# Fruitlet Core Rot disease in ‘Queen Victoria’ pineapple infructescence triggers local and systemic metabolome reconfigurations

Lorraine Latchoumane, Bastien Barral, Olivier Chevallier, Jérôme Minier, Fabrice Davrieux, Marc Chillet, Raphaël Lugan

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Lorraine Latchoumane, Bastien Barral, Olivier Chevallier, Jérôme Minier, Fabrice Davrieux, et al. Fruitlet Core Rot disease in ‘Queen Victoria’ pineapple infructescence triggers local and systemic metabolome reconfigurations. Postharvest Biology and Technology, 2025, 226, pp.113561. 10.1016/j.postharvbio.2025.113561. hal-05034379

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# Fruitlet Core Rot disease in ‘Queen Victoria’ pineapple infructescence triggers local and systemic metabolome reconfigurations

Lorraine Latchoumane<sup>a,b,c,d</sup>, Bastien Barral<sup>a,b</sup>, Olivier Chevallier<sup>e,f</sup>, Jérôme Minier<sup>a,b</sup>, Fabrice Davrieux<sup>a,b</sup>, Marc Chillet<sup>a,b,*</sup>, Raphaël Lugan<sup>b</sup>

## ARTICLE INFO

### Keywords:

Fusarium

Metabolomics

GC-MS

LC-MS

Systemic acquired resistance

Pipecolic acid

## ABSTRACT

We studied the metabolomes of 'Queen Victoria' pineapples subjected to the fungi-induced fruitlet core rot (FCR) disease, a major quality issue in the pineapple industry. Analyses were carried out on the pulp and skin of individual fruitlets within healthy or black spotted infructescences, in order to profile three types of samples: healthy, infected and asymptomatic fruitlets. Our results reveal distinct responses to FCR in pulp and skin of infected fruitlets, both quantitatively and qualitatively. The pulp displayed much stronger and diverse changes, including the implementation of a local phenolic-based defense reflecting both antimicrobial activity and cell wall turnover. Evidence of strong redox regulation linked to the presence of the pathogen was observed through variations in proline and glutathione-conjugated compounds. Moreover, among the numerous sugar variations, a particular trehalose pattern emerged as an antagonistic issue between the plant and the fungus. The activation of signaling pathways following the fungal attack was also revealed, with the accumulation of pipecolate and alpha-aminobutyrate, involved in defense priming and systemic acquired resistance. Interestingly, we also found significant metabolic changes in asymptomatic fruitlets, similar in nature but smaller in magnitude, demonstrating the existence of a systemic response to infection. This work opens the way to a better understanding of the infection and defense mechanisms involved in FCR and their consequences on organoleptic quality.

## 1. Introduction

The pineapple 'Queen Victoria' (Ananas comosus (L.) Merr.) is one of the major crops of Reunion Island, where its production is of high socio-economic importance as it is exported to the european markets (CBI Ministry of Foreign Affairs, 2022; FAO, 2022). However, this cultivar is particularly susceptible to fungal pathogens responsible for fruitlet core rot (FCR) disease (Jacobs et al., 2010; Barral et al., 2019b, 2020), which restricts its compliance with commercial quality standards.

The pathogenic fungi involved in FCR disease, such as Fusarium sp., Penicillium sp. and Talaromyces sp. (Barral et al., 2020) act as necrotroph or hemibiotroph, depending on the species, and have the particularity of remaining latent after contamination at pre-flowering stage before propagating during fruit ripening. As the pineapple ripens, the fungal

infection, initiated by a single pathogen species or a multi-partite pathosystem (Vignassa et al., 2021), causes local necrotic lesions in the pulp of the fruit, while exhibiting no externally visible symptoms (Tryon, 1898; Guérout, 1974; Rohrbach and Pfeiffer, 1976). This represents a considerable challenge for the pineapple industry, as internal fruit necrosis tends to accelerate after harvest, especially during storage and transport. The specific morphology of the pineapple fruit, an infructescence, i.e. an ensemble of single fruit called fruitlets, raises questions about the events triggered in the different fruitlets. Indeed, molecular analysis demonstrated that not all fruitlets are affected by mycelial growth leading to FCR disease although the presence of one or more pathogens was noticed in each fruitlet (Hoarau et al., 2021).

The immune reaction of plants to biotic attacks proceeds from general mechanisms involving molecular systems of detection and

<footnote><sup>a</sup> CIRAD, UMR Qualisud, Saint-Pierre, Réunion, France</footnote>

<footnote><sup>b</sup> Qualisud, Univ Montpellier, Avignon Université, CIRAD, Institut Agro, IRD, Université de La Réunion, Montpellier, France</footnote>

<footnote><sup>c</sup> ITAP, Univ Montpellier, INRAE, Institut Agro, Montpellier, France</footnote>

<footnote><sup>d</sup> AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France</footnote>

<footnote><sup>e</sup> DARI, Plateforme 3A, Avignon Université, Avignon, France</footnote>

<footnote><sup>f</sup> Agilent Technologies Inc, Santa Clara, CA, USA</footnote>

<footnote>* Corresponding author at: CIRAD, UMR Qualisud, Saint-Pierre, Réunion, France
E-mail address: marc.chillet@cirad.fr (M. Chillet).</footnote>
recognition of the aggressors and the coordinated implementation of
adapted responses. The perception of aggressions follows a scheme
theorized by Jones and Dangl (2006). Depending on the nature of the
aggression, signaling cascades orchestrated by relevant hormonal sig-
nals trigger a range of reactions both locally, such as the hypersensitive
response (HR), and remotely, including the systemic acquired resistance
(SAR) and the induced systemic resistance (ISR) (Choudhary et al.,
2007; Jeblick et al., 2022).

Metabolomics has proven its relevance in the study of metabolic
stress responses in plants (Allwood et al., 2008; Arbona and
Gómez-Cadenas, 2016), in particular to highlight the metabolic mech-
anisms implicated in resistance to fungal pathogens (Ballester et al.,
2013; Luna et al., 2020; Duan et al., 2022). The metabolic responses to
FCR disease in pineapple was explored for the first time by Barral et al.
(2017, 2019b), by comparing the fruit metabolic profiles of the resistant
cultivar 'MD-2' and the susceptible 'Queen Victoria' inoculated with the
pathogen Fusarium ananatum, one of the main causal agents of FCR
disease in Reunion Island (Barral et al., 2021). The disease provoked an
accumulation of phenolic compounds, notably coumaroylisocitrate and
caffeoylisocitrate, which were more abundant in 'MD-2'. In addition,
'MD-2' exhibited higher constitutive levels of hydroxycinnamic and
hydroxybenzoic acids in healthy pineapples. These findings suggested
probable antifungal properties of these compounds and their implication
in pineapple resistance against fungal attack during early fruit devel-
opment. Both the infection mechanisms and host responses remain
poorly understood at the molecular level and need to be elucidated,
particularly under natural infection conditions.

