Comparative Proteome Analyses Reveal that Nitric Oxide Is an Important Signal Molecule in the Response of Rice to Aluminum Toxicity
Bibliographic record
Abstract
Acidic soils inhibit crop yield and reduce grain quality. One of the major contributing factors to acidic soil is the presence of soluble aluminum (Al(3+)) ions, but the mechanisms underlying plant responses to Al(3+) toxicity remain elusive. Nitric oxide (NO) is an important messenger and participates in various plant physiological responses. Here, we demonstrate that Al(3+) induced an increase of NO in rice seedlings; adding exogenous NO alleviated the Al(3+) toxicity related to rice growth and photosynthetic capacity, effects that could be reversed by suppressing NO metabolism. Comparative proteomic analyses successfully identified 92 proteins that showed differential expression after Al(3+) or NO treatment. In particular, some of the proteins are involved in reactive oxygen species (ROS) and reactive nitrogen species (RNS) metabolism. Further analyses confirmed that NO treatment reduced Al(3+)-induced ROS and RNS toxicities by increasing the activities and protein expression of antioxidant enzymes, as well as S-nitrosoglutathione reductase (GSNOR). Suppressing GSNOR enzymatic activity aggravated Al(3+) damage to rice and increased the accumulation of RNS. NO treatment altered the expression of proteins associated with cell wall synthesis, cell division and cell structure, calcium signaling and defense responses. On the basis of these results, we propose that NO activates multiple pathways that enhance rice adaptation to Al(3+) toxicity. Such findings may be applicable to crop engineering to enhance yield and improve stress tolerance.
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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) | 0.000 | 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 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".