Cis interactions between CD2 and its ligands on T cells are required for T cell activation
Bibliographic record
Abstract
CD2 is largely described to promote T cell activation when engaged by its ligands, CD48 in mice and CD58 in humans, that are present on antigen-presenting cells (APCs). However, both CD48 and CD58 are also expressed on T cells. By generating new knockout mouse strains lacking CD2 or CD48 in the C57BL/6 background, we determined that whereas CD2 was necessary on T cells for T cell activation, its ligand CD48 was not required on APCs. Rather, CD48 was also needed on T cells. One exception was during cytotoxicity, which required CD48 on T cells and APCs. Fluorescence resonance energy transfer (FRET) studies in nonimmune cells provided evidence that cis interactions between CD2 and CD48 existed within individual cells. CD2-CD48 interactions on T cells enabled more robust T cell receptor (TCR) signals, including protein tyrosine phosphorylation. Using T cells from a CD2 knock-in mouse in which a tag was inserted at the carboxyl terminus of CD2, mass spectrometry analyses revealed that the role of CD2 in T cell activation correlated with its ability to interact with components of the TCR complex and the protein tyrosine kinase Lck. CD2-CD58 provided a similar function in human T cells. Thus, our data imply that T cell-intrinsic cis interactions of CD2 with its ligands are required for TCR signaling and T cell activation. Interactions with ligands on APCs contribute during cytotoxicity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".