In this study, we investigated the metabolic changes induced by FCR
disease in 'Queen Victoria' pineapple tissues. Unlike Barral et al.
(2019b), who focused solely on the pulp metabolome of inoculated
pineapples, we worked on both pulp and skin metabolomes from natu-
rally infected fruit in the field. We also explored a potential systemic
effect of infection by comparing the metabolome of healthy fruitlets
from fully healthy pineapples with that of asymptomatic fruitlets from
pineapples bearing infected fruitlets. This article participates to a
greater comprehension of FCR infection and opens possibilities for
managing this post-harvest disease.

## 2. Materials and methods

2.1. *Fruit samples*

This study was conducted using ‘Queen Victoria’ cultivar of pine-
apple fruit (Ananas comosus (L.) Merr.) cultivated in Reunion Island.
Fruits were grown using the same conventional cultural techniques and
collected at similar phenological stage to ensure homogeneity of anal-
ysis, i.e. the C3 stage, characterized by a three quarters basal yellow
coloration of fruit skin, corresponding to pineapple fruits close to full
maturity. All pineapples were collected on December 2020, approxi-
mately seven months after flower induction treatment and eleven weeks
after the initiation of fruit formation. After harvest, pineapples were
stored in climatic chamber set at 20°C before sampling. Pineapples were
removed from the climatic chamber and sampled one at a time to limit
fruit temperature variations and prevent related metabolic changes.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Schematic representation of healthy (A) and FCR-infected (B) 'Queen Victoria' pineapples. HS: healthy skin, AS, asymptomatic skin, IS: infected skin, HP: healthy pulp, AP: asymptomatic pulp, IP: infected pulp.</figcaption></figure>
Fruitlet classification relied on visual characterization after pineapple opening, determining the presence or absence of naturally FCR-infected fruitlets, which have occurred throughout the fruit's growth period. Sampling was realized on six pineapples for each of the following three sample classes: (i) healthy fruitlets from pineapples entirely free of necrosis ("Healthy"), (ii) necrotic fruitlets from naturally infected pineapples ("Infected"), and (iii) non-necrotic fruitlets from pineapples containing at least one infected fruitlet ("Asymptomatic") (Fig. 1). For healthy samples, fruitlets were randomly collected from pineapples showing no signs of necrosis. For infected samples, fruitlets exhibiting necrosis were collected, with most of the surrounding non-necrotic pulp removed. A fruitlet was classified as asymptomatic if it showed no necrosis while being located within a pineapple that contained infected fruitlets. These asymptomatic fruitlets were collected from areas directly adjacent to infected ones, ensuring that an equivalent amount of material was sampled. Six randomly selected fruitlets located in the lower part of the pineapple were sampled by cutting sections of approximately 1.5 cm² from both the skin and the underlying pulp. The samples were quickly immersed in liquid nitrogen after separating the pulp from the skin and ground using an IKA A11 grinder (IKA, China) after freeze-drying before storage at -20 °C until analysis. Metabolomic analysis was performed on the 36 samples: six healthy pulp (HP), six asymptomatic pulp (AP), six infected pulp (IP), six healthy skin (HS), six asymptomatic skin (AS) and six infected skin (IS).

## 2.2. Sample extraction

Solvents were purchased from Honeywell (Charlotte, USA) and Thermo Fisher Scientific (Wilmington, USA) and ultra-pure water was obtained from a Milli-Q system (Merck, Darmstadt, Germany). Forty mg of lyophilized fruit powder was extracted for 15 min at 70 °C under constant stirring with 1.8 mL of a methanol/water (80:20, v/v) solution containing 100 µM of ribitol as internal standard. Volume was adjusted to keep the same ratio mass to volume in every sample. Samples were centrifuged 2 min at 9 000 g and 4 °C and the supernatants containing polar metabolites were filtered then stocked at -20 °C in an Eppendorf tube before analysis (0.22 µm filters Millex-Lg, PTFE hydrophilic, 4 mm, Sigma Aldrich, Merck, Darmstadt, Germany). 0.5 mL and 1.2 mL of extracts were transferred to amber vials and evaporated to dryness overnight without heating under vacuum concentrator before GC-MS and LC-MS analysis, respectively.

## 2.3. GC-MS analysis

The method was adapted from Roessner et al. (2000). Samples were derivatized online before injection with a MultiPurpose Sampler (Gerstel MPS, CTC Analytics AG, Mülheim an der Ruhr, Switzerland): dry extracts were incubated in 50 µL of a pyridine solution containing 20 g L⁻¹ of methoxyamine hydrochloride under constant shaking at 80 °C for 90 min. Then, 80 µL of BSTFA containing a mixture of nine n-alkanes were added before heating for 30 min at 80 °C under constant shaking. Data acquisition was performed with a gas chromatograph system (7890B GC, Agilent Technologies, Santa Clara, CA, USA) equipped with a capillary column (ZB-SemiVolatiles, 34.59 m, internal diameter 250 µm, film thickness 250 µm, Phenomenex, Torrance, USA) hyphenated to a time-of-flight (TOF) mass spectrometer (Pegasus BT, Leco, Saint Joseph, Benton Harbor, MI, USA). One mL of sample was injected in splitless mode at 230 °C. Helium was used as carrier gas at 0.6 mL min⁻¹. The initial oven temperature was kept at 70 °C for 1 min and then increased to 320 °C (9 °C min⁻¹) and maintained for 10 min. The *m/z* scan range was 70–600 with a cycle time of 20 scans s⁻¹. Source temperature and transfer line were set at 250 °C. The MultiPurpose Sampler was controlled by Maestro Version 1.4.40.1. Gerstel and gas chromatography system with mass spectrometer were controlled by ChromaTOF Version 5.20.38.0.54864 (LECO, Saint Joseph, MI, USA). GC–EI–TOFMS data were deconvoluted with the LECO NTD software

(LECO, St. Joseph, MI, USA), then the peak list was curated manually to remove incorrectly deconvoluted peaks and contaminants. Peak annotation was based on spectral and retention index (RI) similarity using mass spectral libraries (Golm database, NIST 2014, Leco-fiehn rtx5). A specific extracted ion chromatogram (XIC) was chosen for each molecule for peak integration.

