<i>Cis</i> association of leukocyte Ig‐like receptor 1 with MHC class I modulates accessibility to antibodies and HCMV UL18
Bibliographic record
Abstract
Leukocyte Ig-like receptor (LIR) 1 (CD85j/ILT2/LILRB1) is an inhibitory receptor with broad specificity for MHC class I (MHC-I) and the human CMV MHC-I homologue UL18. LIR-1 can inhibit NK cells through the conventional interaction with MHC-I expressed on a target cell (in trans) but the nature and the effects of LIR-1 interactions with MHC-I in cis are not well understood. Here we show that MHC-I expressed in cis has an impact on the detection of LIR-1 with various antibodies. We found the cis interaction alters recognition by only one of two antibodies known to block functional trans recognition by LIR-1 on NK cells. Specifically, we observed an enhancement of recognition with GHI/75 in the presence of various MHC-I alleles on 721.221 cells. We found that blocking the LIR-1 contact site with anti-MHC-I antibodies decreased detection of LIR-1 with GHI/75. We also observed a decrease in GHI/75 following acid denaturation of MHC-I. Finally, disruption of LIR-1 cis interactions with MHC-I significantly enhanced UL18-Fc binding to NK92 cells and enhanced the relative inhibition of NK92 cells by HLA-G. These results have implications for LIR-1 function in scenarios such as infection when MHC-I levels on effector cells may be increased by IFNs.
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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) | 0.002 | 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".