Self-Antigen Maintains the Innate Antibacterial Function of Self-Specific CD8 T Cells In Vivo
Bibliographic record
Abstract
Self-specific CD8 T cells, which are selected by high-affinity interactions with self-Ags, develop into a lineage distinct from conventional CD8 T cells. We have previously shown that these self-specific cells acquire phenotypic and functional similarities to cells of the innate immune system including the expression of functional receptors associated with NK cells. In this study, we show that these self-specific cells have the ability to produce large amounts of IFN-gamma in response to infection with Listeria monocytogenes in a bystander fashion. The rapid production of IFN-gamma is associated with a dramatic reduction in the number of viable bacteria at the peak of infection. Self-specific CD8 T cells provide only marginal innate protection in the absence of self-Ag; however, the presence of self-Ag dramatically increases their protective ability. Exposure to self-Ag is necessary for the maintenance of the memory phenotype and responsiveness to inflammatory cytokines such as IL-15. Significantly, self-specific CD8 T cells are also more efficient in the production of IFN-gamma and TNF-alpha, thus providing more cytokine-dependent protection against bacterial infection when compared with NK cells. These findings illustrate that self-reactive CD8 T cells can play an important innate function in the early defense against bacterial infection.
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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) | 0.001 | 0.001 |
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".