Activation-induced split anergy: a mechanism of persistence of autoimmune T cells (128.28)
Bibliographic record
Abstract
Abstract Anergy and apoptosis are major pathways of T cell tolerance. Impairments in these mechanisms are believed to contribute to the development of autoimmune diseases such as SLE and arthritis. Here we analyzed features of T cell anergy, activation and activation-induced cell death in lupus-prone and MHC-matched healthy mice. Unexpectedly, lupus mice exhibit reduced calcium flux, IL-2 production and proliferation in spleen cells upon anti-CD3 stimulation. Such T cell hyporesponsiveness is abrogated upon ionomycin stimulation, suggesting a specific impairment in TCR signaling pathway. This suggests that autoimmune T cells exhibit features of anergy, rather than resistance to it. In contrast, autoimmune T cells produce higher levels of pro-inflammatory cytokine interferon-gamma, and exhibit features of activation, i.e., increased activation markers. Consistently, quantitative gene expression analyses show lower Il2, but higher Ifn-g, mRNA in lupus T cells than in controls. Further, lupus T cells exhibit increased constitutive phosphorylation of ZAP-70 and other T cell signaling molecules, which do not increase upon anti-CD3 stimulation. Finally, lupus T cells resist apoptosis upon specific TCR signaling. Thus, autoimmune T cells display features of activation as well as anergy. Such split anergic T cells display resistance to activation-induced cell death. These data lead us to posit that persistent in vivo T cell activation results in subsequent induction of anergy that manifests as impaired responsiveness to TCR mediated signaling and prevents T cells from undergoing activation-induced cell death. Thus, induction of anergy may actually prevent autoreactive T cells from being eliminated. Ongoing experiments will delineate mechanisms underlying this novel mechanism of autoimmune T cell persistence.
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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".