IFN-α Inhibits Telomerase in Human CD8+ T Cells by Both hTERT Downregulation and Induction of p38 MAPK Signaling
Bibliographic record
Abstract
The cytokine IFN-α is secreted during viral infections and has been shown to inhibit telomerase activity and accelerate T cell differentiation in vivo. However, the mechanism for this inhibition is not clear. In this study, we show that IFN-α inhibits both the transcription and translation of human telomerase reverse transcriptase (hTERT), the catalytic component of telomerase, in activated CD8(+) T cells. This was associated with increased activity of the repressor of hTERT transcription E2 transcription factor and decreased activation of NF-κB that promotes hTERT transcription. However IFN-α did not affect the translocation of hTERT from the cytoplasm to the nucleus. IFN-α also inhibits AKT kinase activation but increases p38 MAPK activity, and both of these events have been shown previously to inhibit telomerase activity. Addition of BIRB796, an inhibitor of p38 activity, to IFN-α-treated cells reversed, in part, the inhibition of telomerase by this cytokine. Therefore, IFN-α can inhibit the enzyme telomerase in CD8(+) T cells by transcriptional and posttranslational mechanisms. Furthermore, the addition of IFN-α to CD8(+)CD27(+)CD28(+) T cells accelerates the loss of both these costimulatory molecules. This suggests that persistent viral infections may contribute to the accumulation of highly differentiated/senescent CD8(+)CD27(-)CD28(-) T cells during aging by promoting IFN-α secretion during repeated episodes of viral reactivation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".