MicroRNA regulation in sugarcane: Biological functions and breeding perspectives
Bibliographic record
Abstract
Sugarcane, a key crop for both sugar production and bioenergy, is increasingly challenged by climate change and environmental stresses, underscoring the urgent need for high-yield and stress-tolerant varieties. Traditional breeding is constrained by the crop’s complex genome, while molecular breeding provides a more precise and efficient alternative. Identifying key regulators of agronomically important traits is central to this process. MicroRNAs (miRNAs), which govern plant growth, development, and stress responses, represent promising molecular resources for advancing sugarcane genetic improvement. However, knowledge of miRNA-mediated regulation in sugarcane is still limited and fragmented. Recent studies have begun to elucidate their roles, thereby gradually revealing the complexity of miRNA-mediated regulation. This review offers a comprehensive synthesis of recent research on sugarcane miRNAs, encompassing their regulatory roles and target validation strategies. Notably, it represents the first comprehensive overview of miRNA research in sugarcane, thereby representing a significant milestone in the field. Based on recent findings, we also propose future directions for miRNA studies to facilitate molecular breeding efforts and improve stress tolerance in sugarcane. • Comprehensive overview of sugarcane miRNAs. • Emphasizing their roles in sugarcane development and stress responses. • Proposing opportunities and future directions for miRNA-mediated sugarcane improvement.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".