Seed traits are pleiotropically regulated by the flowering time gene <i>PERPETUAL FLOWERING 1</i> (<i>PEP1</i>) in the perennial <i>Arabis alpina</i>
Bibliographic record
Abstract
The life cycles of plants are characterized by two major life history transitions-germination and the initiation of flowering-the timing of which are important determinants of fitness. Unlike annuals, which make the transition from the vegetative to reproductive phase only once, perennials iterate reproduction in successive years. The floral repressor PERPETUAL FLOWERING 1 (PEP1), an ortholog of FLOWERING LOCUS C, in the alpine perennial Arabis alpina ensures the continuation of vegetative growth after flowering and thereby restricts the duration of the flowering episode. We performed greenhouse and garden experiments to compare flowering phenology, fecundity and seed traits between A. alpina accessions that have a functional PEP1 allele and flower seasonally and pep1 mutants and accessions that carry lesions in PEP1 and flower perpetually. In the garden, perpetual genotypes flower asynchronously and show higher winter mortality than seasonal ones. PEP1 also pleiotropically regulates seed dormancy and longevity in a way that is functionally divergent from FLC. Seeds from perpetual genotypes have shallow dormancy and reduced longevity regardless of whether they after-ripened in plants grown in the greenhouse or in the experimental garden. These results suggest that perpetual genotypes have higher mortality during winter but compensate by showing higher seedling establishment. Differences in seed traits between seasonal and perpetual genotypes are also coupled with differences in hormone sensitivity and expression of genes involved in hormonal pathways. Our study highlights the existence of pleiotropic regulation of seed traits by hub developmental regulators such as PEP1, suggesting that seed and flowering traits in perennial plants might be optimized in a coordinated fashion.
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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".