Bibliographic record
Abstract
A key question yet to be resolved concerns the structure and function relationship of the TCR complex. How does antigen recognition by the TCR-alphabeta chains result in the activation of distinct signal transduction pathways by the CD3-gammadeltaepsilon/zeta complex? To investigate which part of the TCR-beta chain is involved in TCR signaling, we exchanged different domains of the constant regions of the TCR-beta chain with the corresponding TCR-gamma chain domains. We show here that hybridoma cells expressing a chimeric TCR-beta chain (betaIII) containing intracellular and transmembrane TCR-gamma amino acids, together with a wild-type TCR-alpha (alphawt) chain, were 10 times more sensitive to antigenic stimulation compared to cells expressing TCR-alphawt/betawt chains. This super-signaling phenotype of the betaIII chain was observed in two different TCRs. One specific for an alloantigen (I-A(bm12)) and one for an autoantigen (I-A(b)/MOG(35-55)). We found that this chimeric alphawt/betaIII TCR had normal association with CD3-gammadeltaepsilon and zeta chains. To investigate the effect of the chimeric betaIII chain in transgenic T cells, we made MOG(35-55)-specific TCR transgenic mice expressing either the alphawt/betawt or chimeric alphawt/betaIII TCR. Similar to what was observed in hybridoma cells, transgenic alphawt/betaIII T cells showed a super-signaling phenotype upon antigenic stimulation. Further studies may help us understand the effect of increased TCR signaling on autoimmunity and may lead to the identification of signaling molecules that can be targeted to stop the progression of autoimmune disorders such as multiple sclerosis.
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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) | 0.001 | 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".