Comparative restriction enzyme analysis of methylation (CREAM) reveals methylome variability within a clonal in vitro cannabis population
Bibliographic record
Abstract
The primary focus of medicinal cannabis research is to ensure the stability of cannabis lines for consistent administration of chemically uniform products to patients. In recent years, tissue culture has emerged as a valuable technique for genetic preservation and rapid multiplication of cannabis clones. However, there is concern that the physical and chemical conditions of the growing media can induce somaclonal variation, potentially impacting the viability and uniformity of clones. To address this concern, we developed Comparative Restriction Enzyme Analysis of Methylation (CREAM), a novel method to assess DNA methylation patterns and used it to study a population of 78 cannabis clones maintained in tissue culture. Through bioinformatics analysis of the methylome, we successfully detected 2,272 polymorphic methylated regions among the clones. Remarkably, our results demonstrated that DNA methylation patterns were preserved across subcultures within the clonal population, allowing us to distinguish between two subsets of clonal lines used in this study. These findings significantly contribute to our understanding of the epigenetic variability within clonal lines in medicinal cannabis produced through tissue culture techniques. This knowledge is crucial for understanding the effects of tissue culture on DNA methylation and ensuring the consistency and reliability of medicinal cannabis products with therapeutic properties. Additionally, the CREAM method is a fast and affordable technology to get a first glimpse at methylation in a biological system. It offers a valuable tool for studying epigenetic variation in other plant species, thereby facilitating broader applications in plant biotechnology and crop improvement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".