Molecular Basis for E-cadherin Recognition by Killer Cell Lectin-like Receptor G1 (KLRG1)
Bibliographic record
Abstract
The killer cell lectin-like receptor G1, KLRG1, is a cell surface receptor expressed on subsets of natural killer (NK) cells and T cells. KLRG1 was recently found to recognize E-cadherin and thus inhibit immune responses by regulating the effector function and the developmental processes of NK and T cells. E-cadherin is expressed on epithelial cells and exhibits Ca(2+)-dependent homophilic interactions that contribute to cell-cell junctions. However, the mechanism underlying the molecular recognition of KLRG1 by E-cadherin remains unclear. Here, we report structural, binding, and functional analyses of this interaction using multiple methods. Surface plasmon resonance demonstrated that KLRG1 binds the E-cadherin N-terminal domains 1 and 2 with low affinity (K(d) approximately 7-12 microm), typical of cell-cell recognition receptors. NMR binding studies showed that only a limited N-terminal region of E-cadherin, comprising the homodimer interface, exhibited spectrum perturbation upon KLRG1 complex formation. It was confirmed by binding studies using a series of E-cadherin mutants. Furthermore, killing assays using KLRG1(+)NK cells and reporter cell assays demonstrated the functional significance of the N-terminal region of E-cadherin. These results suggest that KLRG1 recognizes the N-terminal homodimeric interface of domain 1 of E-cadherin and binds only the monomeric form of E-cadherin to inhibit the immune response. This raises the possibility that KLRG1 detects monomeric E-cadherin at exposed cell surfaces to control the activation threshold of NK and 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) | 0.001 | 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".