Cytokine signals through PI-3 kinase pathway modulate Th17 cytokine production by CCR6+ human memory T cells
Bibliographic record
Abstract
Human memory T cells (T(M) cells) that produce IL-17 or IL-22 are currently defined as Th17 or Th22 cells, respectively. These T cell lineages are almost exclusively CCR6(+) and are important mediators of chronic inflammation and autoimmunity. However, little is known about the mechanisms controlling IL-17/IL-22 expression in memory Th17/Th22 subsets. We show that common γ chain (γc)-using cytokines, namely IL-2, IL-7, and IL-15, potently induce Th17-signature cytokine expression (Il17a, Il17f, Il22, and Il26) in CCR6(+), but not CCR6(-), T(M) cells, even in CCR6(+) cells lacking IL-17 expression ex vivo. Inhibition of phosphoinositide 3-kinase (PI-3K) or Akt signaling selectively prevents Th17 cytokine induction by γc-cytokines, as does ectopic expression of the transcription factors FOXO1 or KLF2, which are repressed by PI-3K signaling. These results indicate that Th17 cytokines are tuned by PI-3K signaling in CCR6(+) T(M) cells, which may contribute to chronic or autoimmune inflammation. Furthermore, these findings suggest that ex vivo analysis of IL-17 expression may greatly underestimate the frequency and pathogenic potential of the human Th17 compartment.
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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".