# Prevalence of mealybug wilt-associated viruses in pineapple 'Queen Victoria' cultivars in Reunion Island and the potential of virus-free vitroplants for disease management

Delphine Massé, Frédéric Chiroleu, Nathalie Cassam, Bruno Hostachy, Jean-Michel Lett, Pierre Lefeuvre, Pierre-Yves Teycheney

► To cite this version:

Delphine Massé, Frédéric Chiroleu, Nathalie Cassam, Bruno Hostachy, Jean-Michel Lett, et al.. Prevalence of mealybug wilt-associated viruses in pineapple 'Queen Victoria' cultivars in Reunion Island and the potential of virus-free vitroplants for disease management. Crop Protection, 2026, 199, pp.107440. 10.1016/j.cropro.2025.107440. hal-05346866

HAL Id: hal-05346866

https://hal.science/hal-05346866v1

Submitted on 4 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Prevalence of mealybug wilt-associated viruses in pineapple 'Queen Victoria' cultivars in Reunion Island and the potential of virus-free vitroplants for disease management

Delphine Massé<sup>a,∗</sup> <sup>●</sup>, Frédéric Chiroleu<sup>b,●</sup>, Nathalie Cassam<sup>a</sup>, Bruno Hostachy<sup>a</sup>,
Jean-Michel Lett<sup>b</sup>, Pierre Lefeuvre<sup>c</sup>, Pierre-Yves Teycheney<sup>b,●</sup>

ARTICLE INFO

Keywords:

Ananas comosus

Pineapple mealybug wilt disease

Etiology

Virus

Prevalence

Symptoms

Vitroplants

A B S T R A C T

Mealybug wilt disease of pineapple (MWP) poses a significant threat to global pineapple production, yet its etiology remains poorly understood. The disease is believed to involve mealybugs, multiple pineapple mealybug wilt-associated viruses (PMWaVs, genus * Ampelovirus*), and possibly other viruses from the *Badnavirus*, *Secovirus*, and *Vitivirus* genera. This study examines the distribution and prevalence of three ampeloviruses (PMWaV1, PMWaV2, PMWaV3) and two badnaviruses (PBERV, PBCOV), previously associated to MWP symptoms, across 15 pineapple plots of the 'Queen Victoria' cultivar in Reunion Island. A total of 450 symptomatic and asymptomatic leaf samples were analyzed using PCR and RT-PCR-based diagnostics. Globally, 93 % of samples were infected with at least one virus, and 76 % were co-infected with two to four viruses. PMWaV1 (78 %) and PBCOV (87 %) were the most prevalent, while PBERV was not detected. Virus presence was associated with leaf dieback, wilting, and curling. Leaf reddening and yellowing, commonly associated with MWP, were not associated with viral infection, suggesting that other biotic or abiotic factors may be responsible for these symptoms. Viral infections and MWP symptoms were significantly lower in plots planted with vitroplants (VPs) compared to those using suckers, highlighting the potential of VPs in reducing viral infections. These findings highlight the complexity of MWP and underscore the potential benefits of using virus-free VPs to manage the disease.

1. Introduction

Pineapple (Ananas comosus L. [Merr.]), is a widely cultivated tropical
perennial from the Bromeliaceae family (Py et al., 1984). It ranks as the
third most-produced tropical fruit worldwide (Oculi et al., 2020).
However, mealybug wilt disease of pineapple (MWP) poses a serious
threat, causing yield losses of up to 80 % (Santa-Cecília et al., 2016).
MWP manifests through leaf reddening or yellowing, downward curling
at margins, leaf tip dieback, root rot, and progressive wilting, often
leading to premature plant death (Carter, 1933, 1945a, 1945b). While
some symptoms overlap with those caused by abiotic stresses like
drought, early research linked MWP to phytotoxins released following
mealybug infestations (Carter, 1933, 1935). Since then, multiple
MWP-associated viruses have been identified (Table 1), yet their exact
role in disease development remains unclear. Conflicting studies

attribute MWP either to a complex of three mealybug-transmitted ampeloviruses, pineapple mealybug wilt-associated virus 1 (PMWaV1), PMWaV2, and PMWaV3 (Gambley, 2008), or to the combined presence of PMWaV2 and mealybug species from the family *Pseudococcidae* (Hernandez-Rodriguez et al., 2014; Peron et al., 2019; Sether and Hu, 2002). Other viruses infecting pineapple, such as pineapple bacilliform CO virus (PBCOV), a badnavirus, pineapple secovirus B (PSVB), a sadwavirus, pineapple mealybug wilt-associated virus 6 (PMWaV6) and pineapple mealybug wilt-associated virus 7 (PMWaV7), two ampeloviruses, may also contribute to the disease (Larrea-Sarmiento et al., 2020; Larrea-Sarmiento et al., 2021; Larrea-Sarmiento et al., 2022; Massé et al., 2024; Massé et al., 2022, Table 1). This lack of consensus regarding MWP etiology hampers the development of effective disease management strategies.

Current methods for managing MWP remain limited. These primarily

<footnote><sup>a</sup> ANSES, LSV RAPT, Saint Pierre, La Réunion, F-97410, France</footnote>

<footnote><sup>b</sup> CIRAD, UMR PVBMT, Saint-Pierre, La Réunion, F-97410, France</footnote>

<footnote><sup>c</sup> CIRAD, UMR PVBMT, Can Tho city, Viet Nam</footnote>

<footnote>* Corresponding author.
E-mail address: delphine.masse@anses.fr (D. Massé).</footnote>
involve the development of resistant cultivars (Perez et al., 2005) and the application of cultural practices such as crop rotation, inter-cycle plant shredding, and the control of mealybug vectors (Nurbel et al., 2021; Soler et al., 2020). As pineapple propagation is mostly vegetative from fruit crowns or suckers, the use of vitroplants (VPs) as planting material has also been recommended to reduce virus transmission; however, their effectiveness in controlling MWP has yet to be evaluated (Ayenew et al., 2013).

Although MWP is recognized as a major constraint to pineapple production worldwide, the diversity and distribution of viruses associated with the disease remain poorly characterized in Reunion Island. To address this knowledge gap, we conducted the first island-wide survey of viral prevalence in pineapple cultivation systems. Our study specifically targeted three ampeloviruses (PMWaV1, PMWaV2, PMWaV3) and two badnaviruses (PBERV and PBCOV), combining molecular detection with systematic filed symptoms assessment across multiple plots and crop cycles. By integrating epidemiological data with the type of planting material used, we aimed to (i) establish the prevalence and distribution of these viruses, (ii) assess their potential associations with symptom expression, and (iii) evaluate the role of vitroplants in shaping viral diversity and infection dynamics. This work provides novel insights into the complex etiology of MWP under field conditions and contributes to clarifying the epidemiological basis for improved disease management strategies in pineapple cultivation.

## 2. Materials and methods

### 2.1. Field survey

Between September and October 2020, a survey was conducted across 15 pineapple plots in Reunion Island's main production areas representing a total of ~42,000 m² of inspected surface area, to assess the presence of symptoms associated with MWP in the *Queen Victoria* cultivar (Fig. 1A). Plot sizes ranged from 150 m² to 7,760 m². In two of the surveyed plots (P4 and P5, Supplementary Table 1), VPs were used as planting material, whereas in the remaining 13 plots, suckers

collected from the previous production cycle were used. For each plot, data were recorded, including altitude, plot age, plant vegetative stage at harvest, and cultivation practices including the use of ethylene (Ethephon®) as flowering induction treatment (FIT), crop rotation and intercropping and the application of pineapple plant residues as organic fertilizer. In each plot, 30 plants were observed and sampled following a W-shaped pattern.

### 2.2. Assessment of disease severity and mealybug presence

Considering that no validated severity scales for MWP and mealybug infestations exist in the literature, we developed such scales based on agronomic conventions and practical field considerations. The severity of leaf symptoms (reddening, yellowing, curling, wilting and dieback) (Fig. 2), was evaluated for each sampled plant using a four-point scale: 0 (no symptoms), 1 (weak), 2 (moderate), and 3 (severe). To facilitate consistent scoring, a visual reference scale was established based on the proportion of leaf area exhibiting symptoms. A score 1 (weak) corresponded to 0–25 % of the leaf surface affected, score 2 (moderate) to 25–70 %, and score 3 (severe) to 70–100 %.

