Rootstocks Overexpressing StNPR1 and StDREB1 Improve Osmotic Stress Tolerance of Wild-Type Scion in Transgrafted Tobacco Plants
Bibliographic record
Abstract
In grafted plants, the movement of long-distance signals from rootstocks can modulate the development and function of the scion. To understand the mechanisms by which tolerant rootstocks improve scion responses to osmotic stress (OS) conditions, mRNA transport of osmotic responsive genes (ORGs) was evaluated in a tomato/potato heterograft system. In this system, Solanum tuberosum was used as a rootstock and Solanum lycopersicum as a scion. We detected changes in the gene expression levels of 13 out of the 21 ORGs tested in the osmotically stressed plants; of these, only NPR1 transcripts were transported across the graft union under both normal and OS conditions. Importantly, OS increased the abundance of StNPR1 transcripts in the tomato scion. To examine mRNA mobility in transgrafted plants, StNPR1 and StDREB1 genes representing the mobile and non-mobile transcripts, respectively, were overexpressed in tobacco (Nicotiana tabacum). The evaluation of transgenic tobacco plants indicated that overexpression of these genes enhanced the growth and improved the physiological status of transgenic plants growing under OS conditions induced by NaCl, mannitol and polyethylene glycol (PEG). We also found that transgenic tobacco rootstocks increased the OS tolerance of the WT-scion. Indeed, WT scions on transgenic rootstocks had higher ORGs transcript levels than their counterparts on non-transgenic rootstocks. However, neither StNPR1 nor StDREB1 transcripts were transported from the transgenic rootstock to the wild-type (WT) tobacco scion, suggesting that other long-distance signals downstream these transgenes could have moved across the graft union leading to OS tolerance. Overall, our results signify the importance of StNPR1 and StDREB1 as two anticipated candidates for the development of stress-resilient crops through transgrafting technology.
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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) | 0.001 | 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".