MicroRNA-guided regulation of heat stress response in wheat
Bibliographic record
Abstract
BACKGROUND: With rising global temperature, understanding plants' adaptation to heat stress has implications in plant breeding. MicroRNAs (miRNAs) are small, non-coding, regulatory RNAs guiding gene expression at the post-transcriptional level. In this study, small RNAs and the degradome (parallel analysis of RNA ends) of leaf tissues collected from control and heat-stressed wheat plants immediately at the end of the stress period and 1 and 4 days later were analysed. RESULTS: Sequencing of 24 small RNA libraries produced 55.2 M reads while 404 M reads were obtained from the corresponding 24 PARE libraries. From these, 202 miRNAs were ascertained, of which mature miRNA evidence was obtained for 104 and 36 were found to be differentially expressed after heat stress. The PARE analysis identified 589 transcripts targeted by 84 of the ascertained miRNAs. PARE sequencing validated the targets of the conserved members of miRNA156, miR166 and miR393 families as squamosa promoter-binding-like, homeobox leucine-zipper and transport inhibitor responsive proteins, respectively. Heat stress responsive miRNA targeted superoxide dismutases and an array of homeobox leucine-zipper proteins, F-box proteins and protein kinases. Query of miRNA targets to interactome databases revealed a predominant association of stress responses such as signalling, antioxidant activity and ubiquitination to superoxide dismutases, F-box proteins, pentatricopeptide repeat-containing proteins and mitochondrial transcription termination factor-like proteins. CONCLUSION: The interlaced data set generated in this study identified and validated heat stress regulated miRNAs and their target genes associated with thermotolerance. Such accurate identification and validation of miRNAs and their target genes are essential to develop novel regulatory gene-based breeding strategies.
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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".