Likewise, the presence and abundance of mealybugs were also monitored using a three-point scale: 0 (no mealybugs), 1 (1–10 mealybugs), and 2 (>10 mealybugs).

### 2.3. Viral contamination of healthy planting material under field culture conditions

Five lines of suckers derived from vitroplants (VPs) were monitored annually over a period of three to five years, depending on the plot (Table 2). Due to the limited number of plots planted with VP-derived suckers during this period on Reunion Island, only three additional lines were included in the monitoring. Surveillance spanned up to three successive crop cycles, each encompassing planting, vegetative growth, fruiting, harvesting, and the production of suckers used as planting material for the next cycle. New generations were established either on the same site or relocated to different plots in accordance with the

<table><caption>**Table 1**
Viruses infecting pineapple.</caption><thead><tr><th>Virus name</th><th>Accronym</th><th>Family (Subfamily)</th><th>Genus (sub genus)</th><th>Species</th><th>Genome size (bp)</th><th>Genbank accession number</th><th>Reference</th></tr></thead><tbody><tr><td>pineapple mealybug wilt-associated virus 1</td><td>PMWaV1</td><td>Closteroviridae</td><td>Ampelovirus</td><td>Ampelovirus unanasas</td><td>13,071</td><td>AF414119</td><td>Melzer et al., 2008</td></tr><tr><td>pineapple mealybug wilt-associated virus 2</td><td>PMWaV2</td><td></td><td></td><td>Ampelovirus duanasas</td><td>16,271</td><td>MN539273</td><td>Green et al., 2020</td></tr><tr><td>pineapple mealybug wilt-associated virus 3</td><td>PMWaV3</td><td></td><td></td><td>Ampelovirus trianasas</td><td>13,298</td><td>MN539274</td><td>Green et al., 2020</td></tr><tr><td>pineapple mealybug wilt-associated virus 5</td><td>PMWaV5</td><td></td><td></td><td>a</td><td>12,971</td><td>OQ850040</td><td>Massé et al. (2024)</td></tr><tr><td>pineapple mealybug wilt-associated virus 6</td><td>PMWaV6</td><td></td><td></td><td>a</td><td>17,907</td><td>MW269512</td><td>Larrea-Sarmiento et al. (2022)</td></tr><tr><td>pineapple mealybug wilt-associated virus 7</td><td>PMWaV7</td><td></td><td></td><td>a</td><td>18,388</td><td>OQ850042</td><td>Massé et al. (2024)</td></tr><tr><td>pineapple bacilliform CO virus</td><td>PBCOV</td><td>Caulimoviridae</td><td>Badnavirus</td><td>Badnavirus alphananas</td><td>7543</td><td>GU121676</td><td>Wu and Li, 2009</td></tr><tr><td>pineapple bacilliform ER virus</td><td>PBERV</td><td></td><td></td><td>Badnavirus betanasas</td><td>7485</td><td>OP860304</td><td>Larrea-Sarmiento et al. (2022)</td></tr><tr><td>pineapple secovirus A</td><td>PSVA</td><td>Secoviridae</td><td>Sadwavirus (Cholivirus)</td><td>Sadwavirus alphananas</td><td>6128</td><td>RNA1: MN809923<br>RNA2: MN809924</td><td>Larrea-Sarmiento et al. (2020)</td></tr><tr><td>pineapple secovirus B</td><td>PSVB</td><td></td><td></td><td>Sadwavirus betanasas</td><td>4161</td><td>RNA1: OM777135<br>RNA2: OM777136</td><td>Larrea-Sarmiento et al. (2022)</td></tr><tr><td>pineapple secovirus C</td><td>PSVC</td><td></td><td></td><td>a</td><td>5956</td><td>RNA1: OP860260<br>RNA2: OP860285</td><td>Larrea-Sarmiento et al. (2022)</td></tr><tr><td>pineapple secovirus D</td><td>PSVD</td><td></td><td></td><td>a</td><td>3808</td><td></td><td></td></tr><tr><td>pineapple secovirus D</td><td>PSVD</td><td></td><td></td><td>a</td><td>6159</td><td></td><td></td></tr><tr><td>pineapple secovirus D</td><td>PSVD</td><td></td><td></td><td>a</td><td>3189</td><td></td><td></td></tr><tr><td>pineapple secovirus D</td><td>PSVD</td><td></td><td></td><td>a</td><td>6160</td><td>b</td><td></td></tr><tr><td>pineapple secovirus D</td><td>PSVD</td><td></td><td></td><td>a</td><td>3600</td><td>b</td><td></td></tr><tr><td>pineapple vitivirus A</td><td>PiVA</td><td>Betaflexiviridae (Trivirinae)</td><td>Vitivirus</td><td>Vitivirus ananasae</td><td>6757</td><td>OM141115</td><td>Massé et al. (2022)</td></tr></tbody></table>

a: not yet recognized by the ICTV.

b: recovered from data mining, not registered in NCBI.
<figure><img src="image_3.png" /><figcaption>Fig. 1. Distribution of surveyed plots and prevalence of PBCOV, PMWaV1, PMWaV2, and PMWaV3.</figcaption></figure>

(A) Plot distribution. Plots established with vitroplants (VPs) are highlighted in yellow. Plots that received flowering induction treatment (FIT) are indicated by underlined plot numbers. (B) Virus prevalence in each plot, expressed as the number of positive plants per virus. Bar heights represent the number of positive samples, with values indicated above each bar. Colors denote to the four detected viruses: PBCOV, PMWaV1, PMWaV2, and PMWaV3. PBERV was not detected in any sample. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

<figure><img src="image_4.png" /><figcaption>Fig. 2. Symptoms associated with mealybug wilt disease of pineapple. (A) leaf reddening; (B) leaf yellowing; (C) leaf curling; (D) wilting; (E) dieback.</figcaption></figure>

farmers' crop rotation schedules.

Each year, between two and 60 plants per plot, depending on plot size and condition, were visually assessed for MWP symptoms using a W-

