# Metabolomic datasets of an apple progeny carrying resistance quantitative trait loci to apple scab before or after inoculation of the pathogen Venturia inaequalis

Romane Lapous, Florent Magot, Romain Larbat, Caroline Denancé, Christian Cattanéo, Hélène Muranty, Charles-Eric Durel, Julie Ferreira de Carvalho

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## ► To cite this version:

Romane Lapous, Florent Magot, Romain Larbat, Caroline Denancé, Christian Cattanéo, et al. Metabolomic datasets of an apple progeny carrying resistance quantitative trait loci to apple scab before or after inoculation of the pathogen Venturia inaequalis. Data in Brief, 2025, 60, pp.111566. 10.1016/j.dib.2025.111566. hal-05069503

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Data Article

# Metabolomic datasets of an apple progeny carrying resistance quantitative trait loci to apple scab before or after inoculation of the pathogen *Venturia inaequalis*

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Romane Lapous<sup>a</sup>, Florent Magot<sup>b,1</sup>, Romain Larbat<sup>a</sup>, Caroline Denancé<sup>a</sup>, Christian Cattanéo<sup>a</sup>, Hélène Muranty<sup>a</sup>, Charles-Eric Durel<sup>a</sup>, Julie Ferreira de Carvalho<sup>a,*</sup>

ARTICLE INFO

Article history:
Received 22 January 2025
Revised 9 April 2025
Accepted 11 April 2025
Available online 16 April 2025

Dataset link: *Metabolomic dataset of an apple progeny carrying resistance quantitative trait loci to apple scab without or after inoculation of the pathogen Venturia inaequalis* (Original data)

Keywords:
*Malus domestica*
QTL
Genetic resistance mechanisms
Biotic interactions
Metabolomics

A B S T R A C T

Phytosanitary treatments are massively used in orchards to fight apple scab, a disease caused by the fungus *Venturia inaequalis* (Vi). To reduce these treatments, resistant varieties are largely deployed but their effectiveness can decrease over time. The combination of complementary molecular mechanisms within new varieties could enhance the durability of genetic resistance however, the underlying resistance mechanisms remain poorly understood. An apple pseudo-F1 progeny was previously widely investigated for its quantitative trait loci (QTL) controlling resistance to scab and at least three of them seem to act complementarily; notably, one of them is specific to some Vi isolates while the others have a broader spectra of action. The aim of this approach is to better understand the underlying molecular mechanisms and metabolites associated with resistance alleles by exploring apple leaf specialized metabolism. A total of three experiments was conducted: one experiment included non-inoculated leaves whereas in the two other experiments, leaf samples were collected five days after inocu-

<footnote><sup>a</sup> University of Angers, Institut Agro, INRAE, IRHS, SFR QUASAV, F-49000 Angers, France</footnote>

<footnote><sup>b</sup> Université de Lorraine, LAE, INRAE, F-54000 Nancy, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: julie.ferreira-de-carvalho@inrae.fr (J. Ferreira de Carvalho).</footnote>

<footnote n="1">¹ Present address: Université de Tours, Biomolécules et Biotechnologies Végétales, EA2106 F-37200 Tours, France</footnote>
lation with two different Vi isolates, including one known to overcome one QTL. Metabolic content was extracted in aqueous methanol before performing an untargeted metabolic analysis using an Orbitrap IDX™ mass spectrometer, allowing high-resolution mass spectrometry (HRMS) detection. This approach without a priori enables the detection of potentially new chemical families involved in resistance to apple scab. The current data article includes 1) the protocol of plant sample production with a table summarizing key elements of the experimental designs, 2) overview of the raw metabolomic profiles from all three experiments and 3) assessment of metabolic feature reproducibility between replicates in each dataset through Principal Component Analysis. The raw data files are available on the recherche.data.gouv repository (10.57745/XJBD8V). These datasets are valuable resources to further investigate the molecular mechanisms underlying genetic resistance to apple scab, with a focus on specialized metabolism. In the long term, it should improve apple breeding strategies by informing on how to combine appropriate genetic and biochemical factors in new varieties to ensure a more durable resistance.

© 2025 The Author(s). Published by Elsevier Inc.

