A role for seed storage proteins in<i>Arabidopsis</i>seed longevity
Bibliographic record
Abstract
Proteomics approaches have been a useful tool for determining the biological roles and functions of individual proteins and identifying the molecular mechanisms that govern seed germination, vigour and viability in response to ageing. In this work the dry seed proteome of four Arabidopsis thaliana genotypes, that carry introgression fragments at the position of seed longevity quantitative trait loci and as a result display different levels of seed longevity, was investigated. Seeds at two physiological states, after-ripened seeds that had the full germination ability and aged (stored) seeds of which the germination ability was severely reduced, were compared. Aged dry seed proteomes were markedly different from the after-ripened and reflected the seed longevity level of the four genotypes, despite the fact that dry seeds are metabolically quiescent. Results confirmed the role of antioxidant systems, notably vitamin E, and indicated that protection and maintenance of the translation machinery and energy pathways are essential for seed longevity. Moreover, a new role for seed storage proteins (SSPs) was identified in dry seeds during ageing. Cruciferins (CRUs) are the most abundant SSPs in Arabidopsis and seeds of a triple mutant for three CRU isoforms (crua crub cruc) were more sensitive to artificial ageing and their seed proteins were highly oxidized compared with wild-type seeds. These results confirm that oxidation is involved in seed deterioration and that SSPs buffer the seed from oxidative stress, thus protecting important proteins required for seed germination and seedling formation.
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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".