EAT-2, a SAP-like adaptor, controls NK cell activation through phospholipase Cγ, Ca++, and Erk, leading to granule polarization
Bibliographic record
Abstract
Ewing's sarcoma-associated transcript 2 (EAT-2) is an Src homology 2 domain-containing intracellular adaptor related to signaling lymphocytic activation molecule (SLAM)-associated protein (SAP), the X-linked lymphoproliferative gene product. Both EAT-2 and SAP are expressed in natural killer (NK) cells, and their combined expression is essential for NK cells to kill abnormal hematopoietic cells. SAP mediates this function by coupling SLAM family receptors to the protein tyrosine kinase Fyn and the exchange factor Vav, thereby promoting conjugate formation between NK cells and target cells. We used a variety of genetic, biochemical, and imaging approaches to define the molecular and cellular mechanisms by which EAT-2 controls NK cell activation. We found that EAT-2 mediates its effects in NK cells by linking SLAM family receptors to phospholipase Cγ, calcium fluxes, and Erk kinase. These signals are triggered by one or two tyrosines located in the carboxyl-terminal tail of EAT-2 but not found in SAP. Unlike SAP, EAT-2 does not enhance conjugate formation. Rather, it accelerates polarization and exocytosis of cytotoxic granules toward hematopoietic target cells. Hence, EAT-2 promotes NK cell activation by molecular and cellular mechanisms distinct from those of SAP. These findings explain the cooperative and essential function of these two adaptors in NK cell activation.
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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".