Social Status is Associated with Impaired Anti-Inflammatory Response in Free-Ranging Male Rhesus Macaques
Bibliographic record
Abstract
Abstract Exposure to social adversity can influence the onset and progression of disease. One possible mechanism through which social adversity may impact health outcomes is by altering immune homeostasis. Recently, evidence has connected social adversity to changes in gene regulation in the immune response of both humans and nonhuman primates. We investigated how social adversity (quantified as low social status) affected the Th2 immune response in free-ranging male rhesus macaques (n=128). We stimulated peripheral blood mononuclear cells (PBMCs) in vitro with Dexamethasone (Dex), an anti-inflammatory synthetic glucocorticoid (GC), and measured how social status influenced the gene regulatory response to Dex stimulation using mRNA-seq. We detected 2,923 differentially expressed genes (FDR<0.1) in response to Dex in PBMCs. After Dex stimulation, glucocorticoid responsive genes, such as FKBP5 and TSCD22, were induced and inflammatory genes, such as CSF1 and TNF, were repressed. Low social status animals had significantly higher mean expression of inflammatory genes (p=1.2×10−20) compared to high social status animals. Many of these genes, such as CD38 and NKAP, are known to be repressed by Dex. After Dex stimulation, low social status animals also had a higher mean expression (p=1.9×10−4) of genes related to the GC resistance-associated c-Myc/MAPK pathway. For instance, low social status animals had significantly higher expression (FDR<0.2) of signature genes in this pathway including AKT3, MAPK1 and PIK3CD. Our study suggests that the anti-inflammatory immune response in rhesus macaques can be negatively affected by low social status, with animals having a blunted response to Dex and presenting signatures of GC resistance. R01-AG060931 R00-AG051764 P40-OD012217
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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 | 0.001 | 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) | 0.002 | 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".