This is an open access article under the CC BY license

(http://creativecommons.org/licenses/by/4.0/)

## Specifications Table

<table><tr><td>Subject</td><td>Agricultural Sciences: Horticulture</td></tr><tr><td>Specific subject area</td><td>Metabolomics and genetic resistance to biotic stress using two distinct isolates of a pathogen.</td></tr><tr><td>Type of data</td><td>Tables/figures (.pptx), raw metabolomic profiles of samples from all three experiments including sample IDs, experimental information and quantification of all metabolic features (.csv) and analytic workflow for metabolomic analyses (.txt)</td></tr><tr><td>Data collection</td><td>Data were obtained from leaves of grafted apple trees carrying QTL for apple scab resistance and artificially inoculated -or not- with two isolates of Vi in three independent experiments. Metabolic content of leaves was extracted in aqueous methanol and analysed using a Vanquish UHPLC system equipped with a XB-C18 Kinetex column. HRMS¹ detection was performed on an Orbitrap IDX™ mass spectrometer with ESI in positive and negative modes. HRMS² analysis was performed on pooled-sample vial using DDA mode and the AcquireX data acquisition workflow (ThermoFisher).</td></tr><tr><td>Data source location</td><td>Institution: Research Institute for Horticulture and Seeds (IRHS), University of Angers, Institut Agro, INRAE<br>City/Town/Region: 49071 Beaucouzé<br>Country: France<br>Latitude and longitude for collected samples/data: Not available</td></tr><tr><td>Data accessibility</td><td>Repository name: Entrepôt-Catalogue Recherche Data Gouv<br>(https://entrepot.recherche.data.gouv.fr/)<br>Data identification number: doi: 10.57745/XJBD8V<br>Direct URL to data: 10.57745/XJBD8V</td></tr><tr><td>Related research article</td><td>None</td></tr></table>

## 1. Value of the Data

* These metabolomic datasets can be used to identify novel metabolic pathways associated to resistance QTL (rQTL), either constitutively expressed or in response to two isolates of Vi.
* Knowing the molecular mechanisms underlying rQTL can assist further research in apple breeding by pyramiding sources of resistance which complement each other, in order to improve long-term resistance. Notably, it will provide information needed for breeders to identify interesting genotypes to integrate in breeding schemes.

* These data can be analysed with univariate, bivariate and multivariate methods. It can also be analysed by combining genotypic data of the progeny in order to determine the genetic architecture of specialized metabolism.

* By employing an untargeted approach, unexplored chemical families may be retrieved from these datasets. To the best of our knowledge, this is one of the first datasets allowing the study of genetic determinism of untargeted metabolic features after infection of apple trees with scab strains.

## 2. Background

Diversifying the molecular mechanisms associated to resistance in new varieties by pyramiding both qualitative and quantitative resistance genes might enhance the durability of genetic resistance [1]. While major-effect R genes are usually associated with pathogen perception [2], functions of resistance quantitative trait loci (rQTL) remain poorly understood. Previous studies highlighted the complementary effects of three rQTL to apple scab [3,4], segregating in a biparental population known as 'TxF progeny' [3]. The rQTL 'qT1', coming from the hybrid TN10-8, is considered as a 'major-gene like' rQTL due to its isolate-specificity and strong resistance symptom induction. In addition to these characteristics, qT1 co-localized with the major *Rvi6* gene, suggesting that it could be an allele or paralog of *Rvi6* with quantitative effect [5-7]. On the other hand, 'qF11' and 'qF17' (from the cultivar 'Fiesta') interact together synergistically, which can be interpreted as the complementary action of two genes involved in a common metabolic pathway [3,8]. To further understand the molecular mechanisms underlying these QTLs, we studied the metabolome of the 'TxF progeny' before and after inoculation with two isolates, including one known to overcome 'qT1'. These datasets will allow identifying specific metabolites associated with resistance to apple scab for each of these QTL.

## 3. Data Description

Metabolomic datasets were acquired from three distinct experiments ('No inoc.', 'Vi-B04', 'Vi-BCZ14') that will be briefly presented in this section (for more details, see EXPERIMENTAL DESIGN, MATERIALS AND METHODS). In each experiment, leaves were collected on grafted apple trees varying for their resistance to apple scab that were artificially inoculated -or not- with two isolates of Vi. Experimental design did not include randomization for the 'No inoc.' experiment whereas a block-design organization was set up for 'Vi-B04' and 'Vi-BCZ14' experiments. Metabolic content was analysed after aqueous methanol extraction using an Ultra High Performance Liquid Chromatography (UHPLC) system coupled with High-Resolution Mass Spectrometry (HRMS) detection.

