Bibliographic record
Abstract
Circular RNAs (circRNAs) are a unique class of noncoding RNAs with covalently closed loop structures that play a crucial role in gene regulation in eukaryotes. In cotton ( Gossypium spp.), microRNAs (miRNAs) are central to posttranscriptional gene regulation, influencing growth, development, and stress responses. This study investigates the role of circRNAs as molecular sponges in regulating miRNA activity in cotton. We first describe the biogenesis of circRNAs, their structural classification, and key features such as stability and tissue-specific expression. We then examine in detail the mechanisms by which circRNAs sequester miRNAs, including a competing endogenous RNA (ceRNA) network framework and experimental approaches to validate their sponging activity. We then highlight the regulatory roles of circRNAs in cotton fiber development, stress adaptation, and defense signaling through the circRNA-miRNA-mRNA axis. This study also reviews the progress in circRNA discovery using high-throughput sequencing and computational methods, as well as the challenges faced in their annotation. A key case study illustrates how specific circular RNAs act as "sponges" for miR156 and miR828, regulating SPL transcription factors and influencing fiber phenotype. Finally, we explore the potential of circular RNAs as biotechnological tools and molecular targets in cotton breeding programs. This study highlights the potential of circular RNA research for improving cotton quality and stress tolerance while also identifying knowledge gaps and future directions for multi-omics integration and genome editing 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 | 0.001 | 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".