Genome-wide transcriptome analysis of rice genes responsive to chilling stress
Bibliographic record
Abstract
Cho, H. Y., Hwang, S.-G., Kim, D. S. and Jang, C. S. 2012. Genome-wide transcriptome analysis of rice genes responsive to chilling stress. Can. J. Plant Sci. 92: 447–460. Low temperature is known to be one of the major challenges to rice production. We have selected chilling-tolerant TILLING of Donganbyeo, which showed significantly vigorous growth relative to wild-type plants under chilling stress conditions (10–12°C). We performed a comparative transcriptome analysis using a chilling-tolerant line and wild-type plant with the objective to evaluate genomic responses to chilling stress and to identify chilling inducible genes. Functional enrichment analysis results demonstrated that a large proportion of chilling-inducible genes were associated with certain biological pathways, e.g., monosaccharide catabolic processes, reflecting the energy requirements necessary for adaptation to sub-optimal temperatures in plants. Extremely low correlation coefficients in a range of −0.07 to 0.04 were detected between plant responses to chilling stress and different abiotic stress conditions such as drought, salt, cold, and heat; these results imply that plants might exploit strikingly different response mechanisms against stress conditions. The largest subnetwork, which was composed of 78 chilling-specific inducible genes, was found in the tolerant plants, but not in the wild-type plants, which probably implies the existence of a delicate and harmonious signaling pathway setup in the tolerant plants. Expression patterns of 20 chilling-responsive genes were assessed via abiotic stress treatments and phytohormone treatments. About 80% of the tested chilling-inducible genes were upregulated by exogenous abscisic acid (ABA) treatment. The results of this study may prove useful in elucidating the chilling-response pathway and in the development of chilling-tolerant rice varieties.
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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) | 0.001 | 0.001 |
| 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) | 0.001 | 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".