Overexpression of the acidic dehydrin WCOR410 improves freezing tolerance in transgenic strawberry leaves
Bibliographic record
Abstract
Progress in freezing tolerance (FT) improvement through plant breeding approaches has met with little success in the last 50 years. Engineering plants for greater FT through plant transformation is one possible way to reduce the damage caused by freezing. Here, we report an improvement of the selection procedure and the transfer of the wheat Wcor410a acidic dehydrin gene in strawberry. The encoded protein has previously been shown to be associated with the plasma membrane, and its level of accumulation has been correlated with the degree of FT in different wheat genotypes. The WCOR410 protein was expressed in transgenic strawberry at a level comparable with that in cold-acclimated wheat. Freezing tests showed that cold-acclimated transgenic strawberry leaves had a 5 degrees C improvement of FT over wild-type or transformed leaves not expressing the WCOR410 protein. However, no difference in FT was found between the different plants under non-acclimated conditions, suggesting that the WCOR410 protein needs to be activated by another factor induced during cold acclimation. These data demonstrate that the WCOR410 protein prevents membrane injury and greatly improves FT in leaves of transgenic strawberry. A better understanding of the limiting factors allowing its activation may open up the way for engineering FT in different plant organs, and may find applications for the cryopreservation of human tissues and organs.
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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".