In vivo biochemical characterization of transcription factors regulating plant defense response to disease
Bibliographic record
Abstract
The timely, coordinated transcriptional control of large sets of genes in response to microbial pathogen challenge is crucial for plant resistance to disease. Accordingly, the study of transcription factors (TFs), sequence-specific DNA-binding proteins that mediate pathogen-regulated gene expression, is key to the understanding of plant defense responses. Research to date has implicated several members from different families of TFs in mediation of plant defense responses. Considerable activity has been focused on ethylene response factors, WRKY and Whirly factors, the TGA subfamily of basic-domain leucine-zipper (bZIP) proteins, and NPR1 protein (nonexpressor of pathogenesis-related genes 1), which regulates defense responses through its interaction with TGA factors. Whereas relatively few TFs had been shown to mediate pathogen-regulated gene expression a decade ago, recent genomics projects are identifying large numbers of new candidates. Elucidating the function of these TFs at the biochemical or molecular level will be a daunting task, but one which should be greatly facilitated by emerging technologies that permit the analysis of protein–DNA and protein–protein interactions in planta. Here we describe some of these technologies as they have been, or could be, applied to plant TFs implicated in regulating defense-related gene expression.
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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 | 0.001 |
| 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".