Peroxisome proliferator-activated receptor (PPAR)α and -γ regulate IFNγ and IL-17A production by human T cells in a sex-specific way
Bibliographic record
Abstract
Women develop certain autoimmune diseases more often than men. It has been hypothesized that this may relate to the development of more robust T-helper (Th)1 responses in women. To test whether women exhibit a Th1 bias, we isolated naïve cluster of differentiation (CD)4(+) T cells from peripheral blood of healthy women and men and measured the proliferation and cytokine production by these cells in response to submaximal amounts of anti-CD3 and anti-CD28. We observed that CD4(+) T cells from women produced higher levels of IFNγ as well as tended to proliferate more than male CD4(+) T cells. Intriguingly, male CD4(+) T cells instead had a predilection toward IL-17A production. This sex dichotomy in Th cytokine production was found to be even more striking in the Swiss/Jackson Laboratory (SJL) mouse. Studies in mice and humans indicated that the sexual dimorphism in Th1 and Th17 cytokine production was dependent on the androgen status and the T-cell expression of peroxisome proliferator activated receptor (PPAR)α and PPARγ. Androgens increased PPARα and decreased PPARγ expression by human CD4(+) T cells. PPARα siRNA-mediated knockdown had the effect of increasing IFNγ by male CD4(+) T cells, while transfection of CD4(+) T cells with PPARγ siRNAs increased IL-17A production uniquely by female T cells. Together, our observations indicate that human T cells exhibit a sex difference in the production of IFNγ and IL-17A that may be driven by expressions of PPARα and PPARγ.
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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) | 0.003 | 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".