Proteomics analysis of salt-induced leaf proteins in two rice germplasms with different salt sensitivity
Bibliographic record
Abstract
Lee, D.-G., Park, K. W., An, J. Y., Sohn, Y. G., Ha, J. K., Kim, H. Y., Bae, D. W., Lee, K. H., Kang, N. J., Lee, B.-H., Kang, K.-Y. and Lee, J. J. 2011. Proteomics analysis of salt-induced leaf proteins in two rice germplasms with different salt sensitivity. Can. J. Plant Sci. 91: 337–349. This study was conducted to investigate salt-stress-related physiological responses and proteomics changes in the leaves of two rice (Oryza sativa L.) cultivars. Shoot growth and water content of rice leaves were more severely reduced in Dalseongaengmi-44 than in Dongjin under salt stress. The salt-sensitive Dalseongaengmi-44 exhibited a greater increase in sodium ion accumulation in its leaves than the salt tolerant Dongjin. Comparative analysis of the rice leaf proteins using two-dimensional gel electrophoresis (2-DGE) revealed that a total of 23 proteins were up-regulated under salt stress. Based on matrix-assisted laser desorption ionization-time of flight mass spectrometry and/or electrospray ionization-tandem mass spectrometry analyses, the 23 protein spots were found to represent 16 different proteins. Ten of the identified proteins were previously reported to be salt-responsive proteins, while six, class III peroxidase 29 precursor, beta-1,3-glucanase precursor, OSJNBa0086A10.7 (putative transcription factor), putative chaperon 21 precursor, Rubisco activase small isoform precursor and drought-induced S-like ribonuclease, were novel salt-induced proteins. Under salt stress, fragmentation was increased in several proteins containing the Rubisco large chain. The results of these physiological and proteomics analyses provide useful information that can lead to a better understanding of the molecular basis of salt-stress responses in rice.
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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 | 0.000 |
| 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".