Identification of miRNAs Involved in Lipid Metabolism and Tuber Development in Cyperus esculentus L.
Bibliographic record
Abstract
L.) is recognized for its high oil and oleic acid content in underground tubers. However, the molecular mechanisms governing growth, development, and fatty acid accumulation in these tubers are not well understood. This study employed gas chromatography-mass spectrometry (GC-MS) and small RNA sequencing on tiger nut tubers across five developmental stages. The findings indicate that the critical period for accumulating dry matter and oils, particularly oleic acid, occurs between 35 and 75 days after tuber formation. A total of 183 microRNAs (miRNAs) were identified, comprising 31 known and 152 novel miRNAs. Approximately half of these miRNAs (such as ces-miR156b-3p and ces-miR166a-3p) exhibited differential expression during and around the key periods of metabolite synthesis. The predicted target genes of these miRNAs were significantly enriched in glycerate 3-phosphate metabolism and cell growth processes. Furthermore, 13 miRNA-mRNA interaction modules related to oil accumulation and tuber growth were identified, and these target genes' expression levels showed significant differences during the tuber developmental stages. These findings advance the understanding of the molecular mechanisms underlying tuber development and oil accumulation in tiger nut.
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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) | 0.000 | 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 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".