Target of Rapamycin (TOR) signaling coordinates a balance between wheat photosynthetic performance and transpirational water conservation for improved water use efficiency and performance under drought
Bibliographic record
Abstract
Drought is an important abiotic stress limiting wheat production worldwide. Hence there is a critical need to develop wheat varieties with improved performance under drought. Target of Rapamycin (TOR) kinase is a central regulator that integrates diverse nutrient, energy, hormone, and environmental stress response signals to coordinate plant growth and development. Recent studies have demonstrated that TOR is also involved in plant responses to abiotic stress. In this current study, in order to address TOR functions in response to wheat drought stress, we generated transgenic wheat lines expressing TaTOR under the control of constitutive and drought-inducible promoters. Inhibition of plant growth in response to drought was discovered to be closely associated with the expression and activity of the wheat TOR protein. Enhancing TaTOR expression driven by a constitutive promoter ( UBQ ) or drought-inducible promoters ( DREB/DEH ), significantly improved drought resistance and greatly reduced yield losses caused by drought stress in wheat. Examination of plant water relations, other related physiological parameters, and genome-wide transcriptomic comparisons demonstrated that enhancing TaTOR expression under drought helps wheat minimize transpirational water loss without compromising photosynthetic performance, thus improving water-use efficiency. This is achieved through efficient regulation of stomatal closure, along with enhanced photosynthetic efficiency, upregulation of ABA-mediated stress signaling, increased antioxidant capacity, and more robust recovery from drought. Our findings highlight the functional roles of TaTOR in wheat drought resistance, providing a valuable new molecular tool for developing wheat cultivars with improved drought resistance needed to address the drought and climate change challenges threatening wheat productivity worldwide.
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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".