Deep sequencing of wheat sRNA transcriptome reveals distinct temporal expression pattern of miRNAs in response to heat, light and UV
Bibliographic record
Abstract
Understanding of plant adaptation to abiotic stresses has implications in plant breeding, especially in the context of climate change. MicroRNAs (miRNAs) and short interfering RNAs play a crucial role in gene regulation. Here, wheat plants were exposed to one of the following stresses: continuous light, heat or ultraviolet radiations over five consecutive days and leaf tissues from three biological replicates were harvested at 0, 1, 2, 3, 7 and 10 days after treatment (DAT). A total of 72 small RNA libraries were sequenced on the Illumina platform generating ~524 million reads corresponding to ~129 million distinct tags from which 232 conserved miRNAs were identified. The expression levels of 1, 2 and 79 miRNAs were affected by ultraviolet radiation, continuous light and heat, respectively. Approximately 55% of the differentially expressed miRNAs were downregulated at 0 and 1 DAT including miR398, miR528 and miR156 that control mRNAs involved in activation of signal transduction pathways and flowering. Other putative targets included histone variants and methyltransferases. These results suggest a temporal miRNA-guided post-transcriptional regulation that enables wheat to respond to abiotic stresses, particularly heat. Designing novel wheat breeding strategies such as regulatory gene-based marker assisted selection depends on accurate identification of stress induced miRNAs.
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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) | 0.001 | 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".