Heterologous Expression of Wheat VERNALIZATION 2 (TaVRN2) Gene in Arabidopsis Delays Flowering and Enhances Freezing Tolerance
Bibliographic record
Abstract
The vernalization gene 2 (VRN2), is a major flowering repressor in temperate cereals that is regulated by low temperature and photoperiod. Here we show that the gene from Triticum aestivum (TaVRN2) is also regulated by salt, heat shock, dehydration, wounding and abscissic acid. Promoter analysis indicates that TaVRN2 regulatory region possesses all the specific responsive elements to these stresses. This suggests pleiotropic effects of TaVRN2 in wheat development and adaptability to the environment. To test if TaVRN2 can act as a flowering repressor in species different from the temperate cereals, the gene was ectopically expressed in the model plant Arabidopsis. Transgenic plants showed no alteration in morphology, but their flowering time was significantly delayed compared to controls plants, indicating that TaVRN2, although having no ortholog in Brassicaceae, can act as a flowering repressor in these species. To identify the possible mechanism by which TaVRN2 gene delays flowering in Arabidopsis, the expression level of several genes involved in flowering time regulation was determined. The analysis indicates that the late flowering of the 35S::TaVRN2 plants was associated with a complex pattern of expression of the major flowering control genes, FCA, FLC, FT, FVE and SOC1. This suggests that heterologous expression of TaVRN2 in Arabidopsis can delay flowering by modulating several floral inductive pathways. Furthermore, transgenic plants showed higher freezing tolerance, likely due to the accumulation of CBF2, CBF3 and the COR genes. Overall, our data suggests that TaVRN2 gene could modulate a common regulator of the two interacting pathways that regulate flowering time and the induction of cold tolerance. The results also demonstrate that TaVRN2 could be used to manipulate flowering time and improve cold tolerance in other species.
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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 | 0.000 |
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".