shaped sampling pattern. Leaf samples were collected from these plants and tested for the presence of PMWaV1, PMWaV2, PMWaV3, PBCOV, and PBERV using the virus indexing methods described previously.
<table><caption>**Table 2**
Monitoring of viral infections in pineapple plants derived from vitroplants (VPs) between 2016 and 2021 on Reunion Island.</caption><thead><tr><th rowspan="2">Line</th><th rowspan="2">Sampling date</th><th rowspan="2">Planting date</th><th rowspan="2">Crop cycle at time of sampling</th><th rowspan="2">Location of plot</th><th rowspan="2">Pineapple cultivated on plot before planting</th><th rowspan="2">Crop cultivated on adjoining plot</th><th rowspan="2">Symptoms</th><th rowspan="2">Number of samples analyzed</th><th colspan="3">Number of infected samples</th><th rowspan="2">Total</th><th rowspan="2">Number of plants infested by mealybugs</th></tr><tr><th>PMWaV1-single infections</th><th>PMWaV3-single infections</th><th>PMWaV1+ PMWaV3 mixed infections</th></tr></thead><tbody><tr><td>L1</td><td>December 2021</td><td>2020</td><td>3</td><td>South (St Pierre)</td><td>No</td><td>pineapple</td><td>No</td><td>60</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>L2</td><td>March 2016</td><td>2016</td><td>1</td><td>West (Boucan)</td><td>No</td><td>other</td><td>reddening</td><td>24</td><td>0</td><td>0</td><td>0</td><td>0</td><td>NA</td></tr><tr><td></td><td>February 2017</td><td>2016</td><td>1</td><td>West (Boucan)</td><td>No</td><td>other</td><td>yellowing, reddening</td><td>26</td><td>0</td><td>0</td><td>0</td><td>0</td><td>NA</td></tr><tr><td></td><td>March 2018</td><td>2016</td><td>1</td><td>West (Boucan)</td><td>No</td><td>other</td><td>NA</td><td>10</td><td>0</td><td>0</td><td>0</td><td>0</td><td>NA</td></tr><tr><td></td><td>May 2019</td><td>2018</td><td>2</td><td>South (Le Tampon)</td><td>No</td><td>pineapple</td><td>No</td><td>51</td><td>0</td><td>0</td><td>0</td><td>0</td><td>NA</td></tr><tr><td></td><td>June 2020</td><td>2020</td><td>3</td><td>South (St Pierre)</td><td>Yes</td><td>pineapple</td><td>reddening</td><td>45</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td></td><td>December 2021</td><td>2020</td><td>3</td><td>South (St Pierre)</td><td>Yes</td><td>pineapple</td><td>yellowing, reddening, wilting, curling</td><td>31</td><td>5</td><td>8</td><td>1</td><td>14</td><td>14</td></tr><tr><td>L3</td><td>February 2019</td><td>2019</td><td>1</td><td>South (St Pierre)</td><td>Yes</td><td>pineapple</td><td>No</td><td>37</td><td>0</td><td>0</td><td>0</td><td>0</td><td>NA</td></tr><tr><td></td><td>June 2020</td><td>2019</td><td>1</td><td>South (St Pierre)</td><td>No</td><td>pineapple</td><td>No</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>NA</td></tr><tr><td></td><td>December 2021</td><td>2019</td><td>1</td><td>South (St Pierre)</td><td>No</td><td>pineapple</td><td>reddening, wilting</td><td>9</td><td>0</td><td>5</td><td>4</td><td>9</td><td>5</td></tr><tr><td>L4</td><td>September 2020 (P4)</td><td>2019</td><td>1</td><td>South (Le Tampon)</td><td>No</td><td>other</td><td>reddening, yellowing, wilting, curling</td><td>30</td><td>7</td><td>12</td><td>2</td><td>21</td><td>0</td></tr><tr><td></td><td>December 2021</td><td>2019</td><td>1</td><td>South (Le Tampon)</td><td>No</td><td>other</td><td>dieback, wilting</td><td>18</td><td>2</td><td>7</td><td>6</td><td>15</td><td>1</td></tr><tr><td>L5</td><td>September 2020 (P5)</td><td>2019</td><td>1</td><td>South (Le Tampon)</td><td>No</td><td>other</td><td>reddening, yellowing, wilting</td><td>30</td><td>6</td><td>1</td><td>1</td><td>8</td><td>9</td></tr><tr><td></td><td>December 2021</td><td>2019</td><td>1</td><td>South (Le Tampon)</td><td>No</td><td>other</td><td>No</td><td>20</td><td>9</td><td>1</td><td>7</td><td>17</td><td>0</td></tr></tbody></table>
## 2.4. Virus indexing

For each sampled plant, two leaves were collected and pooled, resulting in 450 samples. The white basal portion of the leaves was cut into small pieces (~0.5 cm²) and stored at -80 °C until further use. Total nucleic acids (TNAs) were extracted from 100 mg of leaf tissue using a KingFisher robot extractor (Thermo Fisher Scientific, Waltham, MA, USA). Frozen leaves were first homogenized in 1 mL of 10 % PBST-PVP buffer with a Homex crusher (Bioreba, Reinach, Switzerland). The resulting extracts were centrifuged at 20,000×g for 15 min at 4 °C. Supernatants were discarded, and the pellets were resuspended in 350 µL of PBS. TNA extraction was then carried out using the InnuPREP Virus DNA/RNA Kit (Analytik Jena, Jena, Germany), according to the manufacturer's instructions. Extracted TNAs were eluted in 100 µL of RNase-free water and stored at -80 °C until they were used for viral indexings.

Indexing for PMWaV1, PMWaV2, and PMWaV3 was performed via multiplex RT-PCR, following the protocol of Massé et al. (2024), using the One-Step RT-PCR Kit (QIAGEN, Hilden, Germany). Detection of PBCOV and PBERV was conducted by multiplex PCR using primer pairs PBV2dF/PBV2dR and PBVd4F/PBV4dR, respectively, as described by Gambley et al. (2008a) (Supplementary Table 2). Reactions were prepared with the GoTaq G2 Hot Start Polymerase Kit (Promega, Madison, WI, USA) in 25 µL volumes containing 1X PCR buffer, 3 mM MgCl₂, 0.2 mM dNTPs, 0.8 µM PBCOV primers, 0.4 µM PBERV primers, 2 U of Taq DNA polymerase, 2 µL of TNA template, and nuclease-free water.

PCR conditions included an initial denaturation at 95 °C for 2 min, followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at 60 °C for 30 s, and extension at 72 °C for 1 min, with a final extension at 72 °C for 10 min. Amplicons corresponding to PBCOV (513 bp) and PBERV (236 bp) were resolved by electrophoresis on a 2.5 % agarose gel in 0.5X TBE buffer (45 mM Tris-borate, 1 mM EDTA, pH 8.0) containing 0.03 % ethidium bromide and visualized under UV light.

## 2.5. High-throughput sequencing and bioinformatics analysis

Total RNA was extracted using the RNeasy Plant Mini Kit (QIAGEN, Hilden, Germany) according to the manufacturer's instructions, including DNase treatment (DNase I, Thermo Fisher Scientific, Massachusetts, USA). RNA concentration was determined using a Qubit 4 fluorometer and the Qubit RNA BR assay kit (Thermo Fisher Scientific, Massachusetts, USA). RNA extract quality was assessed using the RNA Integrity Number (RIN) value obtained with the Agilent RNA 6000 Pico chip kit for Tapestation (Agilent Technologies, California, USA). For each sample, 2 µg of total RNAs were precipitated with 2.5 vol of absolute ethanol and 0.1 volume of sodium acetate and shipped to Azenta Life Sciences (South Plainfield, NJ, USA), where library preparation was carried out using the Ribozero plant rRNA depletion and TruSeq stranded total RNA library Prep kits (Illumina, San Diego, CA, USA) before paired-end sequencing (2 × 150 bp) on an Illumina NovaSeq sequencer.

Throughout all processes, from extraction to data analysis, two external control samples, hereafter referred to as alien samples (Massart et al., 2022; Rong et al., 2023), were incorporated to evaluate potential contamination or analytical bias. The first alien sample was a banana sample co-infected by two circular DNA viruses of the *Caulimoviridae* family, banana streak OL virus (BSOLV) and banana streak GF virus (BSGFV), and by an RNA virus from the *Betaflexiviridae* family, banana mild mosaic virus (BanMMV). The second alien sample was a sweet potato sample infected by an RNA virus from the *Potyvirus* genus, sweet potato feathery mottle virus (SPFMV).

*De novo* assembly of Illumina reads was performed using SPAdes v3.13 (Bankevich et al., 2012) with support from the MESO@LR Platform. The search for viral contigs was carried out using BLASTn and BLASTx searches against the reference viral sequence database retrieved from GenBank (v216) in April 2023. Coverage statistics for the viral

contigs identified were obtained after alignment of Illumina reads using BWA-MEM (BWA v0.7017.1 (Li and Durbin, 2009);) and after alignment analysis using Samtools v1.9 (Li et al., 2009) via the Galaxy Migale bioinformatics Facility platform (https://migale.inrae.fr).

## 2.6. Statistical analysis of survey data

Survey data were processed using R version 4.2.0 (April 2022) (R Development Core Team, 2022). Initially, Fisher's exact test was employed to compare viral prevalence between plots by assessing discrepancies between observed and expected virus frequencies. The test also evaluated whether the incidence of co-infections deviated from what would be expected by chance, based on the theoretical versus observed prevalence of viruses within co-infected samples.

To characterize plot typology, six qualitative variables potentially influencing virus presence or symptom expression were selected: type of planting material (VP or suckers), crop rotation (yes/no), residue shredding (yes/no), presence of neighboring pineapple plots (yes/no), and whether flowering induction treatment (FIT) was applied (yes/no). These variables were categorized as "qualitative technical itinerary (TI)." Additionally, two quantitative variables, altitude and plot age, were grouped under "quantitative TI".

Multiple factor analysis (MFA) was conducted using the MFA function from the FactoMineR package (Husson et al., 2008), incorporating both qualitative and quantitative TI variables. Euclidean distances between plots were computed from the resulting coordinates, followed by an ascending hierarchical classification. The optimal number of plot clusters was determined using the NbClust function from the NbClust package (Charrad et al., 2014), using the k-means method for partitioning.

