Exploiting Epigenetic Variation for Crop Improvement in the Emerging Oilseed Crop Camelina Sativa
Bibliographic record
Abstract
In the emerging oilseed crop Camelina sativa, breeding efforts have been focused on optimizing seed size and oil quality traits to improve its productivity for Canadian agriculture.Considering that epigenetic mechanisms, such as DNA methylation, undergo dynamic changes throughout seed development, we hypothesized that it may play an important regulatory role in seed traits.We successfully knocked out the epigenetic regulator DEFECTIVE IN RNA-DIRECTED DNA METHYLATION (DRD1) via CRISPR/Cas9 technology.Homozygous drd1 mutants showed a global reduction in DNA methylation predominantly within transposable elements in the upstream region of genes.Loss of DNA methylation in the drd1 seed was found to have a relatively small effect on the expression of genes.We did, however, detect transcriptional changes in 69 hypomethylated genes, of which two candidate genes, FRUCTOKINASE (FRK1) and ACYL CARRIER PROTEIN 2 (ACP2), are predicted to be involved in seed development.This study showcases the potential influence of epigenetic marks on gene expression and offers insights on the functional relevance of DNA methylation in seed development.
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) | 0.000 | 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) | 0.002 | 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".