The dataset contains three files, one for each experiment. Each metabolic feature is unique because it corresponds to a specific mass-to-charge ratio (m/z) measured by HRMS, at a given retention time depending on UHPLC conditions. These two metrics were coupled to attribute to each feature a unique identifier: as an example, a metabolic feature with a m/z value of 275.09129 and a time retention of 19.951 minutes was renamed as "M275.09129_T19.951". The same process of metabolic extraction and analysis workflow was applied for all experiments and led to the identification of 1,576, 2,102 and 2,955 metabolic features in the 'No inoc.', 'Vi-B04' and 'Vi-BCZ14' experiments, respectively. Each line represents one sample while metabolic features are indicated in columns. An additional column defines the replicate or copy number within the block design for 'Vi-B04' and 'Vi-BCZ14' experiments. The quantitative variable for
each metabolic feature corresponds to the surface area under the chromatogram curve, which is proportional to ionization properties of metabolic features and to their relative abundance in samples.

Principal Components Analysis (PCA) was generated after scaling metabolic features to the standard deviation for the three conditions (Fig. 1). The first components of 'No inoc.', 'Vi-B04' and 'Vi-BCZ14' conditions respectively explain 9.7 %, 10 % and 16.3 % of the total variation while the second components explain 7.4 %, 5.5 % and 4.8 % of the total variation. Overlapping ellipses and similar component percentages tend to show comparable variability among the experimental designs.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Assessment of metabolic feature reproducibility between replicates in the three experiments. The two first components of Principal Component Analysis are shown, with samples coloured according to the replicate (No inoculation) or block (Vi-B04/Vi-BCZ14) number.</figcaption></figure>

# 4. Experimental Design, Materials and Methods

## 4.1. Biological material

All experiments were conducted on a pseudo-F1 population described in [3]. The 267 individuals were derived from a controlled cross between TN10-8 and 'Fiesta', two genotypes partially resistant to apple scab. Each year, scions were collected from a conservatory orchard located at the Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE, Angers, France) and maintained by the Horticole Experimental Unit (10.15454/1.5573931618268674E12). Scions were grafted on MM106 rootstock.

Three experiments were conducted: one without pathogen and the others with inoculation of two distinct isolates of Vi. The first inoculation was realized with the reference isolate 'EU-B04' (Origin: Belgium, host: Golden Delicious) previously described in [9,10] whereas the second one, used the isolate '09BCZ014' (Origin: France, host: 'TN10-8 × Prima' progeny [individual E063]), referred to as isolate '2557' in [3]. The isolate '09BCZ014' partially overcomes the resistance conferred by the 'qT1' resistance QTL segregating in the 'TxF progeny'. The use of this isolate allows to better study the effect of other QTL segregating in the progeny.

Monoconidial suspensions were prepared from diseased dry leaves at a concentration of 2.5 × 10⁵ conidia.mL⁻¹ and sprayed on grafted trees, incubated thereafter two days at 17 °C under a plastic sheet to maintain high humidity, according to the conditions described by [11].
<caption>**Table 1**</caption>

Summary of information about the design and sampling strategies for the three experiments conducted on the 'TxF progeny'.

<table><thead><tr><th>Experiment</th><th>No inoculation</th><th>Vi-B04</th><th>Vi-BCZ14</th></tr></thead><tbody><tr><td>Time and year</td><td>Spring 2021</td><td>Spring 2022</td><td>Autumn 2022</td></tr><tr><td>Number of genotypes</td><td>117</td><td>256</td><td>229</td></tr><tr><td>Number of replicated genotypes</td><td>23</td><td>256</td><td>108</td></tr><tr><td>Total number of samples</td><td>140</td><td>512</td><td>337</td></tr><tr><td>Number of copies per sample</td><td>1</td><td>Pool of 2</td><td>Pool of 2</td></tr><tr><td>Randomization (Block-design)</td><td>NO</td><td>YES</td><td>YES</td></tr><tr><td>Metabolic features detected</td><td>1,576</td><td>2,102</td><td>2,955</td></tr></tbody></table>

## 4.2. Experimental design and sampling

After grafting, young trees were grown in a greenhouse at INRAE Angers (France) under semi-controlled conditions (23 °C day/20 °C night, humidity 40–80 %, and artificial light to complement natural light). Leaf sampling was performed on approximately 5-weeks old trees, non-inoculated or five days after inoculation with Vi. All information related to the design and sampling of the three experiments is described in Table 1. Sample sizes vary depending on the experiment (see LIMITATIONS section for more details).

