<i>Vitis CBF1</i> and <i>Vitis CBF4</i> differ in their effect on <i>Arabidopsis</i> abiotic stress tolerance, development and gene expression
Bibliographic record
Abstract
Plants growing in temperate regions encode several C-repeat binding factor/dehydration responsive element binding factors (CBF/DREB1) and the question is whether these transcription factors have different functions. In this study, Arabidopsis transformed with grape CBF1 (VrCBF1) or grape CBF4 (VrCBF4) were characterized. Electrolyte leakage assays showed that the freezing tolerance of transgenic lines was correlated with the level of VrCBF expression irrespective of the type of CBF, while drought tolerance was most increased by VrCBF1. VrCBF overexpression coincided with an increase in the expression of the cold-regulated genes AtCOR15a, AtRD29A, AtCOR6.6 and AtCOR47. In addition, the development of grape CBF overexpressing plants was seen to be altered and resulted in dwarf plants which flowered later and had thicker rosette leaves with a higher stomatal density. Analysis of gene expression showed that these morphological changes may be because of an increase in the expression of AtRGL3 in VrCBF4 lines or AtGA2ox7 in VrCBF1 lines, and AtFLC in both. In addition, the results show for the first time that CBFs can positively affect the expression of AtICE1/SCREAM1, the gene that is known to induce AtCBF3 expression. The difference in gene induction by VrCBF1 compared with VrCBF4 suggests that these CBFs have different regulons.
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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) | 0.001 | 0.000 |
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".