The Low Affinity Fc Receptor for IgG Functions as an Effective Cytolytic Receptor for Self-Specific CD8 T Cells
Bibliographic record
Abstract
We have recently described a population of self-Ag-specific murine CD8(+) T cells with a memory phenotype that use receptors of both the adaptive and innate immune systems in the detection of transformed and infected cells. In this study we show that upon activation with IL-2 with or without Ag, between 10 and 20% of the activated self-specific CD8(+) T cells express the low affinity FcR for IgG. By contrast, all IL-2-activated NK cells express high levels of this FcR. The FcR comprises the FcgammaRIIIalpha and FcRgamma subunits. However, the FcRgamma subunit also associates with the CD3 complex, and this association probably contributes to the low expression of FcR in activated cells. Although the FcR is expressed at a low level on activated self-specific CD8(+) T cells, it functions very efficiently as a cytolytic receptor in ADCC. FcR-dependent killing occurred in the absence of TCR stimulation, but could be augmented by concurrent stimulation of the TCR. In addition to mediating ADCC, engagement of the FcR on self-specific CD8(+) T cells results in the production of both IFN-gamma and TNF-alpha. This is the first report of an activating FcR on self-specific murine CD8(+)alphabeta TCR(+) T cells and establishes the importance of innate immune system receptors in the function of these self-specific CD8(+) T cells.
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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 | 0.001 |
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".