In the experiment of 2021 without inoculation (coded 'No inoc.'), 117 genotypes out of the 267 individuals of the 'TxF progeny' were grafted in two replicates. The resulting plants were grown without randomization in the greenhouse. Out of the 117 genotypes, 94 were sampled as unique whereas 23 were duplicated. Two leaves were collected on each plant and the two halves of both leaves were pooled in an Eppendorf tube. A total of 140 samples were then processed for metabolomic analysis.

In Vi-experiments of 2022 (coded 'Vi-B04' and 'Vi-BCZ14'), 256 genotypes were grafted in eight replicates and distributed in four randomized blocks, containing each, two replicates of the same genotype placed side by side. Plants were inoculated with 'EU-B04' isolate in Spring 2022 and with isolate '09BCZ014' in Autumn 2022. At the end of the first experiment, plants were treated with antifungal chemicals (Score® and Funguran®) and then pruned leading to a fewer number of genotypes and replicates for the second experiment. Sampling was done on two blocks containing the best growing shoots. For each block, one leaf was collected on each replicate and cut in half. Thus, a sample is composed of two half-leaves from the two replicates of the same block. Metabolomic analyses were performed on each of these samples resulting in a total of 512 samples for 'Vi-B04' experiment and 337 samples for 'Vi-BCZ14' experiment.

## 4.3. Extraction of metabolites and chemical profiling

For all three experiments, samples were directly frozen in liquid nitrogen and kept at -80 °C until lyophilization. Leaf samples were then grounded using a ball mills with two balls of 3–4 mm of diameter in each tube. Metabolites were extracted from 20 mg dry powder of apple leaves as described in [12]. The dry powder was extracted in 1 mL 60 % aqueous methanol to which 25 µL of taxifolin were added (as internal standard, 2 mg.mL⁻¹ methanol), then blended (1 min) and centrifuged (10 min, 2800 × g). The extraction was repeated once, and the supernatants pooled and vacuum-dried. The residue was dissolved in 500 µL of 70 % aqueous methanol, filtered (0.22 µm) and transferred to vials before HRMS¹ analyses. One additional vial, the pooled-sample vial, was prepared with an equal quantity of each sample (2 µL each) for the HRMS² analyses.

Chromatographic analyses were performed on a Vanquish UHPLC system equipped with a binary pump, an autosampler and a temperature-controlled column. Metabolites contained in the extracts (10 µL) were separated on a XB-C18 Kinetex (150 × 2.1 mm, 2.6 µm) (Phenomenex
Inc., Torrance, CA, USA) using a gradient of mobile phase composed of water + 0.1 % formic acid (A) and methanol + 0.1 % formic acid (B) at a flow rate of 200 µL.min⁻¹. The elution program consisted in starting with 10 % B for 2 min, then linearly increasing from 10 to 30 % B in 8 min, then to 95 % B in 10 min. The column was rinsed for 5 min with 95 % B and re-equilibrated to the initial conditions for 4 min prior to the next run. The samples were analysed randomly.

HRMS¹ detection was performed on an Orbitrap IDX™ (ThermoFisher Scientific, Bremen, Germany) mass spectrometer in positive and negative electrospray ionization (ESI) modes. The capillary voltages were set at 3.5 kV and 2.5 kV for positive and negative modes, respectively. The source gases were set (in arbitrary unit min⁻¹) to 40 (sheath gas), 8 (auxiliary gas) and 1 (sweep gas) and the vaporizer temperature was 320 °C. Full scan HRMS¹ spectra were acquired from 120 to 1200 m/z at a resolution of 60,000 (at 200 m/z). HRMS² analysis was performed on the pooled-sample vial using the data dependent acquisition (DDA) mode. For this analysis, the AcquireX data acquisition workflow developed by ThermoFisher was applied. Briefly, this workflow increases the number of HRMS² acquisition, especially on low-intensity ions, through the creation of an inclusion list after a first injection of the sample and the establishment of a dynamic exclusion list occurring by the iterative sample analysis (involving 5–6 successive injections).

