Sex-dependent gene expression during puberty has potential mechanistic implications for the development of major depressive disorder
Bibliographic record
Abstract
Major Depressive Disorder (MDD) is a complex illness with heterogeneous symptom profiles, affecting 5% of the global population. Recent molecular studies across biological systems suggest that the manifestations of MDD are significantly shaped by sex. In response, this narrative review organizes up-to-date findings on sex-specific, MDD-related gene expression into multiple biological systems-primarily available from adult human and rodent studies. This adult-centric focus highlights a critical gap: when and how sex-dependent, MDD-associated gene expression patterns emerge, and contribute to adult manifestations. We identify puberty as a sensitive developmental window during which sex-dependent gene expression and regulatory patterns may emerge and carry their impacts into adulthood. We define this as a sex-specific framework established during puberty. To address the "how," we synthesize empirical evidence on the molecular mechanisms associated with the emergence and long-term influence of this framework. Finally, we explore how perturbations acting on this framework may further bifurcate expression patterns and ultimately give rise to the sex-divergent manifestations of MDD observed in adulthood. Overall, this review positions puberty as a key developmental window and calls for future sex-stratified functional genomic studies that span the pubertal timeline.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".