Ectopic expression of Arabidopsis Target of Rapamycin (AtTOR) improves water-use efficiency and yield potential in rice
Bibliographic record
Abstract
Abstract The target of Rapamycin (TOR) present in all eukaryotes is a multifunctional protein, regulating growth, development, protein translation, ribosome biogenesis, nutrient, and energy signaling. In the present study, ectopic expression of TOR gene of Arabidopsis thaliana in a widely cultivated indica rice resulted in enhanced plant growth under water-limiting conditions conferring agronomically important water-use efficiency (WUE) trait. The AtTOR high expression lines of rice exhibited profuse tillering, increased panicle length, increased plant height, high photosynthetic efficiency, chlorophyll content and low ∆13C. Δ13C, which is inversely related to high WUE, was as low as 17‰ in two AtTOR high expression lines. These lines were also insensitive to the ABA-mediated inhibition of seed germination. The significant upregulation of 15 stress-specific genes in high expression lines indicates their contribution to abiotic stress tolerance. The constitutive expression of AtTOR is also associated with significant transcriptional upregulation of putative TOR complex-1 components, OsRaptor and OsLST8. Glucose-mediated transcriptional activation of AtTOR gene enhanced lateral root formation. Taken together, our findings indicate that TOR, in addition to its multiple cellular functions, also plays an important role in response to abiotic stress and potentially enhances WUE and yield related attributes.
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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".