Peak extraction, alignment and identification were performed using ChromaTOF® software. Compounds were tentatively identified based on the Golm metabolome database (Kopka et al., 2005) and in-house spectral database, by comparing registered and actual retention indices (RI) and mass spectra. Metabolites were annotated with confidence levels described by Schymanski et al. (2014): level 1, confirmed structure by comparison with authentic standard, with spectral reverse match > 850 and retention index (RI) gap < 1%; level 2, probable structure according to database screening, with reverse match > 800 and RI gap < 1%; level 3, tentative candidate or molecular family according to database screening, with reverse match > 600 and RI gap > 1%. The 60 level 3 metabolites were annotated with the name of the metabolite with the closest match preceded with “U_”, standing for “Unknown”.

## 2.4. LC-MS analysis

Dried extracts were diluted in 0.4 mL of ultra-pure water. Analyses were performed with an Acquity I-Class UPLC system (Waters, Milford, MA) hyphenated to a Synapt G2-Si quadrupole time-of-flight (QTOF) mass spectrometer (Waters, Milford, MA) equipped with an electrospray ionization source (ESI). Chromatographic separation was achieved using a HSS T3 column (100 × 2.1 mm, 1.8 µm Waters, Milford, MA). The mobile phase consisted of water (A) and methanol (B), both containing 0.1 % formic acid. Two mL of sample was injected before running the solvent gradient: 1 % B for 1.5 min, then a linear gradient to 99 % B in 15 min followed by 2.25 min at 99 % B and then back to initial conditions in 1.25 min (total run time 20 min). The column was maintained at 45 °C with a flow rate of 0.4 mL min⁻¹. The source temperature was set to 120 °C with the desolvation temperature at 450 °C. The capillary voltage was set to 0.8 kV for positive mode and 2.0 kV for negative mode and the cone voltage to 40 V for positive and negative mode. Nitrogen was used as the drying and nebulizing gas, with 50 L h⁻¹ cone gas flow and 650 L h⁻¹ desolvation gas flow. Analyses were performed with triplicate injections, in negative and positive ionization modes using High Resolution mode. Data independent acquisitions were performed simultaneously in MS and MS/MS modes (MS⁶, continuum), with a collision energy ramp from 15 V to 30 V. Mass spectra were recorded at 0.1 s per scan from 50 to 1200 *m/z*. Samples were injected twice, in positive and negative ionization modes. Chromatograms from negative mode acquisition were scanned to extract the major compounds. All metabolites were identified with a confidence level 3 (Schymanski et al., 2014), by a manual examination of MS and MS/MS spectra in positive and negative modes and spectral comparison with in-house and public databases (PubChem, HMDB, ChemSpider) and literature.

## 2.5. Data analysis

Chromatogram peak deconvolution and manual correction of peak area were performed on both GC-MS and LC-MS raw data with the TargetLynx module of MassLynx V4.2 software (Waters, Milford, MA). Then peak areas were normalized against the internal standard (GC-MS only) and sample mass to provide semi-quantitative data.

Nonparametric multivariate analyses were performed on data pre-treated using logarithmic (log10) transformation and Pareto scaling (mean-centered and then divided by the square root of the standard deviation). Principal component analysis (PCA) was conducted using non-linear iterative partial least squares (NIPALS) algorithm (Wold, 1973). Outliers were removed based on diagnostic plot of score distance (SD) and orthogonal distance (OD) of each sample (Hubert et al., 2005),
resulting in a final dataset containing 32 fruitlet samples (4 HP, 6 AP, 5 IP, 5 HS, 6 AS and 6 IS). A Kruskal-Wallis test (one-way analysis of variance) (Kruskal and Wallis, 1952) followed by a Conover-Iman post-hoc test (pairwise multiple comparison) (Conover and Iman, 1979) was used to determine significant differences between sample classes (p-value < 0.05). Then hierarchical clustering analyses (HCA) were performed on the subsets of metabolites that exhibited significantly different variation among sample classes, using the Pearson correlation and the Ward algorithm. These statistics were performed with R Statistical Software (v4.1.1; R Core Team 2021) using the *ropls* R package (v1.25.0; Thévenot et al., 2015) for PCA and the *pheatmap* R package (v1.0.12; Kolde, 2019) for HCA.

# 3. Results

## 3.1. Overall framework of the dataset

LC-MS analysis permitted the putative identification of 52 compounds with a confidence level 3 (Supplementary Table S1). GC-MS analysis allowed the tentative identification of 109 compounds, including 33 compounds identified with a confidence level 1, 16 with a confidence level 2 and 60 with a confidence level 3 (Supplementary Table S2). A total of 161 compounds were thus annotated from pulp or skin pineapple samples, including 18 amino acids, 61 carbohydrates, 6 polyols, 1 amino alcohol, 2 fatty acids, 20 organic acids, 50 polyphenols, 1 phytohormone and 2 others (Supplementary Table S3).

A PCA was performed on the whole dataset (Fig. 2). The first principal component (PC1), accounting for 33.5 % of the total variance separated pulp samples collected from infected fruitlets from all the other samples. A PCA performed on pulp samples only achieved a clear separation of samples collected from infected fruitlets along PC1 (51.5 % of total variance) and a less pronounced separation of asymptomatic fruitlets from healthy ones along the same axis (Fig. 3A). Infection was notably associated with higher content in hydroxycinnamic acid and derivatives (Fig. 3B). On the contrary, a PCA performed on skin samples only could not achieve any distinct separation based on the infection status (Figs. 3C and 3D).

Statistical analysis confirmed the greater impact of FCR disease on the pulp metabolome than on the skin metabolome by revealing 92 metabolites in pulp and 27 in skin samples displaying significantly different contents in healthy, infected and asymptomatic fruitlets

(Supplementary Tables S4 to S9). This contrasting response between pineapple tissues following infection is also illustrated by Venn diagrams in Supplementary Figure S1A.

## 3.2. Effects of FCR disease on pulp metabolome

A hierarchical clustering was performed on the subset of 92 metabolites exhibiting significant differences (Kruskal-Wallis, p-value < 0.05) related to the infection status in pulp samples (Fig. 4). All samples clustered consistently with their infection status. Infected samples clustered appart from asymptomatic and healthy samples, confirming that infection impacted the metabolome of asymptomatic fruitlets, but to a much lesser extent than that of infected fruitlets. Metabolites segregated into two main clusters: the clusters 1 and 2 gathered compounds with respectively increased and decreased levels upon infection. The clustering of compounds noticeably relied on strong correlations between structurally related metabolites such as isomers or members of chemical famillies: for instance isomers of sinapoyl hexoside, hydroxycinnamic acids conjugated to isocitrate or glycerol, and oligosaccharides annotated with low confidence (U_maltotriose_1, U_raffinose_1, U_sucrose_3), indicating that the disease shifted the metabolism not only in very delimited branches but also toward the activation of entire pathways. These variations are shown for a few emblematic compounds in Fig. 5A.

