Deep TCR repertoire sequencing reveals relative change in peanut specific clonotype in subjects undergoing rush oral immunotherapy
Bibliographic record
Abstract
Oral immunotherapy is an emerging therapy currently under investigation for the treatment of food allergy [ 1 ]. Underlying mechanisms are thought to involve a switch in the food specific T cell response from Th2 to either Th1, Tr1 and/or Treg. It is unknown whether this change in response results from re-education of existing pathological food-specific T cells or from their replacement by new healthy T cells (change of guard hypothesis). The objective was to evaluate the clonal distribution of peanut specific T cell in subjects with peanut allergy and follow changes in clonotype with treatment using a high-throughput T cell receptor (TCR) sequencing platform. Peripheral blood mononuclear cells (PBMCs) from three subjects undergoing rush oral immunotherapy in a previous trial [ 2 ] and three control subjects on avoidance diet were cultured with peanut extract at baseline and at 9 and 18 months. Carboxyfluorescein succinimidyl ester (CFSE)-low peanut proliferating T cells were then isolated by fluorescence-activated cell sorting (FACS) and TCR analysis was performed. The CFSE-low proliferating fraction was found to be comprised of between 2000 and 12,000 different T cell clones. However, only between 15 and 25% of proliferating T cells (from 100-400 different clones) were consistently found at all three time points and probably represented true peanut-specific T cells. While the relative frequency of these peanut-specific clones was stable over time in subjects on avoidance diet (R=0.633 to 0.760), it was found to change in subjects undergoing oral immunotherapy (R= 0.123 to 0.350), following two characteristic patterns. Using a deep TCR sequencing platform, we found that only a fraction of CFSE-low peanut proliferating T cells were consistent in time and likely to represent true peanut specific T cells. Oral immunotherapy was associated with changes in relative frequency of clones within this fraction, which would support the change of guard hypothesis.
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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) | 0.001 | 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".