IL-2 receptor chain cytokines differentially regulate human CD8+CD127+ and CD8+CD127- T cell division and susceptibility to apoptosis
Bibliographic record
Abstract
Expression of IL-7 receptor alpha (CD127) is associated with naive and memory (i.e. non-effector) CD8+ T cell phenotypes. Effector CD8+ T cells are predominantly CD127- and most die by apoptosis. Therefore, CD127 appears to be a marker for CD8+ T cell differentiation, yet its role in CD8+ T cell survival and memory development is unclear. To address this, we investigated the cell death and cell division of isolated CD8+CD127+ and CD8+CD127- T cells in response to common IL-2 receptor gamma chain (gamma(C)) cytokines other than IL-7. We show here that (i) memory cells (CD127+CD45RA-) divide frequently in response to either IL-2, -4 or -15; (ii) IL-2 and -15 enhance cell division in effector-memory-like cells (CD127-CD45RA+) while IL-4 enhances the cell division of effector cells (CD127-CD45RA-); (iii) CD8+CD127+ T cells are more sensitive to the anti-apoptotic effects of IL-2 or IL-15 than CD8+CD127- T cells and (iv) CD8+CD127+ T cell produce more Bcl-2 in response to IL-2 or IL-15 compared with CD8+CD127- T cells. Therefore, CD8+CD127+ and CD8+CD127- T cells differ in their responsiveness to cell division and anti-apoptotic signals from IL-2, -4 and -15. This suggests a role for gamma(C) cytokines in the pathogenesis of diseases in which CD127 expression is altered on CD8+ T cells such as in progressive viral infections and cancer.
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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) | 0.002 | 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".