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

Phytosanitary treatments are massively used in orchards to fight apple scab, a disease caused by the fungus <i>Venturia inaequalis</i> (<i>Vi</i>). 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 <i>Vi</i> 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 inoculation with two different <i>Vi</i> isolates, including one known to overcome one QTL. Metabolic content was extracted in aqueous methanol before performing an untargeted metabolomic analysis using an Orbitrap IDX<sup>TM</sup> mass spectrometer, allowing high-resolution mass spectrometry (HRMS) detection. This approach without <i>a priori</i> 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.

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Publication Details

Journal
Data in Brief
Published
2025-04-16
DOI
https://doi.org/10.1016/j.dib.2025.111566
Citations
1
Primary Topic
Fungal Plant Pathogen Control
Type
article
Field-Weighted Citation Impact
3.03

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article

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

Julie Ferreira de Carvalho, Cristina Cattaneo, Hélène Muranty, Caroline Denancé et al.
1 citations
Data in Brief
Fungal Plant Pathogen Control
3.03
article

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

Julie Ferreira de Carvalho, Cristina Cattaneo, Hélène Muranty, Caroline Denancé, Romain Larbat, Florent Magot, Romane Lapous, Charles-Eric Durel
article en
1 citations

Abstract

Phytosanitary treatments are massively used in orchards to fight apple scab, a disease caused by the fungus <i>Venturia inaequalis</i> (<i>Vi</i>). 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 <i>Vi</i> 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 inoculation with two different <i>Vi</i> isolates, including one known to overcome one QTL. Metabolic content was extracted in aqueous methanol before performing an untargeted metabolomic analysis using an Orbitrap IDX<sup>TM</sup> mass spectrometer, allowing high-resolution mass spectrometry (HRMS) detection. This approach without <i>a priori</i> 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.

Data in BriefVol. 60
Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Laboratoire Agronomie et Environnement, Université d'Angers (FR), Université de Lorraine (FR)
Université de Lorraine, Agence Nationale de la Recherche, Conseil Régional des Pays de la Loire, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement
Zero hunger
Openalex Percentile: Top 12%
Fungal Plant Pathogen Control
3.03
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