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Deciphering the language of fungal pathogen recognition receptors

hal.structure.identifierModels and Algorithms for the Genome [ MAGNOME]
dc.contributor.authorDYRKA, Witold
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierModels and Algorithms for the Genome [ MAGNOME]
dc.contributor.authorDURRENS, Pascal
hal.structure.identifierInstitut de biochimie et génétique cellulaires [IBGC]
dc.contributor.authorPAOLETTI, Mathieu
hal.structure.identifierInstitut de biochimie et génétique cellulaires [IBGC]
dc.contributor.authorSAUPE, Sven J
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierModels and Algorithms for the Genome [ MAGNOME]
dc.contributor.authorSHERMAN, David J
dc.date.accessioned2024-04-15T09:57:52Z
dc.date.available2024-04-15T09:57:52Z
dc.date.created2014-10-19
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/198953
dc.description.abstractEnThe NLR family of receptors plays a key role in the innate immune system of animals, plants and fungi. In the latter two phyla NLRs adapt quickly to ever-changing pathogen-specific invasion markers thanks to their repeat-based architecture, which can pro-duce diversity of recognition epitopes through unequal crossing-over and mutation. Charac-terizing computationally the language of these pathogen recognition receptors can provide insight into the molecular mechanisms of immune response and describe the limits of the pathogen targets that can be recognized. In this work, we model generation and selection of the recognition epitopes as a stochastic string rewriting system with constraints, tuned by analysis of observed evolutionary processes and validated with regard to a large dataset of fungal NLRs. Among others, analyzing the feasible set of solutions revealed that the model explained the i/i + 2 periodicity observed in the repeat number distribution of a family of receptors. In addition, in exploring discrepancies between real and simulated data we discovered an overrepresented pattern which potentially has functional importance. The methodology developed in this work is general and therefore can be applied to any class of amino acid repeats generated by unequal crossing-over for which an equivalent high quality dataset is available.
dc.description.sponsorshipReconnaissance hétérospécifique chez les champignons filamenteux : Des récepteurs à la réponse cellulaire - ANR-11-BSV3-0019
dc.language.isoen
dc.subject.eninnate immune system
dc.subject.enamino acid repeats
dc.subject.enunequal crossing-over
dc.subject.enstring rewrit-ing system
dc.subject.enformal languages
dc.title.enDeciphering the language of fungal pathogen recognition receptors
dc.typeDocument de travail - Pré-publication
dc.subject.halInformatique [cs]/Bio-informatique [q-bio.QM]
bordeaux.hal.laboratoriesLaboratoire Bordelais de Recherche en Informatique (LaBRI) - UMR 5800*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
hal.identifierhal-01083421
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01083421v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=DYRKA,%20Witold&DURRENS,%20Pascal&PAOLETTI,%20Mathieu&SAUPE,%20Sven%20J&SHERMAN,%20David%20J&rft.genre=preprint


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