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Record W4324018689 · doi:10.1093/plcell/koad074

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

2023· letter· en· W4324018689 on OpenAlexaff
Bradley Laflamme

Bibliographic record

VenueThe Plant Cell · 2023
Typeletter
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologyTranscription factorImmunityCell biologyKinaseGeneticsComputational biologyGeneImmune system

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0140.014
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.224
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2023
Admission routes1
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