### 3.2.1. Increased metabolites in FCR-infected fruitlets pulp

Almost 70 % of the metabolites were significantly increased in infected fruitlets (Fig. 4, cluster 1). Metabolites displaying the strongest increase were found in the cluster 1.1.1. Some were significantly increased exclusively in infected fruitlets (Supplementary Figure S2), including oxoglutarate, p-coumarate, caffeoyl- and vanilloyl-hexosides, caffeoyl aldarates, as well as coumaroyl-, caffeoyl- and feruloyl-glycerol esters, while others were also significantly increased in asymptomatic fruitlets although to a much lower extent (Supplementary Figure S3), i.e. succinate, caffeate, feruloyl aldarate, a diferuloylspermidine isomer and coumaroyl-, caffeoyl- and feruloyl-isocitrate esters. Metabolites displaying more moderated increase in infected fruitlets were found in the clusters 1.1.2 and 1.2. Some metabolites significantly increased in infected fruitlets only (Supplementary Figure S4), such as arabinose, xylose, galactose, glutamate, two diferuloylspermidine isomers and a set of putative disaccharides and sugar acids like U_gentiobiose_1,

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** PCA score (A) and loading (B) plots of pulp and skin samples from healthy, asymptomatic or FCR-infected 'Queen Victoria' pineapple fruitlets. (A): HP: healthy pulp, AP: asymptomatic pulp, IP: infected pulp, HS: healthy skin, AS: asymptomatic skin, IS: infected skin. Ellipses represent the distribution of each modality with a 95 % level of confidence. (B): red dots represent the compounds that most influenced the PCA.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** PCA score (A, C) and loading (B, D) plots of pulp (A, B) and skin (C, D) samples from healthy, asymptomatic or FCR-infected 'Queen Victoria' pineapple fruitlets. (A, C): HP: healthy pulp, AP: asymptomatic pulp, IP: infected pulp, HS: healthy skin, AS, asymptomatic skin, IS: infected skin. Ellipses represent the distribution of each modality with a 95 % level of confidence. (B, D): red dots represent the compounds that most influenced the PCA.</figcaption></figure>

U_palatinose, U_turanose and U_saccharate, U_galactonate, U_erythronate. Other compounds were also increased in asymptomatic fruitlets (Supplementary Figures S5 and S6): ribose, glucose, fructose-6-phosphate (F6P), glucose-6-phosphate (G6P), malate, phenylalanine, pipecolate and α-aminobutyric acid (AABA) for instance.

### 3.2.2. Decreased metabolites in FCR-infected fruitlets pulp

About 22 % of the metabolites were significantly decreased in infected fruitlets (Fig. 4, cluster 2). Some decreased in infected fruitlets only (Supplementary Figure S7), like a p-coumaroyl aldarate, S-p-coumaroyl and S-sinapyl-glutathione, syringaringesinol hexoside, a syringoyl hexoside, several sinapoyl conjugates, including GSC, sinapoyl hexosides and methyl sinapate, and putative di- and trisaccharides such as U_maltotriose_1, U_sucrose_3 and U_sucrose_5. Other metabolites were also decreased in asymptomatic fruitlets (Supplementary Figure S8), including p-coumaroyl hexoside, sinapoyl hexoside_1 and putative di- or trisaccharides: U_fructosylfructose_1, U_mannobiose_1, U_melezitose_1, U.raffinose 1 and 2.

### 3.2.3. Decreased metabolites only in asymptomatic fruitlets pulp

Finally, a few compounds, mostly disaccharides including melibiose, trehalose and its putative isomer U_trehalose, displayed a significant decrease only in asymptomatic fruitlets (Supplementary Figure S9).

## 3.3. Effects of FCR disease on skin metabolome

A hierarchical clustering was performed on the subset of 27

metabolites exhibiting significant differences (Kruskal-Wallis, p-value < 0.05) related to the infection status in skin samples (Fig. 6). In contrast to the pulp, the samples were classified into two groups: healthy fruitlets on one side, and infected or asymptomatic fruitlets on the other. The metabolite levels in the cluster 1 were increased upon infection whereas those in the cluster 2 were decreased, at least in the majority of fruitlets. We also note that while fewer compounds showed variation in the skin compared to the pulp, the amplitude of these variations was generally more moderate: the maximum logarithmic fold change relative to healthy fruitlets was 7.2 in the pulp and only 1.3 in the skin, as illustrated in Fig. 5B for a few emblematic compounds.

### 3.3.1. Increased metabolites in FCR-infected and asymptomatic fruitlets skin

The metabolites accumulated upon infection included succinate and several amino acids: alanine, isoleucine, leucine, proline, serine and threonine (Supplementary Figure S10 and S11).

### 3.3.2. Decreased metabolites in FCR-infected and asymptomatic fruitlets skin

Metabolites depleted upon infection were mostly mono-, di- and trisaccharides, such as galactose, sucrose, U_gentiobiose_1, U.raffinose_1 and U_maltotriose_1 (Supplementary Figure S12). It is worth noting the decrease in S-p-coumaroylglutathione, just as in the pulp, along with a decrease in S-coniferylglutathione.
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Hierarchical clustering analysis of a subset of metabolites displaying a significant variation (Kruskal-Wallis test and Conover-Iman post-hoc test, p-value < 0.05) due to FCR disease in the pulp of 'Queen Victoria' pineapple fruitlets. HCA was performed on data after log transformation and Pareto scaling, using the Pearson correlation and the Ward algorithm. Samples are displayed in columns and metabolites in rows. HP: healthy pulp; AP: asymptomatic pulp; IP: infected pulp.</figcaption></figure>

## 4. Discussion

The metabolomic study of 'Queen Victoria' pineapple with contrasting FCR infection status provides insights into the observed variations, which may reflect either defense mechanisms or functional disorders (Fig. 7).

