Type 1 IFN Maintains the Survival of Anergic CD4+ T Cells
Bibliographic record
Abstract
Anergic T cells have immunoregulatory activity and can survive for extended periods in vivo. It is unclear how anergic T cells escape from deletion, because both anergy and apoptosis can occur after TCR ligation. Stimulation of human CD4+ T cell clones reactive to influenza hemagglutinin peptides can occur in the absence of APCs when MHC class II-expressing, activated T cells present peptide to each other. This T:T peptide presentation can induce CD95-mediated apoptosis, while the cells that do not die are anergic. We found that the death after peptide or anti-CD3 treatment of a panel of CD4+ T cell clones is blocked by IFN-beta secreted by fibroblasts and also by IFN-alpha. This increases cell recovery after stimulation, which is not due to T cell proliferation. This mechanism for apoptosis inhibition rapidly stops protein kinase C-delta translocation from the cytoplasm to the nucleus, which is an early event in the death process. A central observation was that CD4+ T cells that are rescued from apoptosis after T:T presentation of peptide by IFN-alphabeta remain profoundly anergic to rechallenge with Ag-pulsed APCs. However, anergized cells retain the ability to respond to IL-2, showing that they are nonresponsive but functional. The prevention of peptide-induced apoptosis in activated T cells by IFN-alphabeta is a novel mechanism that may enable the survival and maintenance of anergic T cell populations after TCR engagement. This has important implications for the persistence of anergic T cells with the potential for immunoregulatory function in vivo.
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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".