Interleukin‐7 promotes the survival of human CD4<sup>+</sup> effector/memory T cells by up‐regulating Bcl‐2 proteins and activating the JAK/STAT signalling pathway
Bibliographic record
Abstract
SUMMARY: Interleukin-7 (IL-7) is a crucial cytokine involved in T-cell survival and development but its signalling in human T cells, particularly in effector/memory T cells, is poorly documented. In this study, we found that IL-7 protects human CD4(+) effector/memory T cells from apoptosis induced upon the absence of stimulation and cytokines. We show that IL-7 up-regulates not only Bcl-2 but also Bcl-xL and Mcl-1 as well. Interleukin-7-induced activation of the janus kinase/signal transducer and activator of transcription (JAK/STAT) signalling pathway is sufficient for cell survival and up-regulation of Bcl-2 proteins. In contrast to previous studies with naive T cells, we found that IL-7 is a weak activator of the phosphatidylinositol 3 kinase (PI3K)/AKT (also referred as protein kinase B) pathway and IL-7-mediated cell survival occurs independently from the PI3K/AKT pathway as well as from activation of the mitogen-activated protein kinase/extracellular signal-regulated kinase pathway. Considering the contribution of both IL-7 and CD4(+) effector/memory T cells to the pathogenesis of autoimmune diseases such as rheumatoid arthritis and colitis, our study suggests that IL-7 can contribute to these diseases by promoting cell survival. A further understanding of the mechanisms of IL-7 signalling in effector/memory T cells associated with autoimmune inflammatory diseases may lead to potential new therapeutic avenues.
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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".