Cold acclimation induces distinctive changes in the chromatin state and transcript levels of <i><scp>COR</scp></i> genes in <i>Cannabis sativa</i> varieties with contrasting cold acclimation capacities
Bibliographic record
Abstract
Little is known about the capacity of Cannabis sativa to cold-acclimate and develop freezing tolerance. This study investigates the cold acclimation (CA) capacity of nine C. sativa varieties and the underlying genetic and epigenetic responses. The varieties were divided into three groups based on their contrasting CA capacities by comparing the survival of non-acclimated and cold-acclimated plants in whole-plant freeze tests. In response to the CA treatment, all varieties accumulated soluble sugars but only the varieties with superior capacity for CA could maintain higher levels throughout the treatment. In addition, the varieties that acclimated most efficiently accumulated higher transcript levels of cold-regulated (COR) genes and genes involved in de novo DNA methylation while displaying locus- and variety-specific changes in the levels of H3K9ac, H3K27me3 and methylcytosine (MeC) during CA. Furthermore, these hardy C. sativa varieties displayed significant increases in MeC levels at COR gene loci when deacclimated, suggesting a role for locus-specific DNA methylation in deacclimation. This study uncovers the molecular mechanisms underlying CA in C. sativa and reveals higher levels of complexity regarding how genetic, epigenetic and environmental factors intertwine.
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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".