Dose effects of restorer gene modulate pollen fertility in cotton CMS-D2 restorer lines via auxin signaling and flavonoid biosynthesis
Bibliographic record
Abstract
Abstract CMS-D2 is an economical and effective system for producing hybrid cotton seeds than artificial and chemical emasculation methods. However, the unstable restoring ability of restorer lines is a main barrier in the large-scale application of "three-line" hybrid cotton. Our phenotypic investigation determined that the homozygous Rf1Rf1 allelic genotype had a stronger ability to generate fertile pollen than the heterozygous Rf1rf1 allelic genotype. To decipher the genetic mechanisms that control the differential levels of pollen fertility, an integrated metabolomic and transcriptomic analysis was performed on pollen grains at two environments using four cotton genotypes differing in Rf1 alleles or cytoplasm. Totally 5,391 differential metabolite features were detected, and 369 specific differential metabolites (DMs) were identified between homozygous and heterozygous Rf1 allelic genotypes with CMS-D2 cytoplasm. Additionally, transcriptome analysis identified 2,490 differentially expressed genes (DEGs) and 96 unique hub DEGs with dynamic regulation in this comparative combination. Further integrated analyses revealed that several key DEGs and DMs involved in indole biosynthesis, flavonoid biosynthesis, and sugar metabolism had strong network linkage with fertility restoration. In vitro application of auxin analogue NAA and inhibitor Auxinole confirmed that over-activated auxin signaling might inhibit pollen development whereas suppressing auxin signaling partially promoted pollen development in CMS-D2 cotton. Our results provide new insight into how the dosage effects of the Rf1 gene regulate pollen fertility of CMS-D2 cotton.
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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".