hydrocarbons, phytosterols, terpenoids, alkanes, and phthalate derivatives. In treatment I1 (M. enterolobii alone), the primary metabolite identified was 13-docosenamide (Z)-, also known as erucamide, which constituted 80.35% of the total metabolite profile. Additional metabolites were detected (Table 2) at lower levels included octadecanoic acid (9.17%), 9-octadecenoic acid (oleic acid) (6.33%), hexadecanoic acid (1.92%), 10-heneicosene (1.65%), and 6heptenoic acid, ethyl ester (0.96%). In treatment I2 (M. enterolobii + Fusarium oxysporum), the metabolite profile showed significantly greater diversity. Hexadecanoic acid was the most abundant metabolite (18.31%, followed by γ-sitosterol (16.35%) and 9octadecenoic acid (oleic acid) (16.25%). Other major metabolites included bis(2-ethylhexyl) phthalate (9.20%), methyl 9-cis,11-trans-octadecadienoate (6.60%), ethyl oleate (5.82%), methyl stearate (4.61%), linoleic acid ethyl ester (3.70%), methyl oleate (3.29%), along with several minor metabolites below 3% (Table 2). Treatment I3 (M. enterolobii + Fusarium incarnatum) was predominantly characterized by bis(2-ethylhexyl) phthalate, constituting 84.60% of the total detected metabolites (Table 2). The remaining compounds were present only in trace amounts, including 13-docosenamide (erucamide) (3.19%), hexadecanoic acid (1.52%), ethyl oleate (1.49%), methyl stearate (1.45%), linoleic acid ethyl ester (1.20%), and methyl oleate (1.03%). In treatment I4 (M. enterolobii + Fusarium solani), the primary metabolite was 19-hydroxy-13-epimanoyl oxide, which accounted for 25.69% of the total (Table 2). Other notable metabolites included squalene (13.16%), hexadecanoic acid (9.21%), dodecane (7.10%), bis(2ethylhexyl) phthalate (5.04%), hexadecane (4.89%), along with several metabolites in smaller amounts. Among the plants inoculated with Fusarium oxysporum alone (I5), γ-sitosterol emerged as the most prevalent metabolite (16.16%), followed by methyl palmitate (9.30%), 2-monopalmitin (6.99%), 13-docosenamide (erucamide) (5.23%), ethyl palmitate (4.72%), methyl linoleate (4.24%), 11-octadecenoic acid methyl ester (4.12%), ethyl stearate (3.79%) and several other fatty acid derivatives were present at levels below 3% (Table 2). Treatment I6 (Fusarium incarnatum alone) primarily contained γ-sitosterol (19.36%), with significant amounts of methyl palmitate (10.69%), 2-monopalmitin (7.45%), methyl linoleate (4.93%), ethyl palmitate (3.68%), methyl oleate (3.34%), linoleic acid ethyl ester (3.23%), 11octadecenoic acid methyl ester (2.88%), and eicosanoic acid (2.74%) (Table 2).

In treatment I7 Fusarium solani alone, the metabolite profile was significantly different from those of the other Fusarium treatments (Table 2). The main metabolites were γ-sitosterol (18.89%), eicosane (17.76%), and bis(2-ethylhexyl) phthalate (16.18%). Other detected compounds included methyl palmitate (8.37%), heptacosane (4.94%), tetracosane (4.44%), linoleic acid (4.13%), 5-hydroxy-2methylthiopyrimidine (3.55%), methyl cis-9,trans-11linoleate (3.03%), along with small amounts of additional metabolites. The control plants (I8) showed a relatively simple metabolite profile, primarily consisting of γ-sitosterol (21.86%). Other significant compounds included hexadecanoic acid methyl ester (9.13%), 2-monopalmitin (6.43%), ethanol (5.67%), methyl 9-cis,11-transoctadecadienoate (4.17%), ethyl palmitate (3.96%), linoleic acid ethyl ester (3.47%), methyl oleate (2.56%), and methyl cis-9,trans-11-linoleate (2.35%) (Table 2). The GC-MS analysis revealed significant variation in metabolite profiles depending on the treatment. Combined pathogen inoculations led to the emergence of several unique metabolites, including bis(2-ethylhexyl) phthalate, 19-hydroxy-13-epimanoyl oxide, and squalene. In contrast, Fusarium-only treatments mainly showed phytosterols and fatty acid derivatives. Control plants had a simpler metabolite profile, mainly containing phytosterols and typical fatty acid esters.

