Translational repression of <scp><i>FZP</i></scp> mediated by <scp>CU</scp>‐rich element/<scp>OsPTB</scp> interactions modulates panicle development in rice
Bibliographic record
Abstract
Panicle development is an important determinant of the grain number in rice. A thorough characterization of the molecular mechanism underlying panicle development will lead to improved breeding of high-yielding rice varieties. Frizzy Panicle (FZP), a critical gene for panicle development, is regulated by OsBZR1 and OsARFs at the transcriptional stage. However, the translational modulation of FZP has not been reported. We reveal that the CU-rich elements (CUREs) in the 3' UTR of the FZP mRNA are crucial for efficient FZP translation. The knockout of CUREs in the FZP 3' UTR or the over-expression of the FZP 3' UTR fragment containing CUREs resulted in an increase in FZP mRNA translation efficiency. Moreover, the number of secondary branches (NSB) and the grain number per panicle (GNP) decreased in the transformed rice plants. The CUREs in the 3' UTR of FZP mRNA were verified as the targets of the polypyrimidine tract-binding proteins OsPTB1 and OsPTB2 in rice. Both OsPTB1 and OsPTB2 were highly expressed in young panicles. The knockout of OsPTB1/2 resulted in an increase in the FZP translational efficiency and a decrease in the NSB and GNP. Furthermore, the over-expression of OsPTB1/2 decreased the translation of the reporter gene fused to FZP 3' UTR in vivo and in vitro. These results suggest that OsPTB1/2 can mediate FZP translational repression by interacting with CUREs in the 3' UTR of FZP mRNA, leading to changes in the NSB and GNP. Accordingly, in addition to transcriptional regulation, FZP expression is also fine-tuned at the translational stage during rice panicle development.
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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) | 0.001 | 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".