<i>Fraxinus excelsior</i> updated long-read genome reveals the importance of MADS-box genes in tolerance mechanisms against ash dieback
Bibliographic record
Abstract
Ash dieback caused by the fungus Hymenoscyphus fraxineus has devastated the European ash tree population since it arrived in Europe in 1992. Great effort has been put into breeding programs to increase the genetic diversity of ash trees and find heritable genetic markers associated with resistance, or tolerance mechanisms, to ash dieback. To facilitate identification of molecular markers, we used Oxford Nanopore Technologies combined with Illumina sequencing to obtain an accurate and contiguous ash genome. We used this genome to reanalyze transcriptome data from a Danish ash panel of 182 tree accessions. Using associative transcriptomics, we identified 175 gene expression markers, including 11 genes annotated as dormancy MADS-box transcription factors which are associated with ash bud dormancy, flowering, and senescence. We hypothesize that tolerant trees both break dormancy earlier in the year by increasing the expression of flowering-related SOC1 MADS-box and reducing the expression of SVP-like MADS-box, whilst also accelerating senescence by increasing the expression of JOINTLESS MADS-box genes. DNA methylation differences in the promoters of MADS-box genes between 1 tolerant and 1 susceptible tree indicate potential epigenetic regulation of these traits.
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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".