Characterization of tae-miR156(s) and their response to abiotic stress in wheat (Triticum aestivum L.)
Bibliographic record
Abstract
The microRNA156 (miR156) has been widely studied in plants, however, the characterization of the miR156 family of genes in wheat and their expression patterns under abiotic stress are not completely clear. In this study, a total of 20 miR156 family members, referred to as tae-miR156a to tae-miR156t, were identified in wheat with their loci mapped to various chromosomes. These members were divided into five subgroups: miR156a/b/c/d/e/f, miR156g/h/i, miR156j/k, miR156l/m/n/o/p/q, and miR156r/s/t. They were highly conserved during evolution. The prediction of cis-elements in the tae-MIR156(s) promoter region revealed that the tae-MIR156(s) had diverse cis-acting elements; of these, 15 tae-MIR156(s) and 6 tae-MIR156(s) were found to be drought-responsive elements and cold-responsive elements, respectively. And the prediction target genes of tae-miR156(s) are mainly SPL transcription factor genes. Expression analysis based on quantitative real-time polymerase chain reaction (qRT‒PCR) showed that miR156(s) have different expression levels in the various wheat tissues, and the subgroups' response to abiotic stress varied. Among them, miR156g/h/i were strongly induced in the root of cold and heat stress, and miR156a/b/c/d/e/f were significantly increased in roots after drought stress, whereas miR156r/s/t were highly inhibited in leaves and roots after salt stress. These findings imply that tae-miR156(s) are involved in stress response in wheat, and they provide new fundamental knowledge for further analysis of the function of miR156 and its regulatory mechanism in response to abiotic stress.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".