To obtain a preliminary understanding of potential correlations between virus presence and symptom expression at the individual plant level, a standardized Principal Component Analysis (PCA) was conducted using all binary presence/absence variables for both viruses and symptoms. Symptom severity scores were recoded into binary values: scores of 2 ('moderate') and 3 ('severe') were categorized as 'presence', while a score of 0 ('absent') was classified as 'absence'. Due to the uncertainty associated with the 'weak' category (score of 1), these entries were treated as missing data (NA) in all subsequent analyses.

To explore specific associations between the presence of each virus and the occurrence of symptoms, Fisher's exact tests were carried out. Each test compared the proportion of symptomatic plants based on the detection of individual viruses, assuming independence between observations. Additionally, a power analysis was conducted to assess whether the sample size was adequate to support the validity of the statistical results, estimating the likelihood of a Type II error (i.e., failing to reject a false null hypothesis).

# 3. Results

## 3.1. Symptomatology, vectors and virus prevalence

Symptoms generally associated with MWP were monitored for each sampled plant (Supplementary Table 3) and classified according to their severity (Fig. 3).

The most frequently observed symptom was reddening (Fig. 3A), detected in all 15 surveyed plots and in 69 % of sampled plants (N = 310). Yellowing (N = 178; 40 %; Fig. 3B) and wilting (N = 173; 38 %; Fig. 3D) were also common, occurring across all plots. In contrast, dieback (N = 60; 13 %; Fig. 3E) and leaf curling (N = 32; 7 %; Fig. 3C) were less frequently recorded, observed in ten and eleven plots, respectively. Mealybug infestations (N = 20; 4 %; Fig. 3F) were limited to only four plots and affected a small number of plants.

The overall and plot-specific prevalence of viruses was assessed from the 450 collected samples (Fig. 2B–Supplementary Table 4). PBCOV was the most frequently detected virus, present in 87 % of samples (N =
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Severity of reddening (A), yellowing (B), leaf curling (C), wilting (D), dieback (E) symptoms, and mealybug infestations (F) across surveyed plots (N = 30 plants). Symptom severity is categorized as follows: 0 = no symptoms (light brown), 1 = weak symptoms (medium brown), 2 = moderate symptoms (darker brown), and 3 = severe symptoms (darkest brown). Mealybug infestation levels are classified as: 0 = no mealybugs (light brown), 1 = moderate infestation (1–10 mealybugs per plant, medium brown), and 2 = high infestation (>10 mealybugs per plant, dark brown). Histograms display the frequency distribution of symptom severity and mealybug presence across plots, expressed as relative frequencies (where 1.0 = 100%). Plots where vitroplants (VPs) were used as planting material are numbered in yellow. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

390), followed by PMWaV1 (78%; N = 350), PMWaV3 (44%; N = 200), and PMWaV2 (43%; N = 192). In total, 93% of samples (N = 419) were infected with at least one of these four viruses (Fig. 4). PBERV was not detected in any sample.

Only 7% of samples (N = 31) tested negative for all targeted viruses; all of these originated from plots P4 and P5, which were the only plots planted with vitroplants (VPs) and the only ones where PBCOV was entirely absent. In these VP plots, 48% of plants were infected with at

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Venn diagram showing the distribution of PMWaV1, PMWaV2, PMWaV3, and PBCOV in infected and co-infected samples (N = 419). The green color gradient (from light to dark) indicates increasing prevalence of each virus combination. The presence of mealybugs is marked with red hatching. Results from Fisher's exact tests ($\alpha$ = 0.05) comparing observed versus expected frequencies of virus combinations are highlighted: significantly overrepresented combinations are shown in orange, underrepresented combinations in blue, and non-significant results in black. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
least one virus, compared to 100 % infection in all other plots where suckers were used as planting material. Within plot P5, only PMWaV1 and PMWaV3 were detected. Overall, 76.2 % of samples (N = 343) showed co-infection with two to four viruses (Fig. 4).

Mixed infections involving PMWaV1, PMWaV2, PMWaV3, and PBCOV were the most frequent, detected in 105 samples (25.1 %) (Fig. 4). Co-infections of PMWaV1, PMWaV2, and PBCOV were observed in 83 samples (19.8 %), while PMWaV1, PMWaV3, and PBCOV were co-detected in 77 samples (18.4 %). Dual infections with PMWaV1 and PBCOV were found in 69 samples (16.5 %).

Single infections were relatively rare: PMWaV1 and PMWaV3 were each detected alone in 13 samples (3.1 %), all of which originated from VP-planted plots (P4 and P5). In contrast, single infections by PBCOV were observed in 50 samples (11.9 %). The two most prevalent viruses (i.e. PMWaV1 and PBCOV) were primarily found in co-infected samples, highlighting the high frequency of mixed viral infections in the surveyed plots.

Notably, several viral combinations were absent from the dataset, including four of the seven theoretically possible co-infections involving PMWaV2. In addition, PMWaV2 was never detected as a single infection. Fisher's exact tests revealed that certain combinations were significantly overrepresented (Fig. 4, shown in orange). These included quadruple infections involving PMWaV1, PMWaV2, PMWaV3, and PBCOV (25%; OR = 2.06; *p* < 0.001), single infections with PBCOV (11.9%; OR = 1.89; *p* < 0.001), and single infections with PMWaV3 (3.1%; OR = 4.44; *p* = 0.03). Conversely, some combinations were significantly under-represented (Fig. 4, shown in blue), such as mixed infections with PMWaV1 and PBCOV (16.5%; OR = 0.66; *p* = 0.02), as well as three combinations involving PMWaV2, all of which were entirely absent (0%; *p* < 0.001).

## 3.2. Correlations between virus presence, observed symptoms, and technical itineraries

Based on the survey data (Supplementary Table 1), the 15 surveyed plots were categorized into three distinct groups (Fig. 5). Group 1 comprises plots P4 and P5, both of which used vitroplants (VPs) as planting material. These plots shared several characteristics: an altitude of 400 m, an age of 18 months at the time of the first sampling, implementation of crop rotation, absence of flowering induction treatment (FIT), no residue shredding between crop cycles, and no adjacent pineapple fields. Group 2 consists of seven plots (P3, P8, P9, P10, P11, P12, and P13), all of which used suckers as planting material. At the time of first sampling, these plots were between 12 and 14 months old and

situated at altitudes ranging from 100 to 529 m. With the exception of P10, these plots had no adjacent pineapple fields. Most underwent FIT (P9, P11, P12, and P13), while residue shredding between crop cycles was practiced in three plots (P3, P9, and P12). Crop rotation was implemented in two plots (P8 and P13). Group 3 includes six plots (P1, P2, P6, P7, P14, and P15), also established with suckers as planting material. These plots were between 14 and 20 months old at the time of the first sampling and located at higher altitudes, ranging from 345 to 749 m. All were adjacent to pineapple fields. Residue shredding was practiced in all plots except P14, and FIT was applied in two plots (P6 and P14). None of the plots in this group had undergone crop rotation prior to planting.

The relationship between virus presence, observed symptoms, and technical itineraries was analyzed to search for potential correlations. A comparison of plot classifications based on agricultural practices, virus infection data, and symptom expression revealed no strong overall congruence, except for the two VP-derived plots (P4 and P5), which consistently clustered together across all classifications (Fig. 5D). This finding suggests that agricultural practices do not have a clearly interpretable impact on virus presence or symptomatology in the surveyed plots. While certain trends may exist, no major structured relationship was identified, indicating that additional factors beyond the recorded technical itineraries may influence virus distribution and symptom expression.

We investigated possible correlations between the presence of the four viruses detected in the 15 surveyed plots and the occurrence of MWP symptoms using a standardized principal component analysis (PCA) (Fig. 6).

Individuals contributed 22.1 % to axis 1, 16.1 % to axis 2, and 13.9 % to axis 3, accounting for a total of 51.9 % of the scatterplot's total inertia. While virus-related variables, particularly PMWaV1 and PMWaV2, were well projected onto axis 1, symptomatology variables such as dieback, reddening, and yellowing were primarily projected orthogonally onto axis 2. Wilting was better represented on axis 3 but remained the symptom variable contributing most to axis 1, making it the symptom most strongly associated with virus presence. Meanwhile, curling was mainly projected along axis 4 (data not shown). The presence of mealybugs was best projected onto axis 3, orthogonal to virus presence variables. The lack of alignment between virus presence and symptom projections suggests no clear or strong association between these variables, particularly for reddening and yellowing symptoms.

