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Enregistrement W4324018689 · doi:10.1093/plcell/koad074

WRKYng together: Coordination between kinase cascades and transcription factors contributes to immunity in rice

2023· letter· en· W4324018689 sur OpenAlexaff
Bradley Laflamme

Notice bibliographique

RevueThe Plant Cell · 2023
Typeletter
Langueen
DomaineAgricultural and Biological Sciences
ThématiquePlant-Microbe Interactions and Immunity
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésBiologyTranscription factorImmunityCell biologyKinaseGeneticsComputational biologyGeneImmune system

Résumé

récupéré en direct d'OpenAlex

As an early barrier against many pathogens, plants have evolved transmembrane receptors which perceive conserved motifs (or “patterns”) in microbial populations at large—things like bacterial flagellin or fungal chitin. These receptors can then activate a suite of downstream immune outputs, often collectively referred to as pattern-triggered immunity (PTI). What is particularly astonishing about PTI is the sheer variety of immune outputs and components it can encompass: calcium influx, MAP and receptor-like kinase cascades, production of reactive oxygen species, phytohormone signaling pathways, transcriptional rewiring, cell wall reinforcement, and deployment of antimicrobial compounds are just some of its possible outputs (Bigeard et al. 2015). The interconnectedness of all these processes is clear enough; but the precise ordering and nature of these different processes is still frequently being relitigated by the field, particularly as we see more and more examples of variation within and across plant species when it comes to these core components of PTI. In this issue of The Plant Cell, Shuai Wang and colleagues (Wang et al. 2023) draw functional connections between MAP kinase family members, WRKY transcription factors (TFs), and auxin signaling in the rice (Oryza sativa) immune response. Both MAP kinases and WRKY TFs are known to be of paramount importance to plant immune pathways (Bigeard et al. 2015), though many members of these protein families have remained functionally enigmatic. Here, the authors show that WRKY31, a TF which contributes to rice resistance against Magnaporthe oryzae (Zhang et al. 2008), forms a functional module with a MAP kinase cascade in the rice immune system. Using several approaches (yeast-2-hybrid, protein pull-downs, and in planta bimolecular fluorescence complementation [BiFC] and co-immunoprecipitation), the authors found that WRKY31 interacts with the MAP kinase kinase MKK10-2 and several MAP kinases, including MPK3. Furthermore, these proteins formed a ternary WRKY31–MPK3–MKK10-2 complex, with in vitro and in planta assays showing that the MKK10-2/MPK3 cascade can phosphorylate and thereby increase activity of WRKY31. Knocking out MKK10-2 or WRKY31 led to increased susceptibility to the pathogen (see Figure), while overexpressing MKK10-2 improved defense only when WRKY31 was present, indicating that WRKY31 likely functions downstream of MKK10-2. Susceptibility to M. oryzae is influenced by the presence of WRKY31 and MKK10-2–MAPK. From left to right: M. oryzae infection symptoms are shown across a range of plant genotypes: 2 WRKY31 overexpression lines (Ubi:fW31h) show lower lesion development in comparison to wild-type ZH17 plants, while 2 WRKY31 (w31ko) and MKK10-2 (mkk10-2) knockout lines show dramatic increases in lesion development. Reprinted from Wang et al. (2023), Supplemental Figure 5A. Interestingly, a phosphomimic mutant allele of WRKY31 accumulated to higher levels than the wild-type allele. This led Wang et al. to investigate whether ubiquitination was controlling the stability of WRKY31. Indeed, they found that WRKY31 interacted with REIW1 (RING-finger E3 ubiquitin ligase interacting with WRKY1) in yeast, pull-downs, and in planta using BiFC. The phosphomimic mutant also interacted with REIW1, though the capacity of REIW1 to degrade this mutant is lower than the wild type. Thus, activation of WRKY31 via phosphorylation likely improves its stability and improves the quality of an immune output during infection. The group also characterized the WRKY–MAPK–MKK module in terms of how it affects rice physiology and phytohormone accumulation. MKK10-2 overexpression in the etiolated hypocotyls of rice negatively affected the biosynthetic gene expression for, transport of, and accumulation of indole-3-acetic acid, the most common auxin hormone. In contrast, transcripts involved in the biosynthesis of jasmonic and salicylic acid, 2 other phytohormones which can antagonize auxin (Bigeard et al. 2015), were upregulated coincident with MKK10-2 overexpression. The cumulative effect of these changes was a classic “growth versus defense” trade-off: MKK10-2 activation improved resistance via its modulation of phytohormone signaling, but at the cost of plant growth. With an investigation spanning protein–protein interactions, genetics, phytohormone analysis, biochemistry, pathology, and more, Wang and colleagues offer a compelling picture of how this module plays a significant and multifaceted role in rice PTI. Nonetheless, M. oryzae remains one of the most agriculturally destructive pathogens of rice worldwide, suggesting that this pathogen may have strategies for dealing with wild-type levels of WRKY31–MKK10-2 activity. A recent study of M. oryzae suggests that the pathogen has an expansive repertoire of virulence “effector” proteins (Yan et al. 2023), and such effectors frequently target PTI components (Bigeard et al. 2015)—MAPKs and TFs included. Thus, it will be interesting to see whether any M. oryzae effectors target and impair this WRKY–MAP kinase module during infection, as it may help to further decode how this nasty pathogen manages to stay ahead of PTI.

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)0,002
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,015

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,002
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0140,014
Charge utile insuffisante (le modèle a refusé de juger)0,0030,002

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 Opus0,039
Tête enseignante GPT0,224
Écart entre enseignants0,186 · 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'étudeExpérimental (laboratoire)
Domainenon disponible
GenreAutre

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

En bref

Citations1
Publié2023
Routes d'admission1
Résumé présentnon

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