Modulation of the inhibitory receptor leukocyte Ig-like receptor 1 on human natural killer cells
Bibliographic record
Abstract
Leukocyte Ig-like receptor 1 (LIR-1) is an inhibitory Ig superfamily receptor with broad specificity for MHC-I expressed on leukocytes including natural killer (NK) and T cells. The extent of LIR-1 expression on NK cells is quite disparate between donors but the regulation of LIR-1 in NK cells is poorly understood. We examined expression profiles of LIR-1 on NK and T lymphocytes in 11 healthy donors over 1 year. Four of the 11 donors demonstrated substantial increases in LIR-1⁺ NK cells. High levels of LIR-1 expression were not correlated with exposure to human cytomegalovirus or the fraction of CD57⁺ NK cells in the donor. LIR-1 levels on ex vivo NK and CD56⁺ T cells were increased in vitro by short term exposure to IL-2 or IL-15 compared to control but not with various other cytokines tested. Sorted CD56(bright) NK cells also increased LIR-1 expression when cultured in IL-2. Maintenance of LIR-1 on longer term NK cells was also dependent on continuous stimulation by IL-15 or IL-2. While we could not detect increases in total LIR-1 mRNA in response to cytokine treatment by qPCR, we observed a shift in activity of LIR-1 promoter reporter constructs in the presence of IL-2 favoring the more translationally active transcript from the proximal promoter. Together these results show LIR-1 on NK cells is under the control of cytokines known to drive NK cell maturation and activation and suggest availability of such cytokines may alter the NK repertoire in vivo as we observed in several donors with fluctuating levels of LIR-1 on their NK 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".