Differential Regulation of Killer Cell Lectin-Like Receptor G1 Expression on T Cells
Bibliographic record
Abstract
The killer cell lectin-like receptor G1 (KLRG1) is the mouse homologue of the rat mast cell function-associated Ag and contains a tyrosine-based inhibitory motif in its cytoplasmic domain. It has been demonstrated that KLRG1 is induced on activated NK cells and that KLRG1 can inhibit NK cell effector functions. In this study, we show that in naive C57BL/6 mice KLRG1 is expressed on a subset of CD44(high)CD62L(low) T cells. KLRG1 expression can be detected on a small number of V(alpha)14i NK T cells but not on CD8alphaalpha(+) intraepithelial T cells that are either TCRgammadelta(+) or TCRalphabeta(+). We also show that KLRG1 expression is dramatically induced on approximately 50% of the CD8(+) T cells during both a viral and a parasitic infection. Interestingly, during Toxoplasma gondii infection, KLRG1 is up-regulated on CD4(+) T cells. Although KLRG1 expression can be induced on both NK cells and T cells, the molecular mechanism leading to the induction of KLRG1 differs in these two subsets of cells. Indeed, the up-regulation of KLRG1 on NK cells can be driven in vivo by cytokines, whereas KLRG1 cannot be induced on CD8(+) T cells by cytokines. In addition, although induction of KLRG1 on T cells appears to require TCR engagement in vivo, TCR engagement is not sufficient for KLRG1 induction in vitro. Taken together, these data suggest that the expression and induction of KLRG1 on T cells are tightly regulated. This could have important biological consequences on T cell activation and homeostasis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".