Chronic infection control relies on T cells with lower foreign antigen binding strength generated by N-nucleotide diversity
Bibliographic record
Abstract
Summary The pathogens to which T cells respond is determined by the T cell receptors (TCRs) present in an individual’s repertoire. Although more than 90% of the TCR repertoire is generated by terminal deoxynucleotidyl transferase (TdT)-mediated N-nucleotide addition during V(D)J recombination, the benefit of TdT-modified TCRs remains unclear. Here, we computationally and experimentally investigated whether TdT systematically modifies the affinity distribution of a TCR repertoire in ways that impacts acute or chronic infection. Our computational model predicts a shift toward low-affinity T cells over time during chronic, but not acute, infections. Elimination of low-affinity T cells in silico substantially delayed chronic infection clearance. Corroborating an affinity-centric benefit for TCR diversity, we showed that infection of TdT-deficient mice delayed the clearance of a chronic viral pathogen, while acute viral control was unaffected. Our data thus suggest that TdT-mediated TCR diversity is of particular benefit in the control of prolonged pathogen replication.
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 | 0.000 |
| 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".