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Enregistrement W2345434865 · doi:10.1093/biosci/biw034

Whole-Plant Defenses: How Eukaryotes Catch and Kill Unwary Microbial Predators

2016· article· en· W2345434865 sur OpenAlexaff
Marcia Stone

Notice bibliographique

RevueBioScience · 2016
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueInsect symbiosis and bacterial influences
Établissements canadiensWorld Federation of Science Journalists
Organismes subventionnairesnon disponible
Mots-clésPredationBiologyEcology

Résumé

récupéré en direct d'OpenAlex

Surveillance of their cellular surroundings with a complex network of cell-surface immune molecules enables plants as well as animals to detect microbial intruders early enough to defeat them—a system considered by evolutionary biologists to be one of the most ancient and best conserved of all immune defenses. Rice plants, for example, defend their leaves against the crop-damaging bacterial pathogen Xanthomonas oryzae (Xoo) with XA21ࣧthe first characterized of more than 350 immune receptors, says Pamela Ronald, of the University of California, Davis. Ronald discovered XA21 in 1995. Late last year, Ronald and colleagues identified three Xoo rax genes needed to activate XA21-mediated immunity. These genes suggest that sulfation is important in the invasion process, say the researchers, who named the newly identified tyrosine-sulfated protein that the genes encode RaxX. Sulfation, first identified in 1954, is known to strengthen protein-to-protein bonding and probably makes Xoo a more stable pathogen. If true, this invites speculation that by using the pathogen's RaxX as a marker for the presence of Xanthomonas, rice has out-evolved its bacterial predator, at least for the moment. Leaves dominate the plant universe, or phyllosphere, which includes all of the aboveground parts and “at 1 billion km2 is one of the largest biological surfaces of Earth, outmatching land masses by roughly seven times,” write Mitja Remus-Emsermann and Julia Vorholt in the December 2014 issue of Microbe magazine. The microbes that live on leaves, mostly bacteria, “have to be admired as survival experts” contending with multiple assaults, from damaging ultraviolet radiation to torrential rains, say Remus-Emsermann and Vorholt. Despite all the difficulties, leaves host a diverse community of protective bacteria. But only those from a limited number of phyla appear able to cope; these include Actinobacteria, Bacteroidetes, Firmicutes, and the most predominant phylum, Proteobacteria—the same bacteria consistently found in the roots of the well-studied model plant Arabidopsis thaliana and in human gastrointestinal tracts. Because they are relatively easy to find and host fewer microbes than roots do, leaves have been comparatively well studied, but roots, hidden underground and exposed to hundreds of microbial species and trillions of single cells, are finally getting the attention they deserve. Like animal guts, plant root microbiomes are necessary for optimal nutrition and protection against pathogens. Therefore, it is not surprising that plant roots are functionally analogous to animal guts; they have the same problems and the same molecular toolbox, say Cara Haney, of the University of British Columbia, in Vancouver, and Frederick Ausubel, of Harvard Medical School, in the 21 August 2015 issue of Science. By planting wild versions of A. thaliana with varying abilities to attract the beneficial bacterium Pseudomonas fluorescens in microbe-rich natural soil, Haney and colleagues established that plants can help assemble their own microbiomes. The A. thaliana genetically able to support P. fluorescens were protected from many diseases, but those without such genes were not. Although most of the microbes colonizing both roots and guts are passively acquired from their environments, even a small genetic component can have a big impact on plant and animal health, Haney and colleagues discovered. How some A. thaliana manage the difficult task of crafting their preferred underground microbial communities has recently been revealed by Sarah Lebeis, of the University of Tennessee, Knoxville, in conjunction with colleagues in Jeffery Dangl's laboratory at the University of North Carolina at Chapel Hill. Salicylic acid (SA), jasmonic acid, and gaseous ethylene are important immune regulators in plant shoots and leaves, so scientists reasoned that they would exert a similar authority over roots. As proof of concept, Lebeis and collaborators compared the root microbiomes of wild A. thaliana with the microbiomes of mutant plants unable to either synthesize or read the signals of one or more of these defense hormones. Two groups emerged: one that produced and accumulated large amounts of SA and another that acquired less, stored less, or did not respond to its signals. Only the A. thaliana deficient in either the biosynthesis or signaling recognition of SA developed abnormal microbial communities, indicating that this hormone plays an important role in shaping their root microbiome. Anthony Trewavas, of the Institute of Molecular Plant Science in Edinburgh, Scotland, also notes the importance of interactions of rhizosphere bacteria and symbiotic mycorrhizal fungi known to enhance host resistance and the volatile organic compounds (VOCs) synthesized by both groups to “beneficially reprogram root architectureࣧexpanding and deepening resistance capabilities.” “VOCs [important for plant communication] are probably the basis of self-recognition and alien detection in plant roots,” Trewavas says.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: aucune
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,015
Tête enseignante GPT0,185
Écart entre enseignants0,170 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentnon

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