Comparative Proteome Analyses Reveal that Nitric Oxide Is an Important Signal Molecule in the Response of Rice to Aluminum Toxicity
Why this work is in the frame
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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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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 | 0.006 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it