Arabidopsis <i>glabra2</i> mutant seeds deficient in mucilage biosynthesis produce more oil
Bibliographic record
Abstract
Seed oil, one of the major seed storage compounds in plants, is of great economic importance for human consumption, as an industrial raw material and as a source of biofuels. Thus, improving the seed oil yield in crops is an important objective. The GLABRA2 (GL2) gene in Arabidopsis thaliana encodes a transcription factor that is required for the proper differentiation of several epidermal cell types. GL2 has also been shown to regulate seed oil levels, as a loss-of-function mutation in the GL2 gene results in plants with a higher seed oil content than wild-type. We have extended this observation by showing that loss-of-function mutations in several positive regulators of GL2 also result in a high seed oil phenotype. The GL2 gene is expressed in both the seed coat and embryo, but the embryo is the main site of seed oil accumulation. Surprisingly, our results indicate that it is loss of GL2 activity in the seed coat, not the embryo, that contributes to the high seed oil phenotype. One target of GL2 in the seed coat is the gene MUCILAGE MODIFIED 4 (MUM4), which encodes a rhamnose synthase that is required for seed mucilage biosynthesis. We found that mum4 mutant seeds, like those of gl2 mutants, have an increased seed oil content in comparison with wild-type. Therefore, GL2 regulates seed oil production at least partly through its influence on MUM4 expression in the seed coat. We propose that gl2 mutant seeds produce more oil due to increased carbon allocation to the embryo in the absence of seed coat mucilage biosynthesis.
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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".