Genomic insights into the domestication and genetic basis of yield in papaya
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Papaya (Carica papaya L.) is an important tropical and subtropical fruit crop, and understanding its genome is essential for breeding. In this study, we assembled a high-quality genome of 344.17 Mb for the newly cultivated papaya ‘Zihui’, which contains 22 250 protein-coding genes. By integrating 201 resequenced papaya genomes, we identified four distinct papaya groups and a 34 Mb genomic region with strong domestication selection signals. Within these regions, two key genes associated with papaya yield were discovered: Cp_zihui06549, encoding a leucine-rich receptor-like protein kinase, and Cp_zihui06768, encoding the accumulation of photosystem one 1 (APO1) protein. Heterologous expression of Cp_zihui06549 in tomato confirmed that the total number of fruits in transgenic lines more than doubled compared to wild-type plants, resulting in a significant yield increase. Furthermore, we constructed a pan-genome of papaya and obtained a 77.41 Mb nonreference sequence containing 1543 genes. Within this pan-genome, 2483 variable genes, we detected, including four genes annotated as the ‘terpene synthase activity’ Gene Ontology term, which were lost in cultivars during domestication. Finally, gene retention analyses were performed using gene presence and absence variation data and differentially expressed genes across various tissues and organs. This study provides valuable insights into the genes and loci associated with phenotypes and domestication processes, laying a solid foundation for future papaya breeding efforts.
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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 | 0.000 | 0.001 |
| 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