Consistent with virus prevalence observations and the predominant projection of viruses along axis 1, a gradient was observed from the least infected plots (VP-derived plots P4 and P5, on the left of axis 1) to the

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Classification of the fifteen surveyed plots based on (A) technical itineraries (TI), (B) virus prevalence, and (C) symptom occurrence. Quantitative variables (e. g., plot age, altitude, and virus prevalence) are represented using a color gradient, with darker shades indicating higher values. (D) Hierarchical clustering of plots based on the coordinates obtained from the multiple factor analysis (MFA) performed on technical itinerary variables. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 6. Standardized principal component analysis (PCA) of symptom and virus presence in pineapple plants. (A) and (B) present the projections of individuals (left) and variables on the correlation circle (Hooks et al., 2009) for two factorial planes: (A) axes 1 and 2, and (B) axes 1 and 3. In the individual plots, barycenters of plant projections for each plot are indicated by squares and are colored according to plot typology. Individual plants are colored based on their corresponding plot, following the color key provided below the figure. In the variable plots, a blue gradient indicates the quality of projection for virus presence variables (lighter blue = higher quality), while a brown gradient indicates the quality of projection for symptom presence variables. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

most infected plots (P8 and P14, on the right of axis 1). The coexistence of symptomatic but virus-free plants (P4 and P5, N = 5) and asymptomatic but virus-infected plants (distributed across all plots, N = 69) further suggests the absence of a strong, direct link between virus presence and agricultural practices.

To more precisely determine whether associations exist between virus presence and symptomatology, as suggested from the PCA, these variables were analyzed independently, excluding those related to plot characteristics and crop management (Table 3).

Contrasting results were observed depending on the virus and symptom. Dieback showed significant positive correlations with PMWaV2 (p = 4.72e-3) and PBCOV (p = 4.11e-2), and a marginally significant correlation with PMWaV3 (p = 5.07e-2). No correlation could be found for PMWaV1 (p = 1.82e-1); however, the low statistical

power (32%) suggests that an association might not be detectable given the sample size (51 PMWaV1-positive plants out of 60 affected by dieback). Wilting was strongly associated with PMWaV1 (p = 2.09e-3), PMWaV2 (p = 2.45e-17), and PMWaV3 (p = 4.05e-8), with high test power (92–100%), indicating a robust correlation. Curling exhibited a strong positive correlation with PMWaV1 (p = 5.13e-4, 100% power), since 97 virus-negative plants did not display this symptom. However, plants positive for PMWaV2, PMWaV3, or PBCOV that also showed curling were all co-infected with PMWaV1. The individual correlation of these three viruses with curling was not significant. Additionally, the low statistical power (0–45%) resulting from the small number of symptomatic plants may have affected the ability to detect associations. Yellowing was strongly and negatively correlated with the presence of all four viruses (p values comprised between 7.65e-3 and 4.40e-4). In

<caption>**Table 3**</caption>

Number and percentage of symptomatic pineapple plants exhibiting each symptom, stratified by the presence or absence of individual viruses. The total number of plants assessed for each symptom is indicated in black at the top right of each column. Fisher's exact test was used to evaluate associations between virus presence and symptom occurrence. Statistically significant positive associations are highlighted in black, and significant negative associations are shown in orange (p < 0.05; p < 0.01; p < 0.001). The statistical power of each test, representing the probability of correctly rejecting a false null hypothesis, is visualized using a green gradient (0%–100%), with pale green indicating low power, typically due to limited sample sizes.

<table><thead><tr><th rowspan="2">Virus</th><th rowspan="2">Number of plants</th><th rowspan="2">Infection status</th><th colspan="2">Dieback (60)</th><th colspan="2">Wilting (173)</th><th colspan="2">Yellowing (178)</th><th colspan="2">Leaf curling (32)</th><th colspan="2">Reddening (310)</th></tr><tr><th>Percentage</th><th>Number</th><th>Percentage</th><th>Number</th><th>Percentage</th><th>Number</th><th>Percentage</th><th>Number</th><th>Percentage</th><th>Number</th></tr></thead><tbody><tr><td rowspan="2">PMWaV1</td><td>100</td><td>non-infected</td><td>9.00</td><td>9/100</td><td>29.49</td><td>23/78</td><td>65.33</td><td>49/75</td><td>0.00</td><td>0/97</td><td>94.44</td><td>68/72</td></tr><tr><td>350</td><td>infected</td><td>14.74</td><td>51/346</td><td>49.34**</td><td>150/304</td><td>42.16***</td><td>129/306</td><td>9.28***</td><td>32/345</td><td>83.74*</td><td>242/289</td></tr><tr><td rowspan="2">PMWaV2</td><td>298</td><td>non-infected</td><td>9.38</td><td>24/256</td><td>25.84</td><td>54/209</td><td>53.96</td><td>109/202</td><td>6.72</td><td>17/253</td><td>87.43</td><td>160/183</td></tr><tr><td>192</td><td>infected</td><td>18.95**</td><td>36/190</td><td>68.79***</td><td>119/173</td><td>38.55**</td><td>69/179</td><td>7.94</td><td>15/189</td><td>84.27</td><td>150/178</td></tr><tr><td rowspan="2">PMWaV3</td><td>250</td><td>non-infected</td><td>10.53</td><td>26/247</td><td>32.70</td><td>69/211</td><td>54.33</td><td>113/208</td><td>5.67</td><td>14/247</td><td>87.88</td><td>174/198</td></tr><tr><td>200</td><td>infected</td><td>17.09</td><td>34/199</td><td>60.82***</td><td>104/171</td><td>37.57**</td><td>65/173</td><td>9.23</td><td>18/195</td><td>83.44</td><td>136/163</td></tr><tr><td rowspan="2">PBCOV</td><td>60</td><td>non-infected</td><td>5.00</td><td>3/60</td><td>54.29</td><td>19/35</td><td>70.97</td><td>22/31</td><td>3.57</td><td>2/56</td><td>86.67</td><td>26/30</td></tr><tr><td>390</td><td>infected</td><td>14.77*</td><td>57/386</td><td>44.38</td><td>154/347</td><td>44.57**</td><td>156/350</td><td>7.77</td><td>30/386</td><td>85.80</td><td>284/331</td></tr><tr><td>No virus</td><td>31</td><td>non-infected</td><td>3.23</td><td>1/31</td><td>55.56</td><td>10/18</td><td>75.00</td><td>12/16</td><td>0.00</td><td>0/31</td><td>86.67</td><td>13/15</td></tr><tr><td>At least one virus</td><td>419</td><td>infected</td><td>14.2*</td><td>59/415</td><td>44.80</td><td>163/364</td><td>45.5*</td><td>166/365</td><td>0.08*</td><td>32/411</td><td>85.80</td><td>297/346</td></tr></tbody></table>

<footnote>* : Fisher test significant</footnote>
PMWaV1-negative plants, 65 % exhibited yellowing, compared to 42 % in PMWaV1-positive plants (p = 4.40e-4). Similar results were found for the other three viruses, with high power calculations (88–99 %) supporting the validity of these findings. Finally, reddening also showed a negative correlation with PMWaV1 (p = 2.19e-2, 74 % power), but no significant associations were found for the other viruses. However, the low statistical power (2–40 %) for these cases suggests that the test had limited potential to detect differences.

When comparing virus-free plants to those infected with at least one virus (Table 3, last row), a significant positive correlation was observed for wilting (p = 0.044). The probability of wilting was 14 % in virus-infected plants, compared to just 3 % in uninfected plants. However, the low statistical power (28 %) again suggests caution in interpreting this result. While wilting was significantly associated with all three ampeloviruses individually, no overall correlation was found between the presence of any virus and this symptom. This might be attributed to the widespread presence of PBCOV, which does not correlate with wilting. For curling, a positive correlation was observed in plants infected with at least one virus (p = 0.028). However, the test had near to zero power, meaning the low number of symptomatic plants prevented a reliable statistical validation.