### 4.1. FCR disease altered the pulp metabolome more dramatically than the skin metabolome

Both pulp and skin metabolic profiles were impacted by FCR disease but in different ways. Much more metabolic markers of infection were identified in the pulp (92 compounds) compare to the skin (27 compounds), with the response to infection being notably stronger in pulp. This may be attributed to the physical presence of the pathogenic fungi in the pulp, where their metabolic activity and the majority of plant
A

<figure><img src="image_7.png" /></figure>

B

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Emblematic metabolites illustrating significant variations due to FCR disease in (A) the pulp and (B) the skin samples of 'Queen Victoria' pineapple fruitlets. Analysis of variance was performed on raw data using the Kruskal-Wallis test followed by a Conover-Iman post-hoc test (p-value < 0.05). HP: healthy pulp; AP: asymptomatic pulp; IP: infected pulp, HS: healthy skin; AS: asymptomatic skin; IS: infected skin.</figcaption></figure>

defense mechanisms occurred. This could explain why it is difficult to identify FCR-infected infructescences based solely on the appearance of the fruit, posing a major disadvantage for the pineapple production industry. Currently, infected pineapples are often associated with a remaining green coloration of the skin at full maturity, localized on one or more individual fruitlets (Gu et al., 2015). Professionals rely on this subjective criterion, which can sometimes reflect an underlying infection, to assess the presence of FCR during the post-harvest phases.

Despite the visible symptoms, FCR infections consistently exhibited similar metabolic patterns, suggesting that either all fruits were infected with the same pathogen(s) or that metabolic responses do not significantly differ depending on the fungal species involved. However, further

research is needed to explore potential variations in metabolic responses to different fungal species responsible for FCR necrotic lesions.

A methodological bias may partly explain the differences between pulp and skin, since the metabolite extraction method used here is optimal for soluble compounds of high to medium polarity, which are likely to be abundant in the pulp. The skin has a very different tissue structure, with a high proportion of cellulose fibers that are quite lignified (Huang et al., 2011) and fewer soluble metabolites. FCR-induced differences in wall structure and wax composition of pineapple skin thus remain to be explored using *ad hoc* methods.

## 4.2. FCR induces reconfigurations of fruit metabolism toward defense

### 4.2.1. Meeting energy demand

Several glycolysis and tricarboxylic acid intermediates, such as glucose, G6P, F6P, malate, succinate and oxoglutarate, were significantly accumulated in the pulp of infected fruitlets. This indicates a probable increase in respiration which could be attributed to the plant, the fungus or both, in agreement with Tzortzakis (2019) and Mäkelä et al. (2018), who respectively observed a post-harvest increase in tissue respiration rate and the upregulation of glycolysis gene transcription during plant infection. A higher rate of respiration after infection might be interpreted as a way to satisfy the high-energy demand for the onset of defense mechanisms after infection (Pastor et al., 2014; Balmer et al., 2018) and for increased carbon fluxes toward the biosynthesis of secondary metabolites involved in defense (Rojas et al., 2014; Lacrampe et al., 2023).

### 4.2.2. Fueling defense metabolisms

Xylose and arabinose are other primary metabolites whose contents increased in the pulp of infected fruitlets. These compounds are implicated in plant cell wall elaboration through the synthesis of xylans and xyloglucans constituting hemicelluloses (Rennie and Scheller, 2014). Their accumulation is ambiguous, as it may either reflect pathogen-induced cell wall degradation or on the contrary an activation of the plant parietal metabolism at the site of infection to contain the invasion of the pathogens (Cantu et al., 2008; Lorrai and Ferrari, 2021).

FCR disease also strongly stimulated the entire phenylpropanoid pathways, in agreement with Barral et al. (2017, 2019a, 2019b, 2021), starting with phenylalanine which accumulated upon infection (Fig. 8). The role of phenylpropanoids in plant defense during host-pathogen interactions is generally well established (Zaynab et al., 2018), in particular their antifungal activity (Xu et al., 2018; Costa et al., 2020; Del Cueto et al., 2021). The comparison by Barral et al. (2019b) of pineapple pulp metabolic profiles in FCR susceptible and resistant cultivars strongly suggested that phenolic compounds were factors of resistance. In particular, hydroxycinnamic acid conjugated to isocitrate increased drastically in infected fruitlets, while hydroxybenzoic acid isomers were reduced. In the present work, increase in hydroxycinnamate isocitrate esters content was also observed (Fig. 8) and more phenolic compounds were putatively identified based on previous studies (Steingass et al., 2015, 2017), notably hydroxycinnamic acid glycerol esters. The antimicrobial activity of these compounds was established on bacteria and fungi such as *Escherichia coli*, *Staphylococcus aureus*, *Bacillus subtilis*, *Fusarium ananatum* and *Saccharomyces cerevisiae* (Barral et al., 2017; Sun et al., 2020). Additionally, phenolamides accumulating in the pulp of infected fruitlets were identified, i.e. diferuloylspermidine. This molecular family has been proved to be involved in plant growth and in responses to biotic and abiotic stresses (Seifi and Shelp, 2019; Liu et al., 2022), including resistance to infection (Walters et al., 2001; Knollenberg et al., 2020). A notable exception concerned the derivatives of sinapate, whose contents were, on the contrary, decreased in the pulp of infected fruitlets (Fig. 8). This depletion may result from the ingenuity of pathogens to counteract specifically the biological effects of sinapate, which has been reported to inhibit growth and conidial germination of the fungal pathogen *Fusarium oxysporum*
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Hierarchical clustering analysis of a subset of metabolites displaying a significant variation (Kruskal-Wallis test and Conover-Iman post-hoc test, p-value < 0.05) due to FCR disease in the skin of 'Queen Victoria' pineapple fruitlets. HCA was performed on data after log transformation and Pareto scaling using the Pearson correlation and the Ward algorithm. Samples are displayed in columns and metabolites in rows. HS: healthy skin; AS: asymptomatic skin; IS: infected skin.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Schematic representation of significant metabolic changes in pulp (filled rectangles) and skin (hatched rectangles) metabolome of 'Queen Victoria' pineapple fruits due to FCR-infection. In red, metabolites for which levels increased following FCR-infection; in blue, metabolites for which levels decreased after FCR-infection.</figcaption></figure>

while reducing the activity of pathogenic enzymes (Wu et al., 2009). Similarly, the decrease in the content of the lignan syringaresinol hexoside, which possesses inhibitory activity against pathogens (Ouyang et al., 2007) could trade a lignan degradation provoked by

fungi, as previously observed with *Fusarium solani* and *Phanerochaete chrysosporium* (Kamaya et al., 1983; Higuchi, 1986; Kinjo et al., 1991). Another notable exception was the specific decrease in the pools of glutathione phenolic conjugates, observed in both pulp and skin. These
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Emblematic metabolites illustrating the systemic metabolic reconfigurations in the pulp of asymptomatic fruitlets from FCR-infected pineapple. Analysis of variance was performed on raw data using the Kruskal-Wallis test followed by a Conover-Iman post-hoc test (p-value < 0.05). HP: healthy pulp; AP: asymptomatic pulp; IP: infected pulp.</figcaption></figure>

compounds could constitute reserves of free phenolics and glutathione for defense, cell structure regulator and the control of oxidative stress generated by the fungus. The hydrolysis of glutathione phenolic conjugates, such as S-p-coumarylglutathione and S-sinapyl-glutathione, whose levels were significantly reduced in infected pineapples, could be the cause of an increase in phenolic compounds (p-coumarate and sinapate) serving plant defense mechanisms. Similarly, the decrease in S-coniferylglutathione could be explained by its hydrolysis, resulting in the release of cell wall reinforcement molecules (coniferaldehyde). In parallel, the increase in glutathione levels could reflect the implementation of oxidative stress control, as it maintains cellular redox balance and detoxifies ROS. Indeed, the redox status of tissues is a major issue in infection processes by pathogenic fungi (Zhang et al., 2020), as the plant's defenses obey a pro-life logic, including the production of antioxidant molecules counteracting the necrosis (Deng et al., 2018).