Multivariate statistical analysis of GC-MS metabolites

To gain a more comprehensive understanding of how various pathogen treatments influence metabolite variations, the GC-MS dataset was subjected to analyses including hierarchical clustering, principal component analysis (PCA), and Random Forest classification via metaboanalysis. The hierarchical clustering heat map (Fig 1 ) shows the abundance patterns of metabolites found in guava roots under various pathogen inoculation treatments. Each column corresponds to a biological replicate (R1-R3) for a specific treatment, and each row indicates a metabolite. The colour scale varies from blue, indicating low relative abundance, to red, indicating high relative abundance. The three replicates of M. enterolobii combined with F. oxysporum (I2) formed a distinct cluster, clearly separated from other treatments, indicating a unique metabolic profile for this infection. Similarly, the replicates of M. enterolobii followed by F. solani (I4) clustered together and were distinctly different from the other inoculation treatments. The samples of F. oxysporum alone (I5), F. incarnatum alone (I6), F. solani alone (I7), and the control (I8) clustered more closely, suggesting they have more similar metabolite profiles compared to the combined inoculation treatments. Combined inoculation treatments, especially M. enterolobii followed by F. oxysporum (I2) and M. enterolobii followed by F. solani (I4), showed the most significant changes in metabolite levels compared to the control and singlepathogen treatments. The tight clustering of biological replicates confirms the high reproducibility of the GC-MS data. Additionally, the observed variation in fatty acids, sterols, hydrocarbons, terpenoids, and other lipid-derived metabolites indicates a considerable metabolic reprogramming triggered by nematode and fungal infections.

The PCA biplot and loading plot (Fig 3,4,5) showed distinct differences in the metabolite profiles of guava roots depending on the treatment. The first two principal components accounted for 59.9% of the overall variation, with PC1 explaining 34.3% and PC2 25.6% (Fig 2). The combined inoculation with Meloidogyne enterolobii and Fusarium oxysporum (I2) was clearly separated along the positive PC1 axis and was linked to higher levels of hexadecanoic acid, oleic acid, ethyl oleate, bis(2-ethylhexyl) phthalate, and 13-docosenamide, indicating notable metabolic changes. Conversely, the treatment with M. enterolobii followed by F. solani (I4) mainly separated along the positive PC2 axis and was associated with increased dodecane, squalene, hexadecane, and 19-hydroxy-13-epimanoyl oxide. The control and single-pathogen treatments grouped together on the negative side of PC1, implying similar metabolite profiles. The loading plot confirmed that fatty acids, esters, phytosterols, hydrocarbons, and terpenoid metabolites were primary factors driving the differences among treatments. The Random Forest analysis showed strong classification performance, with the overall error stabilizing around 8%, suggesting that the GC-MS metabolite profiles effectively distinguish among various pathogen treatments. The model identified 13-docosenamide (erucamide), bis(2-ethylhexyl) phthalate, hexadecanoic acid, γ-sitosterol, octadecanoic acid, 2,4-di-tert-butylphenol, ethyl oleate, linoleic acid ethyl ester, methyl linoleate, and dodecane as key metabolites driving the treatment classification. The importance heat map highlighted clear (Fig 6 ), treatment-specific abundance patterns for these metabolites, supporting their potential as biomarkers of pathogen-induced metabolic responses. Outlier analysis showed (Fig 7) high consistency across most biological replicates, though a few samples, especially I6_R2, showed higher outlying measures. Overall, the Random Forest model confirmed that the metabolite profiles contain sufficient discriminatory information to reliably distinguish healthy plants from those affected by various nematode and fungal infections. The combined application of hierarchical clustering, principal component analysis (PCA), and Random Forest classification provided an integrated assessment of pathogen-induced metabolic reprogramming in guava roots and enabled the comparative evaluation of treatments summarized in Table 3. All three multivariate approaches consistently demonstrated that metabolite profiles differed according to the inoculation treatment, with the sequential inoculations involving Meloidogyne enterolobii and Fusarium spp. exhibiting the greatest divergence from the healthy control. In particular, the M. enterolobii-F. oxysporum (I2) and M. enterolobii-F. solani (I4) treatments showed the most pronounced metabolic alterations, characterized by distinct clustering patterns, clear separation in PCA ordination space, and high classification accuracy in the Random Forest model. Conversely, single-pathogen inoculations produced comparatively smaller metabolic shifts and clustered more closely with the control. Based on the concordant outcomes of these complementary analyses, the treatments were comparatively ranked according to their overall metabolic response, providing a concise summary of the relative extent of pathogen-induced metabolic alterations in guava roots (Table 3). [2,14] . The higher levels of hexadecanoic acid and its esters in combined infections support reports suggesting that pathogen invasion stimulates membrane lipid remodeling and fatty acid metabolism, which are crucial for managing oxidative stress and antimicrobial defense (Ghfil et al., 2025) [4] . Likewise, the presence of γ-sitosterol both in healthy and Fusarium-infected plants points to membrane stability under stress. Changes in sterol makeup are known to influence plant susceptibility and defense signaling during pathogen attack (Griebel & Zeier, 2010) [6] . The detection of squalene, especially in the M. enterolobii + F. solani treatment, further indicates activation of terpenoid biosynthesis, as squalene plays a key role as an intermediate in sterol production and is linked to antioxidant activity and stress resilience during biotic challenges (Lozano-Grande et al., 2018; Jiang et al., 2015) [9,11] .

