Integrative Network Analysis of Physiological, Transcriptomic, and Metabolomic Profiles Reveals the Mechanism of Propionic Acid-Mediated Alleviation of Cadmium Toxicity in Wheat ( <i>Triticum aestivum</i> L.)
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Cadmium (Cd), a pervasive toxic metal, poses significant detrimental effects on plant development and yield. Exogenous propionic acid (PA) application significantly alleviated Cd toxicity in wheat seedlings. Based on physiological analysis, exogenous 10 μM PA treatment significantly reduced Cd accumulation by 36.29% in the shoot and 25.79% in the root under 20 μM Cd treatment. Integrative transcriptomic and metabolomic analyses revealed that PA enhances the antioxidant capacity via activation of branched-chain amino acid (BCAA) metabolism and salicylic acid (SA) signaling. Furthermore, PA upregulates TaHSP20 expression through H 2 O 2 signaling, promoting pectin synthesis and demethylation to improve Cd chelation in the cell wall. Additionally, PA activates abscisic acid (ABA) signaling, inducing the expression of TaHSP20 and TaHSP70 to reinforce cell wall biosynthesis and Cd sequestration. Key metabolites such as sedoheptulose and rhamnose may further facilitate Cd 2+ immobilization within the cell wall. These findings elucidate a systemic mechanism underlying PA-mediated alleviation of Cd toxicity, offering new strategies for improving agroecological sustainability.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".