Unraveling the role of MADS transcription factor complexes in apple tree dormancy using sequential DAP-seq
Bibliographic record
Abstract
Abstract Background The effect of global warming on dormancy and flowering patterns of crop trees threatens world-wide fruit production and food security. In Rosaceous tree species, it is believed that a group of genes encoding MADS transcription factors (TFs) controls temperature-mediated dormancy cycle. These genes are similar to SHORT VEGETATIVE PHASE ( SVP ) from Arabidopsis thaliana and referred as DORMANCY-ASSOCIATED MADS-BOX ( DAM ) genes. Results By making use of apple tree ( Malus x domestica ) as a model for Rosaceous species, we have investigated the function of MADS TFs during the dormancy cycle. We found that MdDAM and other dormancy related MADS TFs form multimeric complexes with MdSVPa, and that MdSVPa is essential for the transcriptional complex activity. Then, for the first time in non-model plant species, we performed sequential DNA Affinity Purification sequencing (seq-DAP-seq) to define the genome-wide binding sites of these MADS TF complexes. Target genes associated with the binding sites were identified by combining seq-DAP-seq data with transcriptomics datasets obtained by the inducible glucocorticoid receptor expression system, and reanalyzing preexisting data related to dormancy cycle in apple trees. Conclusion We have determined a gene regulatory network formed by MdSVPa-containing complexes that regulate the dormancy cycle in apple trees in response to environmental cues. Key genes identified with our genomic approach and the elucidated regulatory relationships provide leads for breeding fruit trees better adapted to changing climate conditions. Moreover, we provide novel molecular evidence on the evolutionary functional segregation between DAM and SVP proteins in the Rosaceae family.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".