Identification and Pleiotropic Effect Analysis of GSE5 on Rice Chalkiness and Grain Shape
Bibliographic record
Abstract
Chalkiness is one of several major restricting factors for the improvement of rice quality. Although many chalkiness-related quantitative trait loci have been mapped, only a small number of genes have been cloned to date. In this study, the candidate gene GSE5 of a major quantitative trait locus (QTL) for rice chalkiness, qDEC5, was identified by map-based cloning. Phenotyping and haplotype analysis of proActin:GSE5 transgenic line, gse5-cr mutant, and 69 rice varieties further confirmed that GSE5 had the pleiotropic effects and regulated both chalkiness and grain shape. Genetic analysis showed GSE5 was a dominant gene for grain length and a semi-dominant gene for grain width and chalkiness. The DNA interval closely linked to GSE5 was introgressed to Zhenshan 97B (ZB) based on molecular marker-assisted selection, and the improved ZB showed lower chalkiness and longer but smaller grains, which showed that GSE5 played an important role in breeding rice varieties with high yield and good quality. Transcriptomics, proteomics, and qRT-PCR analyses showed that thirty-nine genes associated with carbon and protein metabolism are regulated by GSE5 to affect the formation of chalkiness, including some newly discovered genes, such as OsCESA9, OsHSP70, OsTPS8, OsPFK04, OsSTA1, OsERdj3A, etc. The low-chalkiness lines showed higher amino sugar and nucleotide sugar metabolism at 10 days after pollination (DAP), lower carbohydrate metabolism at 15 DAP, and lower protein metabolism at 10 and 15 DAP. With heat shock at 34/30°C, rice chalkiness increased significantly; OsDjC10 and OsSUS3 were upregulated at 6 and 12 DAP, respectively, and OsGSTL2 was downregulated at 12 DAP. Our results identified the function and pleiotropic effects of qDEC5 dissected its genetic characteristics and the expression profiles of the genes affecting the chalkiness formation, and provided a theoretical basis and application value to harmoniously pursue high yield and good quality in rice production.
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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".