Physiological changes and transcriptome profiling in Saccharum spontaneum L. leaf under water stress and re-watering conditions
Bibliographic record
Abstract
As the polyploidy progenitor of modern sugarcane, Saccharum spontaneum is considered to be a valuable resistance source to various biotic and abiotic stresses. However, little has been reported on the mechanism of drought tolerance in S. spontaneum. Herein, the physiological changes of S. spontaneum GXS87-16 at three water-deficit levels (mild, moderate, and severe) and after re-watering during the elongation stage were investigated. RNA sequencing was utilized for global transcriptome profiling of GXS87-16 under severe drought and re-watered conditions. There were significant alterations in the physiological parameters of GXS87-16 in response to drought stress and then recovered differently after re-watering. A total of 1569 differentially expressed genes (DEGs) associated with water stress and re-watering were identified. Notably, the majority of the DEGs were induced by stress. GO functional annotations and KEGG pathway analysis assigned the DEGs to 47 GO categories and 93 pathway categories. The pathway categories were involved in various processes, such as RNA transport, mRNA surveillance, plant hormone signal transduction, and plant-pathogen interaction. The reliability of the RNA-seq results was confirmed by qRT-PCR. This study shed light on the regulatory processes of drought tolerance in S. spontaneum and identifies useful genes for genetic improvement of drought tolerance 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 | 0.000 | 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".