Genome‐wide analysis, metabolomics, and transcriptomics reveal the molecular basis of <i>ZlRc</i> overexpression in promoting phenolic compound accumulation in rice seeds
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Bibliographic record
Abstract
Abstract Chinese wild rice ( Zizania latifolia ) is rich in phenolic compounds, particularly flavonoids. This study identified 203 basic helix‐loop‐helix (bHLHs) in Z. latifolia and showed that ZlbHLH196 ( Zla16G011250 ) corresponds to the ZlRc gene in Z. latifolia , with its protein product localizing to the nucleus. Notably, the pericarps of ZlRc ‐overexpressing (OE) rice are brown, whereas those of wild‐type (WT) rice are nonpigmented. The total phenolic, flavonoid, and proanthocyanidin contents, antioxidant activity, as well as enzyme inhibitory effects of ZlRc ‐OE rice were significantly higher than those of WT rice. Overall, 221 differential phenolic metabolites were identified between ZlRc ‐OE and WT rice, among which 198 were upregulated in the former. Additionally, a total of 227 differentially expressed genes were identified between ZlRc ‐OE and WT rice, with 173 upregulated. Kyoto Encyclopedia of Genes and Genomes annotation and enrichment analysis of phenolic metabolites revealed enhanced isoflavonoid, flavone, flavonol, and flavonoid biosynthesis pathways in ZlRc ‐OE rice, which, furthermore, showed a markedly upregulated expression and significantly higher activities of four key flavonoid biosynthesis–related enzymes (phenylalanine ammonia‐lyase, chalcone synthase, chalcone‐flavanone isomerase, and dihydroflavonol 4‐reductase). These findings show that ZlRc ‐overexpression promotes phenolic compound accumulation in rice seeds and can be used to bioaugment rice phenolic content.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it