Continuous Antigenic Stimulation of DO11.10 TCR Transgenic Mice in the Presence or Absence of IL-1β: Possible Implications for Mechanisms of T Cell Depletion in HIV Disease
Bibliographic record
Abstract
Untreated HIV disease is associated with chronic immune activation and CD4(+) T cell depletion. A variety of mechanisms have been invoked to account for CD4(+) T cell depletion in this setting, but the quantitative contributions of these proposed mechanisms over time remain unclear. We turned to the DO11.10 TCR transgenic mouse model, where OVA is recognized in the context of H-2(d), to explore the impact of chronic antigenic stimulation on CD4(+) T cell dynamics. To model dichotomous states of persistent Ag exposure in the presence or absence of proinflammatory stimulation, we administered OVA peptide to these mice on a continuous basis with or without the prototypic proinflammatory cytokine, IL-1β. In both cases, circulating Ag-specific CD4(+) T cells were depleted. However, in the absence of IL-1β, there was limited proliferation and effector/memory conversion of Ag-specific T cells, depletion of peripheral CD4(+) T cells in hematolymphoid organs, and systemic induction of regulatory Foxp3(+)CD4(+) T cells, as often observed in late-stage HIV disease. By contrast, when OVA peptide was administered in the presence of IL-1β, effector/memory phenotype T cells expanded and the typical symptoms of heightened immune activation were observed. Acknowledging the imperfect and incomplete relationship between Ag-stimulated DO11.10 TCR transgenic mice and HIV-infected humans, our data suggest that CD4(+) T cell depletion in the setting of HIV disease may reflect, at least in part, chronic Ag exposure in the absence of proinflammatory signals and/or appropriate APC functions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".