### 4.2.3. Coping with secondary stresses

The most remarkable feature in skin metabolic profiles upon infection was the accumulation of amino acids related to abiotic stress, like proline and branched-chain amino acids (BCAAs) (Fig. 6 and Supplementary Figure S11). The rise of proline is usually a response to osmotic disruption resulting from biotic or abiotic stresses (Verbruggen and Hermans, 2008; Dar et al., 2016) but it has also been related to ROS detoxification, membrane integrity protection and hypersensitive cell death triggering (Zeier, 2013). Proline accumulation has been previously associated to the hypersensitive response, acting to alleviate oxidative damages on photosynthetic tissues surrounding the infected area by acting as a free radical scavenger (Fabro et al., 2004). Skin samples from infected fruitlets also exhibited a significant increase in isoleucine and leucine contents. The accumulation of BCAAs has often been reported following abiotic and biotic stress, notably after bacterial infection (Ward et al., 2010; Návarová et al., 2012). Their role in plant defense is still unclear but it has been suggested that they could trigger plant systemic acquired resistance (Zeier, 2013). Accordingly, increased BCAA levels in pineapple samples may reflect their crucial role in activating defenses, as Corredor-Moreno et al. (2021) identified an association between BCAA levels and resistance to yellow rust infection in wheat.

## 4.3. FCR activates metabolic signaling pathways

### 4.3.1. Onset of systemic acquired resistance

Two non-proteinogenic amino acids were significantly accumulated in the pulp of infected fruitlets and are reported here for the first time: pipecolate and AABA (Fig. 8). The role of pipecolate as a key hormone in SAR alongside salicylic acid has been studied for a decade (Návarová et al., 2012; Bernsdorff et al., 2016; Wang et al., 2018; Sharma et al., 2021). Pipecolate is the precursor of N-hydroxypipecolate, the active form of the hormone that triggers defense responses in tissues remote from the biotic attack (Hartmann and Zeier, 2019). However, the literature is limited regarding the role of this signaling pathway in fruit. Pipecolate has already been found in different organs, including fruit of the *Citrus* genus (Servillo et al., 2012). Similarly, it was one of the main biomarkers identified in melon pulp infected post-harvest with *Fusarium pallidoroseum* (Filho et al., 2020). The increase of pipecolate observed after FCR infection suggests the onset of systemic defense mechanisms, especially since its levels in asymptomatic fruitlets were also increased almost two times (p-value = 0.087, Supplementary Table S4). This is consistent with the establishment of SAR, linked to transcriptional activation of salicylate-regulated pathways observed during the preservation of citrus fruit infected with various fungi (Deng et al., 2018). AABA, β-aminobutyric acid (BABA) and γ-aminobutyric acid (GABA) are naturally synthetized in planta and fulfil meaningful though diverse role in plant response subsequent to biotic and abiotic stresses (Shang et al., 2011; Scholz et al., 2015; Thevenet et al., 2017; Wang et al., 2017; Yao et al., 2020). Although BABA and GABA play a major part in priming defense following pathogen or insect attacks (Walters et al., 2013; Cao et al., 2014), AABA has shown only limited activity, as reported by Thevenet et al. (2017). However, AABA has proven some efficacy by inducing resistance in tomato and potato plants infected with *Phytophthora infestans* (Cohen, 1994, 2002; Cohen et al., 1994) since it increased chitinase activity, ethylene production and pathogenesis-related protein accumulation in plants. Interestingly, AABA is also the precursor of ophthalmic acid, a tripeptide analogue of glutathione (Servillo et al.,
2018), which may suggest a role in oxidative stress management during the necrotrophic phase of FCR disease in pineapple. Like pipecolate, AABA drastically increased in both asymptomatic and infected fruitlets, suggesting its involvement in systemic resistance following the pineapple invasion by FCR-associated pathogens.

### 4.3.2. Sugar signaling

A large number of metabolic markers varying with the experimental conditions belonged to di- and trisaccharide families. Most of them could not be identified with certainty but their spectra presented important similarities with known saccharides such as sucrose, trehalose, maltose or melibiose. The functions of saccharides in plant immune responses after microbial attack have recently gained immense interest as they appear essential in plant-pathogen interactions, acting both as energy sources to stimulate plant defense mechanisms and as signaling molecules in localized or systemic defense responses (Morkunas and Ratajczak, 2014; Trouvelot et al., 2014).

A remarkable trend was that of trehalose, a plant disaccharide known to be involved in both biotic stress signaling and sugar sensing to regulate carbohydrate storage (Rolland et al., 2006; Baena-González, 2010; Phan and Van Dijck, 2019). Interestingly, trehalose is also

produced by fungi, such as *Aspergillus niger*, where it is important during spores germination (Svanström et al., 2014). Its role in pathogenesis is rather ambivalent, since depending on the pathosystem, it can promote infection (Foster et al., 2003; Piazza et al., 2015), or on the contrary serve as an elicitor likely to prime defense (De Britto et al., 2021). In plants, trehalose is generally not expected to accumulate, but can have positive effects on plant defense, as demonstrated in many pharmacological experiments (Reignault et al., 2001; Li et al., 2022; MacIntyre et al., 2022). The higher trehalose amounts observed in the pulp of infected fruitlets (Fig. 8) may thus originate either from the fungus metabolism or from the plant itself. This could possibly result from a higher activity of the trehalose-6-phosphate (T6P) phosphatase, depleting the T6P pool with a consequent upregulation of the carbon toward soluble sugar production instead of starch storage (Ponnu et al., 2011).