The raw UHPLC-HRMS files were uploaded on the Compound Discoverer 3.3.2.31 software (ThermoFisher Scientific, Bremen, Germany) for metabolomics analysis. Briefly, it includes peak detection, chromatogram alignment and peak grouping in features. The Detect Compounds node was used with 5 ppm mass tolerance, 200,000 minimum peak intensity, at least 8 scans per peak, peak detection S/N threshold 3, remove baseline false, gap ratio threshold 0.35 and max peak width 1. All details are given in the analytic workflow available in Supplementary File 1. Each feature corresponds to a specific *m/z* at a given retention time.

## 4.4. Data exploration and visualization

All descriptive analyses were performed on published datasets [13] using the software R version 4.3.1 [14]. PCA were generated with the PCA function (FactoMineR package, [15]) and plotted with *fviz_pca_ind* function (factoextra package, [16]).

## Limitations

Fewer genotypes were grafted and repeated in the non-inoculated experiment (2021) than in the other two experiments, but this experiment was not designed to be compared with the other two but primarily to develop the untargeted metabolomic approach. In addition, the number of genotypes and replicates studied varied from experiment to experiment due to plant growth constraints.

## Ethics Statement

The authors have read and followed the ethical requirements for publication in Data in Brief and confirm that the current work does not involve human subjects, animal experiments, or any data collected from social media platforms.

## Credit Author Statement

**Romane Lapous:** Data acquisition. Data curation. Formal analysis. Writing - original draft/review/editing.
**Florent Magot:** Data acquisition. Data curation. Writing - review/editing.

**Romain Larbat:** Data acquisition. Methodology. Writing - review/editing.
**Caroline Denancé:**
Data acquisition. **Christian Cattanéo**: Data acquisition. **Hélène Muranty**: Conceptualization.
Methodology. Writing - review/editing. **Charles-Eric Durel**: Conceptualization. Funding acqui-
sition. Methodology. Writing - review/editing. **Julie Ferreira de Carvalho**: Conceptualization.
Methodology. Writing - original draft/review/editing.

Data Availability

Metabolomic dataset of an apple progeny carrying resistance quantitative trait loci to apple
scab without or after inoculation of the pathogen Venturia inaequalis (Original data) (en-
trepot.recherche.data.gouv.fr).

Acknowledgments

This work was supported by (i) a grant from the BAP (Plant biology and breeding) division of INRAE and by (ii) a grant from the French government managed by the Agence Nationale de la Recherche as part of the programme d'investissement d'avenir under the reference ANR-20-PCPA-0003 (CapZeroPhyto project). Romane Lapous was supported by a Ph.D. fellowship from the BAP division of INRAE and the 'Pays de la Loire' region (France). The authors greatly thank the PHENOTIC platform for carefully taking care of the plant material in the greenhouse (10.17180/YKBZ-2V85) in particular E. Durand. The authors would like to thank the Biological Resource Center "RosePom - Pome Fruits and Roses" (https://www6.angers-nantes.inrae.fr/irhs/Ressources-mutualisees/Ressources-genetiques/CRB-Fruits-a-pepins-et-rosier) and associated staff for maintaining the plant material and associated datasets used in the present data article. They thank the Horticole Experimental Unit (10.15454/1.5573931618268674E12) for the management of the conservatory orchard belonging to the BRC RosePom and the supply of scions. They thank the Metabolomic and Structural Analytic Platform (PASM, Université de Lorraine; https://ensaia.univ-lorraine.fr/fr/content/plateau-danalyse-structurale-et-metabolomique) for the metabolomic analyses. Finally, the authors wish to thank all the people that helped in the sampling of leaf material across the three experiments: C. Bernard, A. Bodelot, A. Degrave, M. Delaire, N. Dousset, M. Gaucher, M.-C. Guillou, C. Heintz, G. Maillard, B.-H. Nguyen, F. Pantin, L. Perchepied, E. Ravon, F. Tirado, E. Vergne.

Declaration of Competing Interest

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

**Supplementary Materials**

Supplementary material associated with this article can be found, in the online version, at
doi:10.1016/j.dib.2025.111566.

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