### 3.3. Assessment of the presence of additional viruses

We investigated the potential presence of viruses other than PMWaV1, PMWaV2, PMWaV3, PBERV, and PBCOV in ten symptomatic pineapple plants from plot P5, sampled in December 2021, five years after planting. These plants, derived from vitroplants (VPs), tested negative for the targeted viruses but exhibited moderate to severe symptoms: reddening (N = 9), wilting (N = 4), yellowing (N = 2), and leaf curling (N = 1) (Table 4). Four plants displayed multiple symptoms: E22 showed wilting, yellowing, and reddening; E24 and E29 had wilting and reddening; and E25 showed wilting, leaf curling, and reddening. Mealybugs were observed on five plants (E9, E10, E11, E13, and E25). Flowering induction treatment (FIT) had been applied to the plot (P5) approximately six months before sampling. Additionally, it was located near another pineapple field established using suckers. To identify potential viral agents, high-throughput sequencing (HTS) was performed on these samples, along with alien controls.

High-throughput sequencing (HTS) of the banana control sample yielded contigs matching banana streak OL virus (NC_003381; N = 15; 276 reads), banana streak GF virus (NC_007002; N = 18; 688 reads), and banana mild mosaic virus (NC_002729; N = 2; 9501 reads). The sweet potato control showed contigs matching sweet potato feathery mottle virus (NC_001841; N = 8; 3188 reads). No cross-contamination was detected between control and pineapple samples.

HTS of the pineapple samples generated between 8,537,418 and 21,323,665 reads per sample. De novo assembly of ~112 million paired reads (~33.6 Mb) produced 13,614 contigs ranging from 74 to 6384 bp. No viral contigs were identified in the assembled sequences through similarity searches against viral databases. However, 13 reads from sample E29, not assembled into contigs, displayed sequence similarity to

PMWaV3, despite this sample having tested negative by RT-PCR. This discrepancy may reflect a viral load below the RT-PCR detection threshold.

Overall, the HTS analysis did not reveal any additional viral agents in the symptomatic samples. These findings suggest that the observed symptoms in the absence of detectable viruses could result from factors other than viral infection, highlighting the potential for non-viral causes or viral loads below diagnostic sensitivity to trigger symptom development.

### 3.4. Assessment of the potential of pineapple VPs to control viral infections

Virus prevalence and symptom expression were monitored over up to three crop cycles in five pineapple lines (L1 to L5) derived from vitro-plants (VPs) and cultivated in five distinct locations. Only PMWaV1 and PMWaV3 were detected during this period; PMWaV2, PBCOV, and PBERV were not observed in any of the monitored lines (Table 2). This longitudinal monitoring provided valuable insights into the dynamics of PMWaV1 and PMWaV3 contamination.

In certain lines, such as L1, virus incidence remained very low even after three crop cycles. Among the 60 L1 plants analyzed, only a single plant tested positive for PMWaV1 but was asymptomatic. In line L2, both PMWaV1 and PMWaV3 were detected four years after planting in 15 out of 187 samples. This delayed and limited contamination likely originated from nearby fields where suckers were used as planting material. In contrast, lines L3, L4, and L5 exhibited higher virus prevalence after only a single crop cycle. Nonetheless, infection levels in these lines remained lower than those observed in plots established with suckers. These findings suggest that while VPs can initially reduce viral diversity and prevalence, their long-term effectiveness as a virus control strategy needs to be further assessed. However, the progressive increase in viral contamination over successive crop cycles highlights the necessity of implementing complementary control measures such as vector management, strict field isolation, and routine virus indexing to fully realize the benefits of VPs in reducing viral infections in pineapple crops.

## 4. Discussion

In our sampling, the highest prevalence were observed for PBCOV (87 %) and PMWaV1 (78 %), followed by PMWaV3 (44 %) and PMWaV2 (43 %). These findings are consistent with those reported in a previous survey conducted in Australia, where PMWaV1 and PBCOV were also the most prevalent viruses, followed by PMWaV3 and PMWaV2 (Gambley, 2008; Gambley et al., 2008b). However, studies conducted in Colombia (Moreno et al., 2023), Peru (Carrasco-Lozano et al., 2023), Cuba (Hernández-Rodríguez et al., 2017), Hawaii (Sether and Hu, 2002), and Ghana (Asare-Bediako et al., 2020) have reported differing patterns of virus prevalence, underscoring the geographic variability in virus dynamics. These discrepancies may stem from differences in the history of virus introduction, local vector populations, and agronomic practices, as well as in the evolving composition of the viral complex

<table><caption>**Table 4**
Symptoms, mealybug counts, number of reads and their taxonomic assignment for the ten samples analyzed using HTS.</caption><thead><tr><th>Sample</th><th>Dieback</th><th>Wilting</th><th>Yellowing</th><th>Leaf curling</th><th>Reddening</th><th>Mealybug counts</th><th>Total reads</th><th>Mbases</th><th>BLASTn >3 viral database (Id)</th><th>Nb reads assigned</th></tr></thead><tbody><tr><td>E9</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>2</td><td>89,94,782</td><td>2698</td><td>0</td><td>0</td></tr><tr><td>E10</td><td>0</td><td>0</td><td>0</td><td>0</td><td>3</td><td>1</td><td>1,00,71,116</td><td>3021</td><td>0</td><td>0</td></tr><tr><td>E11</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>2</td><td>1,05,12,677</td><td>3154</td><td>0</td><td>0</td></tr><tr><td>E13</td><td>0</td><td>0</td><td>0</td><td>0</td><td>3</td><td>1</td><td>1,02,87,507</td><td>3086</td><td>0</td><td>0</td></tr><tr><td>E18</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td><td>98,12,127</td><td>2944</td><td>0</td><td>0</td></tr><tr><td>E2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td><td>85,37,418</td><td>2561</td><td>0</td><td>0</td></tr><tr><td>E22</td><td>0</td><td>1</td><td>2</td><td>0</td><td>1</td><td>0</td><td>1,15,29,605</td><td>3459</td><td>0</td><td>0</td></tr><tr><td>E24</td><td>0</td><td>2</td><td>0</td><td>0</td><td>2</td><td>0</td><td>1,09,28,612</td><td>3279</td><td>0</td><td>0</td></tr><tr><td>E25</td><td>0</td><td>2</td><td>0</td><td>2</td><td>3</td><td>1</td><td>2,13,23,665</td><td>6397</td><td>0</td><td>0</td></tr><tr><td>E29</td><td>0</td><td>1</td><td>0</td><td>0</td><td>2</td><td>0</td><td>99,50,184</td><td>2985</td><td>NC_043406 (PMWaV3)</td><td>13</td></tr></tbody></table>
associated with MWP. Collectively, these factors likely shape distinct epidemiological landscapes across regions and over time.

No strong correlation between technical practices and the occur-
rence of MWP symptoms were revealed from statistical analyses. How-
ever, several symptoms were significantly associated with viral
infections. Overall, dieback and wilting emerged as the symptoms most
consistently linked to viral infections. Associations between specific
viral infections and symptoms were uncovered. Dieback was signifi-
cantly associated with PMWaV2 and PBCOV, and marginally with
PMWaV3, while no clear association was observed with PMWaV1.
Nevertheless, the low statistical power for PMWaV1 suggests that a
potential association may have gone undetected. Wilting showed strong
and statistically robust correlations with PMWaV1, PMWaV2, and
PMWaV3, supported by high test power. Leaf curling was significantly
linked to PMWaV1, with no such cases observed in virus-negative plants.
In contrast, PMWaV2, PMWaV3, and PBCOV were not individually
associated with leaf curling, likely due to co-infections involving
PMWaV1.

Interestingly, yellowing was negatively associated with all four vi-
ruses, being more frequently observed in virus-free plants. Reddening
also showed a negative correlation with PMWaV1, while no significant
associations were found for the other viruses, possibly due to limited
statistical power. These findings indicate that the etiology of yellowing
and reddening, particularly in the 'Queen Victoria' cultivar on Réunion
Island, remains unclear. When comparing virus-free plants to those
infected with at least one virus, wilting appeared more frequently in
infected plants, though the low statistical power calls for cautious
interpretation. Leaf curling was positively associated with infection, but
the limited number of symptomatic cases restricted statistical
validation.

Mixed infections were the norm in our sampling (Fig. 4), a common
observation in vegetatively propagated crops like pineapple. Because
most plant viruses are not seed-transmitted, the absence of sexual
reproduction in these crops prevents natural sanitation, allowing virus
accumulation over successive generations (Bradamante et al., 2021).
Mixed infections can lead to either synergistic or antagonistic in-
teractions between viruses, resulting in more severe or attenuated
symptoms, respectively.

