FcRγ Presence in TCR Complex of Double-Negative T Cells Is Critical for Their Regulatory Function
Bibliographic record
Abstract
TCRalphabeta+CD4-CD8- double-negative (DN) T regulatory (Treg) cells have recently been shown to suppress Ag-specific immune responses mediated by CD8+ and CD4+ T cells in humans and mice. Our previous study using cDNA microarray analysis of global gene expression showed that FcRgamma was the most highly overexpressed gene in functional DN Treg cell clones compared with nonfunctional mutant clones. In this study, we demonstrate that FcRgamma-deficient DN T cells display markedly reduced suppressive activity in vitro. In addition, unlike FcRgamma-sufficient DN T cells, FcRgamma-deficient DN T cells were unable to prolong donor-specific allograft survival when adoptively transferred to recipient mice. Protein analyses indicate that in addition to FcRgamma, DN Treg cell clones also express higher levels of TCRbeta, while mutant clones expressed higher levels of Zap70 and Lck. Within DN Treg cells, we found that FcRgamma associates with the TCR complex and that both FcRgamma and Syk are phosphorylated in response to TCR cross-linking. Inhibition of Syk signaling and FcRgamma expression were both found to reduce the suppressive function of DN Treg cells in vitro. These results indicate that FcRgamma deficiency significantly impairs the ability of DN Treg cells to down-regulate allogeneic immune responses both in vitro and in vivo, and that FcRgamma plays a role in mediating TCR signaling in DN Treg 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 | 0.001 |
| 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".