Distinctions between sex and time in patterns of DNA methylation across puberty
Bibliographic record
Abstract
BACKGROUND: There are significant sex differences in human physiology and disease; the genomic sources of these differences, however, are not well understood. During puberty, a drastic neuroendocrine shift signals physical changes resulting in robust sex differences in human physiology. Here, we explore how shifting patterns of DNA methylation may inform these pathways of biological plasticity during the pubertal transition. In this study we analyzed DNA methylation (DNAm) in saliva at two time points across the pubertal transition within the same individuals. Our purpose was to compare two domains of DNAm patterns that may inform processes of sexual differentiation 1) sex related sites, which demonstrated differences between males from females and 2) time related sites in which DNAm shifted significantly between timepoints. We further explored the correlated network structure sex and time related DNAm networks and linked these patterns to pubertal stage, assays of salivary testosterone, a reliable diagnostic of free, unbound hormone that is available to act on target tissues, and overlap with androgen response elements. RESULTS: Sites that differed by biological sex were largely independent of sites that underwent change across puberty. Time-related DNAm sites, but not sex-related sites, formed correlated networks that were associated with pubertal stage. Both time and sex DNAm networks reflected salivary testosterone levels that were enriched for androgen response elements, with sex-related DNAm networks being informative of testosterone levels above and beyond biological sex later in the pubertal transition. CONCLUSIONS: These results inform our understanding of the distinction between sex- and time-related differences in DNAm during the critical period of puberty and highlight a novel linkage between correlated patterns of sex-related DNAm and levels of salivary testosterone.
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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 | 0.000 | 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) | 0.003 | 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".