In our dataset, the frequent co-occurrence of mixed infections sug-
gests a lack of competitive exclusion among viruses (i.e., the presence of
one virus did not inhibit subsequent infection by others; Supplementary
Table 5). This absence of exclusion points to a permissive host envi-
ronment and highlights the complexity of viral ecology in pineapple.
Although virus-virus interactions in pineapple remain poorly under-
stood, our correlation-based analyses offer preliminary insights into
potential synergistic mechanisms in plants showing MWP symptoms. In
particular, strong associations between wilting and the three ampelo-
viruses (PMWaV1, PMWaV2, and PMWaV3) suggest that these viruses
may act synergistically. Leaf curling, by contrast, was significantly
associated only with PMWaV1, and all cases involving other viruses
(PMWaV2, PMWaV3, or PBCOV) occurred in co-infection with
PMWaV1. This implies that PMWaV1 may be the primary causal agent of
leaf curling, while other viruses contribute indirectly via co-infection.
Similarly, dieback was associated with PMWaV2 and PBCOV, with a
potential role for PMWaV3, further suggesting complex interviral
dynamics.

Overall, our findings support the hypothesis that MWP symptom
expression in pineapple, particularly in the ‘Queen Victoria’ cultivar, is
driven by intricate interactions among multiple viruses rather than by
any single agent. Experimental validation of these interactions remains a
critical next step, though currently limited by the lack of reliable virus
inoculation protocols in pineapple.

The observed negative correlation between yellowing and reddening
symptoms and the presence of viruses raises the possibility that some
viral infections may play a protective role under certain conditions.
Previous research has shown that viruses can, in specific contexts,

engage in mutualistic interactions with their plant hosts by enhancing
tolerance to environmental stress. For example, turnip mosaic virus
(TuMV) has been reported to improve water stress tolerance in *Nicotiana
benthamiana* and *Arabidopsis thaliana* (González et al., 2021). Similarly,
virus-infected plants have demonstrated increased resilience to various
abiotic stresses, including extreme temperatures, high salinity, and
combined stress factors (Prasad et al., 2022). In pineapple, one could
hypothesize that viral infections may mitigate the effects of certain bi-
otic stresses (e.g., mealybug infestations or fungal pathogens) (Py et al.,
1984; Rohrbach and Johnson, 2003) or abiotic pressures (e.g., nutrient
deficiencies, drought, or flowering induction treatments) (Carter,
1945a; Malézieux et al., 2003; Py et al., 1984).

MWP presents with variable and often nonspecific symptoms, which complicates diagnosis. These symptoms are not reliably linked to specific pathogens or virus combinations (e.g., PMWaV2 or PMWaV1/PMWaV3), and may also arise from environmental conditions or secondary infections. The multifactorial nature of MWP, including contributions from biotic factors like mealybugs and abiotic stressors, makes accurate virological diagnosis particularly challenging.

While MWP-associated viruses are transmitted by mealybugs, their
role in virus dissemination remains difficult to quantify in this study due
to their limited visibility. Mealybugs typically inhabit leaf axils and root
zones (Beardsley, 1959; McKenzie, 1967), areas that may have been
under-sampled following the non-destructive methods used here.
Furthermore, transmission efficiency for ampeloviruses and badnavi-
ruses in pineapple has yet to be evaluated in controlled studies, repre-
senting a significant gap in current understanding.

The use of virus-free vitroplants (VPs) as planting material emerges
as a promising strategy to reduce initial viral loads in pineapple culti-
vation, mirroring successful approaches in the banana industry.
Although only a few VP plots were included in our monitoring, the re-
sults were encouraging: four years after planting, 75 % of sampled plants
remained virus-free, and infections were limited to PMWaV1 and
PMWaV3. Notably, PBCOV (highly prevalent in fields planted with
suckers) was absent from VP plots. These findings suggest that VPs may
help prevent the introduction and spread of key viruses, including those
associated with MWP.To fully assess the long-term benefits of VPs and
their role in managing pineapple viral diseases, further monitoring
across more plots and successive crop cycles is essential. Such studies
would strengthen evidence for VP-based management strategies and
inform best practices for sustainable pineapple production.

In conclusion, this study provides the first comprehensive overview
of viruses associated with mealybug wilt of pineapple (MWP) in Reunion
Island, revealing the widespread distribution of PMWaVs and badnavi-
ruses, often occurring in mixed infections. Although PBERV was not
detected, the high prevalence of other viruses and their partial associ-
ation with field symptoms highlight the multifactorial nature of MWP
and suggest that additional biotic or abiotic factors may contribute to its
expression. Importantly, our findings confirm that the use of vitroplants
as planting material can substantially reduce viral prevalence, demon-
strating their value as a practical tool to limit early-stage infections.
However, sustainable management of MWP will require complementary
strategies, including vector control, field isolation, and routine virus
indexing. Furthermore, controlled biological experiments are needed to
clarify the complex interactions between viruses, vectors, and host
plants that underpin this disease. Collectively, these findings provide
essential insights into the epidemiology of MWP and reinforce the cen-
tral role of virus-free planting material within integrated management
strategies for pineapple cultivation.

CRediT authorship contribution statement

**Delphine Massé:** Writing – original draft, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

**Frédéric Chiroleu:** Supervision, Methodology, Formal analysis.

**Nathalie Cassam:** Formal analysis. **Bruno Hostachy:** Supervision,
**Funding acquisition.** Jean-Michel Lett: Writing – review & editing, Validation, Supervision, Conceptualization. **Pierre Lefeuvre:** Writing – review & editing, Validation, Supervision, Methodology, Formal analysis, Conceptualization. **Pierre-Yves Teychene:** Writing – review & editing, Supervision.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Delphine Masse reports was provided by 97080. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

Pineapple field survey was conducted in collaboration with FDGDON (Fédération Départementale des Groupements de Défense contre les Organismes Nuisibles), DAAF Réunion (Direction de l'Agriculture et de la Forêt), and LSV Réunion (Laboratoire de la Santé des Végétaux, Anses). This work was realized with the support of MESO@LR-Platform at the University of Montpellier. P-Y.T., J-M.L. and F.C. are funded by the European Regional Development Fund (ERDF, contract REU005756) and the Conseil Régional de La Réunion through the project "Dispositif de partenariat en santé et biodiversité"). For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.cropro.2025.107440.

Data availability

Data will be made available on request.

References

Asare-Bediako, E., Nyarko, J., van der Puije, G.C., 2020. First report of pineapple
mealybug wilt associated virus-2 infecting pineapple in Ghana. New Dis. Rep. 41.

Ayenew, B., Tadesse, T., Gebremariam, E., Mengesha, A., Tefera, W., 2013. Efficient use of temporary immersion bioreactor (TIB) on pineapple (Ananas comosus L.) multiplication and rooting ability. J. Microbiol. Biotechnol. Food Sci. 2, 2456-2465.

Bankevich, A., Nurk, S., Antipov, D., Gurevich, A.A., Dvorkin, M., Kulikov, A.S., Lesin, V. M., Nikolenko, S.I., Pham, S., Prjibelski, A.D., Pyshkin, A.V., Sirotkin, A.V., Vyahni, N., Tesler, G., Alekseyev, M.A., Pevzner, P.A., 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455-477.

Beardsley, J.W., 1959. On the Taxonomy of Pineapple Mealybugs in Hawaii, with a Description of a Previously Unnamed Species (Homoptera: Pseudococcidae).

Bradamante, G., Mittelsten Scheid, O., Incarbone, M., 2021. Under siege: virus control in plant meristems and progeny. Plant Cell 33, 2523-2537.

Carrasco-Lozano, E.C., Carrillo-Ordónez, G.A., Gamarra-Gamarra, D., Torres-Suarez, G., Villar, C.M., Stange, C., Valverde, R.A., 2023. Pineapple mealybug wilt-associated viruses 1, 2, and 3 are associated with mealybug wilt disease of pineapple in Peru. J. Plant Pathol. 105, 581-586.

Carter, W., 1933. The pineapple mealybug, Pseudococcus brevipes, and wilt of pineapples. Phytopathology 23, 207-242.

