Ethylene Regulates Combined Drought and Low Nitrogen Tolerance in Wheat: Proteomic Analysis
Bibliographic record
Abstract
Wheat is a staple crop in China’s arid and semi-arid regions. Drought and low nitrogen (LN) are two major constraints to wheat growth and production. However, the molecular mechanism underlying wheat response to both drought and LN stress remains unknown. Accordingly, we conducted a proteomic study on the roots of two wheat varieties, Chang6878 (drought tolerant) and Shi4185 (drought sensitive) and compared the differences between drought and combined drought and LN stress treatments. In total, 5143 proteins were identified, of which 163 differentially abundant proteins (DAPs) were uniquely upregulated under drought and LN stress in Chang6878. Enrichment analysis showed that DAPs were mainly involved in mitogen-activated protein kinase signaling, phenylpropanoid biosynthesis, glutathione metabolism, ethylene biosynthesis, ethylene signal transduction, and oxidation–reduction reactions. These DAPs were verified via parallel reaction monitoring and quantitative real-time polymerase chain reaction. Chang6878 was treated with the ethylene synthesis precursor 1-aminocyclopropanecarboxylic acid, and its resistance to drought and LN stress improved. After treatment with the ethylene synthesis inhibitor silver nitrate and ethylene signal transduction inhibitor 1-methylcyclopropene, drought and LN stress resistance reduced. These results provide novel insights into the tolerance mechanisms of Chang6878 to drought and LN stress by altering ethylene synthesis and signal transduction. This study provides a reference for breeding drought- and low-nitrogen-tolerant wheat germplasm resources and a theoretical basis for maintaining food security in arid, barren areas.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".