The leucine-rich repeat receptor kinase QSK1 regulates PRR-RBOHD complexes targeted by the bacterial effector HopF2<i>Pto</i>
Bibliographic record
Abstract
Plants detect pathogens using cell-surface pattern recognition receptors (PRRs) such as ELONGATION Factor-TU (EF-TU) RECEPTOR (EFR) and FLAGELLIN SENSING 2 (FLS2), which recognize bacterial EF-Tu and flagellin, respectively. These PRRs belong to the leucine-rich repeat receptor kinase (LRR-RK) family and activate the production of reactive oxygen species via the NADPH oxidase RESPIRATORY BURST OXIDASE HOMOLOG D (RBOHD). The PRR-RBOHD complex is tightly regulated to prevent unwarranted or exaggerated immune responses. However, certain pathogen effectors can subvert these regulatory mechanisms, thereby suppressing plant immunity. To elucidate the intricate dynamics of the PRR-RBOHD complex, we conducted a comparative coimmunoprecipitation analysis using EFR, FLS2, and RBOHD in Arabidopsis thaliana. We identified QIAN SHOU KINASE 1 (QSK1), an LRR-RK, as a PRR-RBOHD complex-associated protein. QSK1 downregulated FLS2 and EFR abundance, functioning as a negative regulator of PRR-triggered immunity (PTI). QSK1 was targeted by the bacterial effector HopF2Pto, a mono-ADP ribosyltransferase, reducing FLS2 and EFR levels through both transcriptional and transcription-independent pathways, thereby inhibiting PTI. Furthermore, HopF2Pto transcriptionally downregulated PROSCOOP genes encoding important stress-regulated phytocytokines and their receptor MALE DISCOVERER 1-INTERACTING RECEPTOR-LIKE KINASE 2. Importantly, HopF2Pto requires QSK1 for its accumulation and virulence functions within plants. In summary, our results provide insights into the mechanism by which HopF2Pto employs QSK1 to desensitize plants to pathogen attack.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".