Carter, W., 1935. Studies on the biological control of Pseudococcus brevipes and wilt of pineapple. Phytopathology 28, 1037-1041.

Carter, W., 1945a. The influence of plant nutrition on susceptibility of pineapple plants to mealybug wilt. Phytopathology 35, 316-323.

Carter, W., 1945b. The oral secretions of the pineapple mealybug. J. Econ. Entomol. 38, 335-338.

Charrad, M., Ghazzali, N., Boiteau, V., Niknafs, A., 2014. NbClust: an R package for determining the relevant number of clusters in a data set. J. Stat. Software 61, 1-36.

Gambley, C.F., 2008. Aetiology of pineapple mealybug wilt disease in Australia. School of Integrative Biology. The University of Queensland, p. 167.

Gambley, C.F., Geering, A.D.W., Steele, V., Thomas, J.E., 2008a. Identification of viral and non-viral reverse transcribing elements in pineapple (Ananas comosus), including members of two new badnavirus species. Arch. Virol. 153, 1599-1604.

Gambley, C.F., Steele, V., Geering, A.D.W., Thomas, J.E., 2008b. The genetic diversity of ampeloviruses in Australian pineapples and their association with mealybug wilt disease. Australas. Plant Pathol. 37, 95-105.

González, R., Butković, A., Escaray, F.J., Martínez-Latorre, J., Melero, Í., Pérez-Parets, E., Gómez-Cadenas, A., Carrasco, P., Elena, S.F., 2021. Plant Virus Evolution Under Strong Drought Conditions Results in a Transition from Parasitism to Mutualism, 118. Proceedings of the National Academy of Sciences, e2020990118.

Hernández-Rodríguez, L., Nápoles, L., Pérez-Vicente, L.F., Laffitte, O., Ruiz, M., Llanes-Alvarez, Y., Zamora-Rodríguez, V., 2017. Infección de Pineapple mealybug wilt-associated virus 1, 2 y 3 en plantas de piña, híbrido 'MD-2' en Ciego de Ávila. Revista Centro Agrícola 44, 52-60.

Hernandez-Rodriguez, L., Ramos-Gonzalez, P.L., Garcia-Garcia, G., Zamora, V., Peralta-Martin, A.M., Peña, I., Perez, J.M., Ferriol, X., 2014. Geographic distribution of mealybug wilt disease of pineapple and genetic diversity of viruses infecting pineapple in Cuba. Crop Prot. 65, 43-50.

Hooks, C.R., Fukuda, S., Perez, E.A., Manandhar, R., Wang, K.H., Wright, M.G., Almeida, R.P., 2009. Aphid transmission of banana bunchy top virus to bananas after treatment with a bananacide. J. Economic Entomol. 102, 493-499.

Husson, F., Josse, J., Lè, S., 2008. FactoMineR: an R package for multivariate analysis. J. Stat. Software 25.

Larrea-Sarmiento, A., Olmedo-Velarde, A., Green, J.C., Al Rwahnih, M., Wang, X., Li, Y. H., Wu, W., Zhang, J., Matsumoto, T.K., Suzuki, J.Y., Wall, M.M., Borth, W., Melzer, M.J., Hu, J.S., 2020. Identification and complete genomic sequence of a novel sadwavirus discovered in pineapple (Ananas comosus). Arch. Virol. 165, 1245-1248.

Larrea-Sarmiento, A., Olmedo-Velarde, A., Wang, X., Borth, W., Matsumoto, T.K., Suzuki, J.Y., Wall, M.M., Melzer, M., Hu, J., 2021. A novel ampelovirus associated with mealybug wilt of pineapple (Ananas comosus). Virus Genes 57, 464-468.

Larrea-Sarmiento, A.E., Olmedo-Velarde, A., Wang, X., Borth, W., Domingo, R., Matsumoto, T.K., Suzuki, J.Y., Wall, M.M., Melzer, M.J., Hu, J., 2022. Genetic diversity of viral populations associated with ananas germplasm and improvement of virus diagnostic protocols. Pathogens (Basel, Switzerland) 11.

Li, H., Durbin, R., 2009. Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics (Oxford, England) 25, 1754-1760.

Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., Subgroup, G.P.D.P., 2009. The sequence alignment/Map format and SAMtools. Bioinformatics (Oxford, England) 25, 2078-2079.

Malézieux, E., Cote, F., Bartholomew, P., 2003. Crop environment, plant growth and physiology. In: The Pineapple, Botany, Production and Uses. Chapter 5.

Massart, S., Adams, I., Al Rwahnih, M., Baeyen, S., Bilodeau, G.J., Blouin, A.G., Boonham, N., Candresse, T., Chandellier, A., De Jonghe, K., 2022. Guidelines for the reliable use of high throughput sequencing technologies to detect plant pathogens and pests. Peer Community J. 2.

Massé, D., Candresse, T., Filloux, D., Massart, S., Cassam, N., Hostacy, B., Marais, A., Fernandez, E., Roumagnac, P., Verdin, E., Teychene, P.Y., Lett, J.M., Lefeuvre, P., 2024. Characterization of six ampeloviruses infecting pineapple in Reunion Island using a combination of high-throughput sequencing approaches. Viruses 16.

Massé, D., Filloux, D., Candresse, T., Massart, S., Marais, A., Verdin, E., Cassam, N., Fernandez, E., Roumagnac, P., Teychene, P.Y., Lefeuvre, P., Lett, J.M., 2022. Identification of a novel vitivirus from pineapple in Reunion island. Arch. Virol. 167, 2355-2357.

McKenzie, H.L., 1967. Mealybugs of California. Univ of California Press.

Moreno, I., Rodríguez-Arévalo, K.A., Tarazona-Velásquez, R., Kondo, T., 2023. Occurrence and distribution of pineapple mealybug wilt-associated viruses (PMWaVs) in MD2 pineapple fields in the Valle del Cauca Department, Colombia. Tropical Plant Pathol. 48, 217-225.

Nurbel, T., Soler, A., Thuriés, L., Dorey, E., Chabanne, A., Tisserand, G., Hoarau, L., Darnaudery, M., 2021. ANANABIO - concevoir des systèmes de production d'ananas en agriculture biologique. Innov. Agronomiques 82, 1-24.

Oculi, J., Bua, B., Ocwa, A., 2020. Reactions of pineapple cultivars to pineapple heart rot disease in central Uganda. Crop Prot. 135, 105213.

Perez, E., Sether, D., Melzer, M., Busto, J., Nagai, C., Hu, J., 2005. Characterization and control of pineapple mealybug wilt associated ampeloviruses. V Int. Pineapple Sym. 702, 23-27.

Peron, F.N., Calloni, R., Ventura, J.A., Fernandes, P.M.B., 2019. Bioinformatics approach to the study of the molecular behaviour of mealybug wilt of pineapple. Acta Hortic. 177-184, 2019 v. no.1239.

Prasad, A., Sett, S., Prasad, M., 2022. Plant-virus-abiotic stress interactions: a complex interplay. Environ. Exp. Bot. 199, 104869.

Py, C., Lacoeuilhe, J.-J., Teisson, C., 1984. L'Ananas : Sa Culture, Ses Produits.

Rohrbach, K.G., Johnson, M.W., 2003. Pests, Diseases and Weeds, the Pineapple: Botany, Production and Uses. CABI publishing, Wallingford UK, pp. 203-251.

Rong, W., Rollin, J., Hanafi, M., Roux, N., Massart, S., 2023. Validation of high-throughput sequencing as virus indexing test for musa germplasm: performance criteria evaluation and contamination monitoring using an alien control. PhytoFrontiers™ 3, 91-102.

Santa-Cecília, L., Prado, E., Souza, B., 2016. Probing behavior of Dysmicoccus brevipes mealybug in pineapple plants1. Pesqui. Agropecuária Trop. 46, 458-463.

Sether, D.M., Hu, J.S., 2002. Closterovirus infection and mealybug exposure are necessary for the development of mealybug wilt of pineapple disease. Phytopathology 92, 928-935.

Soler, A., Nurbel, T., Masson, J., Hoarau, I., Tisserand, G., Thuries, L., Chabanne, A., Dorey, E., Chillet, M., 2020. Livret Technique ANANABIO : Innovations Techniques Pour La Culture De L'Ananas En Agriculture Biologique À La Réunion.