The leucine-rich repeat receptor kinase QSK1 is a novel regulator of PRR-RBOHD complex and is employed by the bacterial effector HopF2 <i> <sub>Pto</sub> </i> to modulate plant immunity
Bibliographic record
Abstract
Abstract Plants detect pathogens using cell-surface pattern recognition receptors (PRRs) like EFR and FLS2, which recognize bacterial EF-Tu and flagellin, respectively. These PRRs, belonging to the leucine-rich repeat receptor kinase (LRR-RK) family, activate the production of reactive oxygen species via the NADPH oxidase RBOHD. The PRR-RBOHD complex is tightly regulated to prevent unwarranted or exaggerated immune responses. However, certain pathogenic effectors can subvert these regulatory mechanisms, thereby suppressing plant immunity. To elucidate the intricate dynamics of the PRR-RBOHD complex, we conducted a comparative co-immunoprecipitation analysis using EFR, FLS2, and RBOHD. We identified QSK1, an LRR-RK, as a novel component of the PRR-RBOHD complex. QSK1 functions as a negative regulator of PRR-triggered immunity (PTI) by downregulating the abundance of FLS2 and EFR. QSK1 is targeted by the bacterial effector HopF2 Pto , a mono-ADP ribosyltransferase, resulting in the reduction of FLS2 and EFR levels through both transcriptional and transcription-independent pathways, thereby inhibiting PTI. Furthermore, HopF2 Pto reduces transcript levels of PROSCOOP genes encoding important stress-regulated phytocytokines and their receptor MIK2. Importantly, HopF2 Pto requires QSK1 for its accumulation and virulence functions within plants. In summary, our results provide novel insights into the mechanism by which HopF2 Pto employs QSK1 to desensitize plants to pathogen attack. One Sentence Summary QSK1, a novel component in the plant immune receptor complex, downregulates these receptors and phytocytokines, and is exploited by bacterial effector HopF2 Pto to desensitize plants to pathogen attack.
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 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) | 0.002 | 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".