Comparaison et évaluation d’approches bioinformatiques et statistiques pour l'analyse du pathobiome des plantes cultivées

Abstract : Interactions between microorganisms underpin many ecosystem services, including the regulation of crop diseases. An actor in this regulation is the pathobiome, defined as the subset of microorganisms associated with a host plant in interaction with a pathogen. One of the current challenges is to reconstruct pathobiomes from metabarcoding data, in order to identify potential biocontrol agents and to monitor in real time their responses to environmental changes. However, several methodological hurdles must be overcomed to achieve these objectives. First, there is no consensus on the most reliable bioinformatics approach to determine the identity and abundance of microorganisms present in plant samples. In addition, microbial networks built with currently available methods are networks of statistical associations between sequence counts, not directly related to networks of interactions (e. g. competition, parasitism) between microorganisms. The objective of the thesis was therefore to determine the most relevant bioinformatics and statistical approaches to reconstruct microbial interaction networks from metabarcoding data. The study system was grapevine (Vitis vinifera L. cv. Merlot noir) and the fungal agent of grapevine powdery mildew Erysiphe necator. First, we determined the most appropriate bioinformatics approach to identify the fungal community associated with this pathogen, by comparing the ability of 360 pipelines to recover the composition of an artificial community of 189 fungal strains. DADA2 has emerged as the most powerful tool. We then evaluated the influence of the cropping system (conventional vs. organic viticulture) on foliar fungal communities and assessed the level of replicability of microbial networks built with a standard inference method, SparCC. Replicability was very low, casting doubt on the usefulness of these networks for biocontrol and biomonitoring We therefore used a new statistical approach, the PLN model, which allows us to take into account environmental variability, to finely explore the pathobiome of Erysiphe necator. The microbial interactions predicted by the model are being compared with experiments confronting yeasts in co-cultures. An alternative approach, HMSC, was also tested on another biological model and some predictions were successfully compared with the data in the literature. Microbial networks, provided improved reconstruction methods, could therefore be used to capture signals of biotic interactions in the pathobiome.
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Charlie Pauvert. Comparaison et évaluation d’approches bioinformatiques et statistiques pour l'analyse du pathobiome des plantes cultivées. Bio-Informatique, Biologie Systémique [q-bio.QM]. Université de Bordeaux, 2019. Français. ⟨NNT : 2019BORD0214⟩. ⟨tel-02452386⟩

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