An integrative approach to identify hexaploid wheat miRNAome associated with development and tolerance to abiotic stress
Bibliographic record
Abstract
BACKGROUND: Wheat is a major staple crop with broad adaptability to a wide range of environmental conditions. This adaptability involves several stress and developmentally responsive genes, in which microRNAs (miRNAs) have emerged as important regulatory factors. However, the currently used approaches to identify miRNAs in this polyploid complex system focus on conserved and highly expressed miRNAs avoiding regularly those that are often lineage-specific, condition-specific, or appeared recently in evolution. In addition, many environmental and biological factors affecting miRNA expression were not yet considered, resulting still in an incomplete repertoire of wheat miRNAs. RESULTS: We developed a conservation-independent technique based on an integrative approach that combines machine learning, bioinformatic tools, biological insights of known miRNA expression profiles and universal criteria of plant miRNAs to identify miRNAs with more confidence. The developed pipeline can potentially identify novel wheat miRNAs that share features common to several species or that are species specific or clade specific. It allowed the discovery of 199 miRNA candidates associated with different abiotic stresses and development stages. We also highlight from the raw data 267 miRNAs conserved with 43 miRBase families. The predicted miRNAs are highly associated with abiotic stress responses, tolerance and development. GO enrichment analysis showed that they may play biological and physiological roles associated with cold, salt and aluminum (Al) through auxin signaling pathways, regulation of gene expression, ubiquitination, transport, carbohydrates, gibberellins, lipid, glutathione and secondary metabolism, photosynthesis, as well as floral transition and flowering. CONCLUSION: This approach provides a broad repertoire of hexaploid wheat miRNAs associated with abiotic stress responses, tolerance and development. These valuable resources of expressed wheat miRNAs will help in elucidating the regulatory mechanisms involved in freezing and Al responses and tolerance mechanisms as well as for development and flowering. In the long term, it may help in breeding stress tolerant plants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".