Comparative Transcriptome Analysis Reveals Potential Gene Modules Associated with Cold Tolerance in Sugarcane (Saccharum officinarum L.)
Bibliographic record
Abstract
Abstract Sugarcane is an important crop worldwide, and most sugar is derived directly from sugarcane. Due to its thermophilic nature, the yield of sugarcane is largely influenced by extreme climate conditions, especially cold stress. Therefore, the development of sugarcane with improved cold tolerance is an important goal. However, little is known about the multiple mechanisms underlying cold acclimation at the bud stage in sugarcane. In this study, we emphasized that sensitivity to cold stress was higher for the sugarcane variety ROC22 than for GT42, as determined by physical signs, including bud growth capacity, relative conductivity, malonaldehyde contents, and soluble sugar contents. To understand the factors contributing to the difference in cold tolerance between ROC22 and GT42, comparative transcriptome analyses were performed. We found that genes involved in the regulation of the stability of the membrane system were the relative determinants of difference in cold tolerance. Additionally, genes related to protein kinase activity, starch metabolism, and calcium signal transduction were associated with cold tolerance. Finally, 25 candidate genes, including 23 variety-specific and 2 common genes, and 7 transcription factors were screened out for understanding the possible cold resistance mechanism. The findings of this study provide candidate gene resources for cold resistance and will improve our understanding of the regulation of cold tolerance at the bud stage in sugarcane.
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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 | 0.001 | 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".