Advances in Physiological, Transcriptomic, Proteomic, Metabolomic, and Molecular Genetic Approaches for Enhancing Mango Fruit Quality
Bibliographic record
Abstract
L.) is a nutritionally important fruit of high nutritive value, delicious in taste with an attractive aroma. Due to their antioxidant and therapeutic potential, mango fruits are receiving special attention in biochemical and pharmacognosy-based studies. Fruit quality determines consumer's acceptance, and hence, understanding the physiological, biochemical, and molecular basis of fruit development, maturity, ripening, and storage is essential. Transcriptomic, metabolomic, proteomic, and molecular genetic approaches have led to the identification of key genes, metabolites, protein candidates, and quantitative trait loci that are associated with enhanced mango fruit quality. The major pathways that determine the fruit quality include amino acid metabolism, plant hormone signaling, carbohydrate metabolism and transport, cell wall biosynthesis and degradation, flavonoid and anthocyanin biosynthesis, and carotenoid metabolism. Expression of the polygalacturonase, cutin synthase, pectin methyl esterase, pectate lyase, β-galactosidase, and ethylene biosynthesis enzymes are related to mango fruit ripening, flavor, firmness, softening, and other quality processes, while genes involved in the MAPK signaling pathway, heat shock proteins, hormone signaling, and phenylpropanoid biosynthesis are associated with diseases. Metabolomics identified volatiles, organic acids, amino acids, and various other compounds that determine the characteristic flavor and aroma of the mango fruit. Molecular markers differentiate the mango cultivars based on their geographical origins. Genetic linkage maps and quantitative trait loci studies identified regions in the genome that are associated with economically important traits. The review summarizes the applications of omics techniques and their potential applications toward understanding mango fruit physiology and their usefulness in future mango breeding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 teacher head, 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".