Tracking global changes induced in the CD4 T cell receptor repertoire by immunisation with a complex antigen using local sequence features of CDR3 protein sequence
Bibliographic record
Abstract
Abstract The clonal theory of adaptive immunity proposes that immunological responses are encoded by increases in the frequency of lymphocytes carrying antigen-specific receptors. In this study, we measure the frequency of different TcRs in CD4+ T cell populations of mice immunized with a complex antigen, killed Mycobacterium tuberculosis, using high throughput parallel sequencing of the TcR beta chain. In order to track the changes induced by immunisation within this very heterogeneous repertoire, the sequence data were classified by counting the frequency of different clusters of short (3 or 4) continuous stretches of amino acids within the CDR3 repertoire of different mice. Both unsupervised (hierarchical clustering) and supervised (support vector machine) analysis of these different distributions of sequence clusters differentiated between immunised and unimmunised mice with 100% efficiency. The CD4+ T cell receptor repertoires of mice 5 and 14 days post immunisation were clearly different from that of unimmunised mice, but were not distinguishable from each other. However, the repertoires of mice 60 days post immunisation were distinct both from unimmunised mice, and the day 5/14 animals. Our results reinforce the remarkable diversity of the T cell receptor repertoire, resulting in many diverse private TcRs contributing to the T cell response even in genetically identical mice responding to the same antigen. Finally, specific motifs defined by short sequences of amino acids within the CDR3 region may have a major effect on TcR specificity. The results of this study provide new insights into the properties of the CD4+ adaptive T cell response.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".