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Poster

Session: Session 2 Presenting Authors at Posters (Odds)

P-183 - Deciphering the evolution of transcriptomic responses in quantitative disease resistance

Wednesday, July 16, 2025
13:30 - 15:15  
Location: Confex Hall 3
Emerging and re-emerging pathosystems

    Presenting Author(s)

  • FD

    Florent Delplace

    PostDoc
    Laboratoire des Interactions Plantes-Microbes Environnement (LIPME), INRAE, CNRS, Université de Toulouse
    Castanet-Tolosan, Occitanie, FRANCE

    • Author(s)

  • MK

    Mehdi Khafif

    Engineer
    Laboratoire des Interactions Plantes-Microbes Environnement (LIPME), INRAE, CNRS, Université de Toulouse
    Castanet Tolosan, Midi-Pyrenees, France

  • RS

    Remco Stam

    PI/Professor
    Kiel University - Institute of Phytopathology
    Kiel, Schleswig-Holstein, Germany

  • AB

    Adelin Barbacci

    Researcher
    Laboratoire des Interactions Plantes-Microbes Environnement (LIPME), INRAE, CNRS, Université de Toulouse
    Castanet Tolosan, Midi-Pyrenees, France

  • SR

    Sylvain Raffaele

    Senior Researcher
    LIPME, INRAE - CNRS, Université de Toulouse, Castanet Tolosan, France
    Castanet Tolosan, Midi-Pyrenees, France

Quantitative disease resistance (QDR) is the only form of plant immunity that mitigates disease symptoms caused by pathogens, including the necrotrophic fungus Sclerotinia sclerotiorum. Understanding the evolutionary origins of QDR is key to deciphering how resistance durability and spectrum evolve in plants. While interspecies comparisons of transcriptomic responses to S. sclerotiorum in Arabidopsis thaliana and five crop species have identified broadly conserved genes with regulatory divergence, the evolution of defense responses within a single species remains largely unexplored. To address this, we analyzed global gene expression in response to S. sclerotiorum across 23 A. thaliana accessions with contrasted QDR phenotypes. Over half of the species' pan-transcriptome exhibited local responses, while global reprogramming patterns were largely independent of accession phylogeny and geographical origin. Further analyses revealed that QDR phenotypes emerge from integrating multiple gene expression patterns, rather than a single regulatory pathway, with rewiring linked to variations in cis-regulatory elements. Finally, we constructed a transcriptome-phenotype map to explore evolutionary trajectories linking transcriptomic landscapes to QDR phenotypes. Our fitness landscape model reveals neutral regulatory networks linking diverse QDR-associated transcriptomes without compromising resistance, key features of robustness and evolutionary potential.