Other sugars varying significantly upon FCR infection in the present study were found to be involved in plant resistance responses to biotic stresses. It was notably the case for palatinose and turanose, which participated in defense signaling pathways during *Fusarium oxysporum lycopersici* infection in tomato (Sinha et al., 2002), as well as for raffinose, which played a role in *Arabidopsis* response to nematode

<caption>**Table 1**</caption>

Significant metabolite variations induced by FCR disease (Kruskal-Wallis, p-value < 0.05) in fruitlet pulp (A) and skin (B) of 'Queen Victoria' pineapples: HP: healthy pulp; AP: asymptomatic pulp; IP: FCR-infected pulp, HS: healthy skin; AS: asymptomatic skin; IS: FCR-infected skin.

<table><thead><tr><th rowspan="2">A<br>Compound identity</th><th rowspan="2">Id level</th><th colspan="2">Log2 ratios</th><th rowspan="2">B<br>Compound identity</th><th rowspan="2">Id level</th><th colspan="2">Log2 ratios</th></tr><tr><th>IP/HP</th><th>AP/HP</th><th>IS/HS</th><th>AS/HS</th></tr></thead><tbody><tr><td>U_Erythritol</td><td>[3]</td><td>7,2</td><td>*</td><td>3,5</td><td>*</td><td>0,9</td><td>*</td></tr><tr><td>Caffeoylisocitrate</td><td>[3]</td><td>6,5</td><td>*</td><td>2,2</td><td>*</td><td>0,8</td><td>*</td></tr><tr><td>Feruloylisocitrate</td><td>[3]</td><td>6,7</td><td>*</td><td>2,1</td><td>*</td><td>0,6</td><td>*</td></tr><tr><td>p-coumaroylisocitrate_1</td><td>[3]</td><td>4,4</td><td>*</td><td>1,7</td><td>*</td><td>0,8</td><td>*</td></tr><tr><td>p-coumaroylisocitrate_2</td><td>[3]</td><td>4,6</td><td>*</td><td>1,2</td><td>*</td><td>0,5</td><td>*</td></tr><tr><td>Caffeate</td><td>[2]</td><td>6,6</td><td>*</td><td>1,0</td><td>*</td><td>0,4</td><td>*</td></tr><tr><td>Feruloyl-caffeoylglycerol</td><td>[1]</td><td>4,1</td><td>*</td><td>1,0</td><td>*</td><td>0,4</td><td>*</td></tr><tr><td>G6P</td><td>[1]</td><td>1,6</td><td>*</td><td>0,9</td><td>*</td><td>0,3</td><td>*</td></tr><tr><td>F6P</td><td>[2]</td><td>1,5</td><td>*</td><td>0,9</td><td>*</td><td>0,3</td><td>*</td></tr><tr><td>Diferuloylspermidine_4</td><td>[3]</td><td>2,8</td><td>*</td><td>0,8</td><td>*</td><td>-0,2</td><td>*</td></tr><tr><td>U_Galactonic acid-1,4-lactone</td><td>[3]</td><td>1,1</td><td>*</td><td>0,8</td><td>*</td><td>-0,2</td><td>*</td></tr><tr><td>U_myo-Inositol</td><td>[3]</td><td>0,7</td><td>*</td><td>0,7</td><td>*</td><td>-0,2</td><td>*</td></tr><tr><td>Octadecenoate</td><td>[1]</td><td>0,7</td><td>*</td><td>0,6</td><td>*</td><td>-0,1</td><td>*</td></tr><tr><td>U_Galactosylglycerol_4</td><td>[3]</td><td>3,8</td><td>*</td><td>0,5</td><td>*</td><td>-0,1</td><td>*</td></tr><tr><td>Hexadecanoate</td><td>[3]</td><td>1,0</td><td>*</td><td>0,5</td><td>*</td><td>-0,2</td><td>*</td></tr><tr><td>Feruloyl aldarate_1</td><td>[3]</td><td>2,2</td><td>*</td><td>0,5</td><td>*</td><td>-0,3</td><td>*</td></tr><tr><td>U_Galactosylglycerol_1</td><td>[3]</td><td>1,4</td><td>*</td><td>0,5</td><td>*</td><td>-0,3</td><td>*</td></tr><tr><td>Feruloyl aldarate_2</td><td>[3]</td><td>2,5</td><td>*</td><td>0,5</td><td>*</td><td>-0,3</td><td>*</td></tr><tr><td>Phenylalanine</td><td>[3]</td><td>0,9</td><td>*</td><td>0,4</td><td>*</td><td>-0,3</td><td>*</td></tr><tr><td>Succinate</td><td>[3]</td><td>1,7</td><td>*</td><td>0,4</td><td>*</td><td>-0,3</td><td>*</td></tr><tr><td>U_Galactose</td><td>[3]</td><td>1,0</td><td>*</td><td>0,3</td><td>*</td><td>-0,3</td><td>*</td></tr><tr><td>Threonate-1,4-lactone</td><td>[2]</td><td>0,7</td><td>*</td><td>0,3</td><td>*</td><td>-0,4</td><td>*</td></tr><tr><td>Malate</td><td>[1]</td><td>0,4</td><td>*</td><td>0,3</td><td>*</td><td>-0,7</td><td>*</td></tr><tr><td>Ribose</td><td>[1]</td><td>0,8</td><td>*</td><td>0,3</td><td>*</td><td>-0,8</td><td>*</td></tr><tr><td>HDFMH_1</td><td>[3]</td><td>1,0</td><td>*</td><td>0,3</td><td>*</td><td>-0,7</td><td>*</td></tr><tr><td>HDFMH_2</td><td>[3]</td><td>1,0</td><td>*</td><td>0,3</td><td>*</td><td>-0,8</td><td>*</td></tr><tr><td>U_Methylmannopyranoside</td><td>[3]</td><td>0,7</td><td>*</td><td>0,3</td><td>*</td><td>-0,8</td><td>*</td></tr><tr><td>U_Pentonate-1,4-lactone</td><td>[3]</td><td>0,8</td><td>*</td><td>0,2</td><td>*</td><td>-0,8</td><td>*</td></tr><tr><td>PCGG</td><td>[3]</td><td>1,0</td><td>*</td><td>0,2</td><td>*</td><td>-0,8</td><td>*</td></tr><tr><td>U_Mannobiose_1</td><td>[3]</td><td>-0,2</td><td>*</td><td>-0,2</td><td>*</td><td>-0,7</td><td>*</td></tr><tr><td>U_Isomaltose</td><td>[3]</td><td>0,1</td><td>*</td><td>-0,2</td><td>*</td><td>-0,7</td><td>*</td></tr><tr><td>HDFH</td><td>[3]</td><td>-0,3</td><td>*</td><td>-0,2</td><td>*</td><td>-0,7</td><td>*</td></tr><tr><td>U_Raffinose_2</td><td>[3]</td><td>-0,3</td><td>*</td><td>-0,2</td><td>*</td><td>-0,7</td><td>*</td></tr><tr><td>U_Melezitose_1</td><td>[3]</td><td>-0,3</td><td>*</td><td>-0,2</td><td>*</td><td>-0,7</td><td>*</td></tr><tr><td>U_Mannopyranose</td><td>[3]</td><td>0,1</td><td>*</td><td>-0,2</td><td>*</td><td>-0,7</td><td>*</td></tr><tr><td>U_Maltose_2</td><td>[3]</td><td>0,1</td><td>*</td><td>-0,2</td><td>*</td><td>-0,7</td><td>*</td></tr><tr><td>Melibiose</td><td>[1]</td><td>0,1</td><td>*</td><td>-0,3</td><td>*</td><td>-0,7</td><td>*</td></tr><tr><td>Sinapoyl hexoside_1</td><td>[3]</td><td>-2,3</td><td>*</td><td>-0,3</td><td>*</td><td>-0,7</td><td>*</td></tr><tr><td>U_Trehalose</td><td>[3]</td><td>-0,1</td><td>*</td><td>-0,3</td><td>*</td><td>-0,7</td><td>*</td></tr><tr><td>U_Fructosylfructose_1</td><td>[3]</td><td>-0,3</td><td>*</td><td>-0,3</td><td>*</td><td>-0,7</td><td>*</td></tr><tr><td>U_Sophorose</td><td>[3]</td><td>0,2</td><td>*</td><td>-0,3</td><td>*</td><td>-0,7</td><td>*</td></tr><tr><td>Feruloyl hexoside</td><td>[3]</td><td>0,2</td><td>*</td><td>-0,5</td><td>*</td><td>-0,7</td><td>*</td></tr><tr><td>Trehalose</td><td>[1]</td><td>1,1</td><td>*</td><td>-0,5</td><td>*</td><td>-0,7</td><td>*</td></tr></tbody></table>

