Cannabis significantly alters DNA methylation of the human ovarian follicle in a concentration-dependent manner
Why this work is in the frame
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Bibliographic record
Abstract
Cannabis is increasingly consumed by women of childbearing age, and the reproductive and epigenetic effects are unknown. The purpose of this study was to evaluate the potential epigenetic implications of cannabis use on the female ovarian follicle. Whole-genome methylation was assessed in granulosa cells from 14 matched case-control patients. Exposure status was determined by liquid chromatography-mass spectrometry (LC-MS/MS) measurements of five cannabis-derived phytocannabinoids in follicular fluid. DNA methylation was measured using the Illumina TruSeq Methyl Capture EPIC kit. Differential methylation, pathway analysis and correlation analysis were performed. We identified 3679 differentially methylated sites, with two-thirds affecting coding genes. A hotspot region on chromosome 9 was associated with two genomic features, a zinc-finger protein (ZFP37) and a long non-coding RNA (FAM225B). There were 2214 differentially methylated genomic features, 19 of which have been previously implicated in cannabis-related epigenetic modifications in other organ systems. Pathway analysis revealed enrichment in G protein-coupled receptor signaling, cellular transport, immune response and proliferation. Applying strict criteria, we identified 71 differentially methylated regions, none of which were previously annotated in this context. Finally, correlation analysis revealed 16 unique genomic features affected by cannabis use in a concentration-dependent manner. Of these, the histone methyltransferases SMYD3 and ZFP37 were hypomethylated, possibly implicating histone modifications as well. Herein, we provide the first DNA methylation profile of human granulosa cells exposed to cannabis. With cannabis increasingly legalized worldwide, further investigation into the heritability and functional consequences of these effects is critical for clinical consultation and for legalization guidelines.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it