Guard cell and whole plant expression of AtTOR improves performance under drought and enhances water use efficiency
Bibliographic record
Abstract
Water use efficiency is an important target for breeding of improved drought resistance. Minimizing leaf transpirational water loss plays a key role in drought resistance. But this reduces CO 2 levels in leaves, which often reduces photosynthetic efficiency and yield. Signaling pathways play important roles in stress responses, and identifying the molecular, biochemical, and physiological determinants underlying drought signaling may offer new drought mitigating strategies. To explore these possibilities, and because of the importance of stomata in drought response and photosynthesis, we employed guard cell (GC) targeted and constitutive overexpression of the Target of Rapamycin (TOR) kinase, a master regulator of signaling networks, in transgenic Arabidopsis. To investigate the impact of these AtTOR transgenes in drought, we conducted physiological and molecular investigations into drought responses, including leaf water loss, photosynthetic CO 2 assimilation, stomatal H 2 O/CO 2 conductance, , leaf chlorophyll content, and global gene expression in response to drought in wild type and AtTOR expressing Arabidopsis. Links between both guard cell-localized and whole plant AtTOR overexpression were identified, revealing TOR is involved in conservation of water and sustained photosynthetic performance, along with identification of genes associated with drought response in WT vs. AtTOR- expressing transgenic lines. These findings suggest that targeted guard cell AtTOR expression should help achieve a balance between plant water conservation during drought, and maintaining plant performance, by minimizing reductions in photosynthesis. Manipulation of guard cell AtTOR expression could be an effective avenue for developing crops with enhanced drought resistance and increased yield under drought stress, resulting in enhanced water use efficiency.
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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) | 0.002 | 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".