Regulation of CD4+ T cell responses: The impact of limiting CD28 costimulation (P1083)
Bibliographic record
Abstract
Abstract Controlling T cell activation is important to prevent autoimmunity. Costimulatory molecules CD28 and CTLA-4 bind the same ligands (CD80 and CD86) but cause opposite outcomes: CD28 signalling enhances TCR signals whereas CTLA-4 inhibits T cell activation. The mechanism of CTLA-4 action has been unclear however we have identified a novel mechanism whereby CTLA-4 removes its ligands from antigen presenting cells by trans-endocytosis. This reduces the availability of costimulatory ligands for CD28 engagement and thereby regulates T cell activation. This model predicts that there is a threshold number of costimulatory ligands required for T cell stimulation. We are using a dose-dependent inducible expression system to determine this threshold and compare the two ligands. We have also begun to examine the functional consequences of stimulating CD4+ T cells while restricting costimulation and observed that CD28 costimulation is not always required for T cell activation and proliferation, but without CD28 engagement T cells do not fully differentiate or upregulate a number of functional proteins. Interestingly in our studies, human T cells activated in the absence of CD28 signalling are not classically anergic. Establishing the characteristics of T cells activated without adequate costimulation may be a useful surrogate for CTLA-4 function and improve our understanding of immune responses in the context of autoimmunity, tumour biology or transplant rejection.
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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) | 0.002 | 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".