Molecular tailoring of farnesylation for plant drought tolerance and yield protection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Protecting crop yield under drought stress is a major challenge for modern agriculture. One biotechnological target for improving plant drought tolerance is the genetic manipulation of the stress response to the hormone abscisic acid (ABA). Previous genetic studies have implicated the involvement of the beta-subunit of Arabidopsis farnesyltransferase (ERA1) in the regulation of ABA sensing and drought tolerance. Here we show that molecular manipulation of protein farnesylation in Arabidopsis, through downregulation of either the alpha- or beta-subunit of farnesyltransferase enhances the plant's response to ABA and drought tolerance. To test the effectiveness of tailoring farnesylation in a crop plant, transgenic Brassica napus carrying an ERA1 antisense construct driven by a drought-inducible rd29A promoter was examined. In comparison with the non-transgenic control, transgenic canola showed enhanced ABA sensitivity, as well as significant reduction in stomatal conductance and water transpiration under drought stress conditions. The antisense downregulation of canola farnesyltransferase for drought tolerance is a conditional and reversible process, which depends on the amount of available water in the soil. Furthermore, transgenic plants were more resistant to water deficit-induced seed abortion during flowering. Results from three consecutive years of field trial studies suggest that with adequate water, transgenic canola plants produced the same amount of seed as the parental control. However, under moderate drought stress conditions at flowering, the seed yields of transgenic canola were significantly higher than the control. Using protein farnesyltransferase as an effective target, these results represent a successful demonstration of engineered drought tolerance and yield protection in a crop plant under laboratory and field conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it