Genome‐wide analysis, metabolomics, and transcriptomics reveal the molecular basis of <i>ZlRc</i> overexpression in promoting phenolic compound accumulation in rice seeds
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".