Major depressive disorder and anti-depressant therapy markedly alters the human follicular niche DNA methylome
Bibliographic record
Abstract
In brief: Generalized anxiety disorder, major depressive disorder and their treatment selective serotonin reuptake inhibitors (SSRIs) impact 4-17% of pregnancies worldwide and alter the epigenome of numerous tissues, but their effects on the ovarian follicle are unknown. This study profiles the methylome of granulosa cells, revealing novel epigenetic pathways and molecular mechanisms altered by mental health conditions and their treatments. Abstract: Generalized anxiety and major depressive disorders (GAD/MDD) impact 4-17% of pregnancies worldwide. GAD/MDD and SSRIs alter the epigenome of numerous tissues; however, their effect on the ovarian follicular niche is unknown. In this study, we determined SSRI concentrations in the follicular fluid and matched patients by clinical and stimulation characteristics, and then grouped them into three groups: i) treated GAD/MDD (n = 10), ii) untreated GAD/MDD (n = 4), and iii) control (n = 10). DNA methylation sequencing was performed on granulosa cells using the Illumina TruSeq Methyl Capture EPIC kit. For patients with untreated GAD/MDD, we identified 3,829 differentially methylated sites (DMSs). Pathway analysis revealed an enrichment in genes involved in catabolism and immune response for the hypomethylated DMSs and hypermethylated DMSs were associated with protein localization and cellular transport. When assessing the effect of SSRI treatment, we identified 3,690 DMSs. Hypomethylated DMSs were associated with genes involved in cytoskeleton organization and cellular transport, whereas hypermethylated DMSs were associated with apoptosis and cell cycle. This is the first study profiling the methylome of human granulosa cells from patients with treated or untreated GAD/MDD. This study provides a valuable dataset describing the effects of SSRI on cells in the ovarian follicular niche.
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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".