A key finding of this study was the significant accumulation of DEHP in treatments with Fusarium, especially M. enterolobii, followed by F. incarnatum, where DEHP was the main metabolite. Recent research shows that Fusarium oxysporum can produce DEHP as a bioactive secondary metabolite, suggesting that the elevated DEHP levels observed may result from fungal metabolism in addition to host responses (Amr et al., 2025) [1] . Additionally, phthalate derivatives like DEHP and diisooctyl phthalate have been reported to support plant defense by reducing Fusarium wilt, enhancing antioxidant activity, and modifying rhizosphere microbial communities (Li et al., 2024; Yang et al., 2024) [16] . The detection of erucamide in nematode-infected roots is also significant, as recent studies identify it as a conserved plant-derived metabolite that can suppress pathogen virulence by inhibiting the bacterial type III secretion system, indicating its role in induced plant defense (Wang et al., 2025; Ikram et al., 2025) [8,15] . Overall, these findings show that co-infections by nematodes and fungi cause more substantial metabolic changes than single infections. They highlight fatty acids, phytosterols, terpenoids, hydrocarbons, and phthalate derivatives as key biochemical markers of pathogen-induced metabolic reprogramming in guava roots, with potential as biomarkers for understanding hostpathogen interactions and guiding disease management strategies.

Conclusion

This study demonstrated that infection by Meloidogyne enterolobii and Fusarium spp. induces distinct and treatment-specific metabolic reprogramming in guava roots. Sequential inoculation, particularly M. enterolobii followed by Fusarium oxysporum (I2), caused the greatest metabolic disturbance, followed by M. enterolobii + F. solani (I4), whereas single-pathogen infections produced comparatively moderate alterations. GC-MS combined with multivariate analyses consistently identified fatty acids, phytosterols, terpenoids, hydrocarbons, and phthalate derivatives as key metabolites associated with disease progression. These findings improve the understanding of the biochemical basis of guava decline and identify potential metabolite biomarkers that may facilitate early disease diagnosis, hostpathogen interaction studies, and the development of effective disease management strategies.

Acknowledgement

AcknowledgmentsThe authors acknowledge the Department of Plant Pathology, College of Agriculture, Govind Ballabh Pant University of Agriculture and Technology (GBPUAT), Pantnagar, Uttarakhand, India, for providing the facilities and research support for this work. The authors also acknowledge IICT, Hyderabad, Telangana for providing GC-MS analytical facilities. This study forms part of the Ph.D. research of the first author.

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