New alleles of Arabidopsis <i>BIK1</i> reinforce its predominant role in pattern-triggered immunity and caution interpretations of other reported functions
Bibliographic record
Abstract
Abstract The receptor-like cytoplasmic kinase BIK1 and its close homolog PBL1 have been widely recognized as central components of plant immunity. However, most genetic studies of BIK1 and PBL1 functions were carried out with single T-DNA insertional mutant alleles. Some phenotypes observed in these mutants, e.g. autoimmunity, have been difficult to reconcile with the proposed role of BIK1 and PBL1 in pattern-triggered immunity. In this study, we generated multiple new alleles of bik1 and pbl1 by CRISPR-Cas9-based gene editing and systematically analyzed these mutants alongside existing T-DNA insertional lines. These analyses reinforced the central role of BIK1 and PBL1 in pattern-triggered immunity mediated by both receptor kinases and receptor-like proteins. At the same time, however, we revealed several pleiotropic phenotypes associated with T-DNA insertions that are not necessarily linked to loss of BIK1 or PBL1 function. Further analyses of newly generated bik1 pbl1 double mutants uncovered an even greater contribution of these kinases to immune signaling and disease resistance than previously appreciated. These findings clarify longstanding ambiguities surrounding BIK1 and PBL1 functions.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".