Insights into <scp>microRNA</scp>‐mediated interaction and regulation of metabolites in tomato
Bibliographic record
Abstract
microRNAs direct regulation of various metabolic pathways in plants and animals. miRNAs may be useful in developing novel/elite genotypes, with enhanced metabolites and disease resistance. We examined miRNAs in tomato. In tomato, miRNAs in the carotenoid pathway have not been fully elucidated. We examined the potential role of miRNAs in biosynthesis of carotenoids, transcript profiling of miRNAs and their possible targets (genes and transcription factors) at different development stages of tomato using stem-loop PCR and RT-qPCR. We also identified miRNAs targeting key flavonoid genes, such as chalcone isomerase (CHI), and dihydroflavonol-4-reductase (DFR). Distinct expression profiles of miRNAs and their targets were found in fruits of three tomato accessions, suggesting carotenoid regulation by miRNAs at various stages of fruit development. This was also confirmed using HPLC of the carotenoids. The present study may help in understanding possible regulation of carotenoid biosynthesis. The identified miRNAs can be exploited to enhance biosynthesis of different carotenoids in 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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".