TCR Affinity Controls the Dynamics but Not the Functional Specification of the Antimycobacterial CD4+ T Cell Response
Bibliographic record
Abstract
Abstract The quality of T cell responses depends on the lymphocytes’ ability to undergo clonal expansion, acquire effector functions, and traffic to the site of infection. Although TCR signal strength is thought to dominantly shape the T cell response, by using TCR transgenic CD4+ T cells with different peptide:MHC binding affinity, we reveal that TCR affinity does not control Th1 effector function acquisition or the functional output of individual effectors following mycobacterial infection in mice. Rather, TCR affinity calibrates the rate of cell division to synchronize the distinct processes of T cell proliferation, differentiation, and trafficking. By timing cell division–dependent IL-12R expression, TCR affinity controls when T cells become receptive to Th1-imprinting IL-12 signals, determining the emergence and magnitude of the Th1 effector pool. These findings reveal a distinct yet cooperative role for IL-12 and TCR binding affinity in Th1 differentiation and suggest that the temporal activation of clones with different TCR affinity is a major strategy to coordinate immune surveillance against persistent pathogens.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".