<footnote>* p-value < 0.05 of Conover-Iman post-hoc test for pairwise comparisons of IP vs HP and AP vs HP (A) and IS vs HS and AS vs HS (B)</footnote>
infestation (Hofmann et al., 2010).

### 4.4. Asymptomatic fruitlets developed a systemic metabolic response to infection in FCR-infected pineapples

An important result of this study was the observation of a systemic response to infection, with the content of 43 pulp compounds and 24 skin compounds significantly affected by FCR disease in asymptomatic fruitlets (Table 1), showing that the presence of fungal pathogens in some fruitlets affected the entire infructescence. Many trends observed in infected fruitlets were also observed in asymptomatic fruitlets (Supplementary Figure S1B), with 86% of the compounds significantly altered in the asymptomatic fruitlets pulp being also altered in the infected fruitlets pulp, all but two compounds, notably trehalose, varying in the same direction. The systemic response observed was therefore largely similar to certain responses observed in fruitlets in direct contact to the fungal filaments.

The metabolic pathways involved were mostly phenolic compounds and some central metabolites in the pulp, amino acids in the skin, and many sugars in both tissues. The essential difference concerned the amplitude of the variations, clearly less marked in the pulp of asymptomatic fruitlets compared to that of the infected fruitlets (Table 1). The reason of these systemic metabolic responses is unclear but could reflect attempts to limit the pathogen progression. On one hand, the preventive accumulation of fungicidal and antioxidant phenolic compounds in the pulp is likely to prevent the growth of necrotrophic fungi (Lattanzio et al., 2006; Barral et al., 2017); on the other hand, trehalose, whose content increased in infected fruitlets while it decreased in asymptomatic fruitlets, may reflect a fungal production of trehalose in filament-carrying tissues and a reduction of its biosynthesis in filament-free tissues attempting to limit their expansion. A functional role in defense of these metabolic reconfigurations seems to be confirmed by pipecolate variations, which reflect a probable establishment of SAR, in a manner consistent with observations in banana subjected to Fusarium attack (Wu et al., 2013; Sun et al., 2019). This systemic response could explain the increased levels of phenylalanine, the precursor of defense phenolic compounds, in asymptomatic fruitlets, thereby promoting antifungal metabolite biosynthesis. At this stage, it would be of interest to investigate the presence of fungal mycotoxins and protein effectors to assess their influence on asymptomatic fruitlets linked to pathogen activity.

Beyond their physiological significance, the FCR-induced metabolic reconfigurations affecting systemically the whole infructescence are even more likely to impact pineapples organoleptic and nutritional value, as post-harvest fruit quality depends to a large extent on the metabolome (Pott et al., 2020), and in particular on the major sugar-acid balances and astringent, bitter or fragrant compounds.

## 5. Conclusion

The present study reveals the significant variations in metabolic profiles of 'Queen Victoria' pineapples pulp and skin samples of healthy, asymptomatic and FCR-infected fruitlets. Important markers were found among amino acids, phenolic compounds implicated in plant defense, as well as carbohydrates and phytohormone involved in glycolysis and respiration metabolisms, cellular integrity and signaling pathways. The results suggest the establishment of a strong response in infected fruitlets, including the implementation of a local defense following the fungal attack, and the induction of a systemic resistance highlighted by simultaneous metabolic variations in both infected and asymptomatic fruitlets. These insights could enable the development of new control strategies, including elicitation, to stimulate plant defense mechanisms. Further research is essential to clarify the underlying defense metabolic processes in the 'Queen Victoria' cultivar and to develop agricultural practices aimed at improving fruit resistance.

## CRediT authorship contribution statement

**Barral Bastien:** Writing – review & editing, Supervision, Investigation. **Latchoumane Lorraine:** Writing – original draft, Visualization, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Minier Jérôme:** Writing – review & editing, Data curation. **Chevallier Olivier:** Resources, Methodology, Investigation. **Chillet Marc:** Writing – review & editing, Supervision, Conceptualization. **Davrieux Fabrice:** Writing – review & editing. **Lugan Raphaël:** Writing – original draft, Visualization, Validation, Supervision, Resources, Methodology, Investigation, Conceptualization.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

Authors thank Exotic Boyer Réunion for providing fruit. Association National Recherche Technologie (ANRT) and Exotic Boyer Réunion are thanked for the funding. Authors wish to thank Simon Raynal for his help during data acquisition, and Frédéric Chiroleu, Nathalie Lacrampe and Coline Pons for their help regarding the statistical analyses.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.postharvbio.2025.113561.

## Data availability

The data that has been used is confidential.

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