Social environment is associated with gene regulatory variation in the rhesus macaque immune system
Bibliographic record
Abstract
Variation in the social environment is a fundamental component of many vertebrate societies. In humans and other primates, adverse social environments often translate into lasting physiological costs. The biological mechanisms associated with these effects are therefore of great interest, both for understanding the evolutionary impacts of social behavior and in the context of human health. However, large gaps remain in our understanding of the mechanisms that mediate these effects at the molecular level. Here we addressed these questions by leveraging the power of an experimental system that consisted of 10 social groups of female macaques, in which each individual's social status (i.e., dominance rank) could be experimentally controlled. Using this paradigm, we show that dominance rank results in a widespread, yet plastic, imprint on gene regulation, such that peripheral blood mononuclear cell gene expression data alone predict social status with 80% accuracy. We investigated the mechanistic basis of these effects using cell type-specific gene expression profiling and glucocorticoid resistance assays, which together contributed to rank effects on gene expression levels for 694 (70%) of the 987 rank-related genes. We also explored the possible contribution of DNA methylation levels to these effects, and identified global associations between dominance rank and methylation profiles that suggest epigenetic flexibility in response to status-related behavioral cues. Together, these results illuminate the importance of the molecular response to social conditions, particularly in the immune system, and demonstrate a key role for gene regulation in linking the social environment to individual physiology.
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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) | 0.001 | 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".