Carcinoembryonic Antigen (CEA) Inhibits NK Killing via Interaction with CEA-Related Cell Adhesion Molecule 1
Bibliographic record
Abstract
The NK killing activity is regulated by activating and inhibitory NK receptors. All of the activating ligands identified so far are either viral or stress-induced proteins. The class I MHC proteins are the ligands for most of the inhibitory NK receptors. However, in the past few years, several receptors have been identified that are able to inhibit NK killing independently of class I MHC recognition. We have previously demonstrated the existence of a novel inhibitory mechanism of NK cell cytotoxicity mediated by the homophilic carcinoembryonic Ag (CEA)-related cell adhesion molecule 1 (CEACAM1) interactions. In this study, we demonstrate that CEACAM1 also interacts heterophilically with the CEA protein. Importantly, we show that these heterophilic interactions of CEA and CEACAM1 inhibit the killing by NK cells. Because CEA is expressed on a wide range of carcinomas and commonly used as tumor marker, these results represent a novel role for the CEA protein enabling the escape of tumor cells from NK-mediated killing. We further characterize, for the first time, the CEACAM1-CEA interactions. Using functional and binding assays, we demonstrate that the N domains of CEACAM1 and CEA are crucial but not sufficient for both the CEACAM1-CEACAM1 homophilic and CEACAM1-CEA heterophilic interactions. Finally, we suggest that the involvement of additional domains beside the N domain in the heterophilic and homophilic interactions is important for regulating the balance between cis and trans interactions.
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".