DNA methylation, but not microRNA expression, is affected by in vitro THC exposure in bovine granulosa cells
Bibliographic record
Abstract
Abstract Background A global increase in cannabis use has led to questions about its effects on fertility. The rise in consumption amongst women of reproductive age is a growing concern, as this group is vulnerable in terms of reproductive health. Ample evidence suggests that the psychoactive component of cannabis, Δ9-Tetrahydrocannabinol (THC), interacts with the endocannabinoid system (ECS), that helps regulate mammalian reproduction. This study aimed to research the epigenetic effects of THC in bovine granulosa cells (GCs) by (1) investigating global DNA methylation via measuring 5-mC and 5-hmC levels; (2) measuring key methylation regulators, including the methylating enzymes DNMT1, DNMT3a, DNMT3b and the demethylases TDG and TET1/2/3; and (3) assessing fertility-associated miRNAs key in developmental competency, including miR-21, -155, -33b, -324 and -346. Methods Bovine GCs were used as a translational model for reproductive toxicity in humans. To determine THC effects, GCs were isolated from Cumulus-Oocyte-Complexes (COCs) from bovine ovaries, cultured in vitro for 7 days, or until confluent, and cryopreserved at passage 1 (P1). For experimentation, cells were thawed, cultured until passage 2 (P2), serum restricted for 24-h and treated for 24-h in one of five groups: control, vehicle (1:1:18 ethanol: tween: saline) and three clinically relevant THC doses (0.032, 0.32 and 3.2 μM). Global methylation was assessed by measuring 5-mC and 5-hmC levels with flow cytometry. To assess mRNA and protein expression of methylation regulators and miRNA profiles, qPCR and Western Blotting were utilized. Shapiro-Wilk test was used to determine normality within datasets. One-way ANOVA was applied to determine statistical significance using GraphPad Prism 6.0.0. Results Results indicate a significant decrease (p = 0.0435) in 5-mC levels following low THC exposure, while no changes were observed in 5-hmC levels. A significant increase in DNMT1 following high THC exposure at the RNA level (p < 0.05) and a significant increase following low THC exposure at the protein level (p = 0.0048) were also observed. No significant differences were observed in DNMT3a/3b, TDG, TET1/2/3 mRNAs or in any of the miRNAs analyzed. Conclusions This research suggests that THC mainly affects DNA methylation, but not miRNA profiles, ultimately altering gene expression and likely impairing oocyte competence, maturation, and fertilization potential.
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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".