TCRs with segment TRAV9‐2 or a CDR3 histidine are overrepresented among nickel‐specific CD4+ T cells
Bibliographic record
Abstract
Abstract Background Nickel is the most frequent cause of T cell–mediated allergic contact dermatitis worldwide. In vitro, CD4+ T cells from all donors respond to nickel but the involved αβ T cell receptor (TCR) repertoire has not been comprehensively analyzed. Methods We introduce CD154 (CD40L) upregulation as a fast, unbiased, and quantitative method to detect nickel‐specific CD4+ T cells ex vivo in blood of clinically characterized allergic and non allergic donors. Naïve (CCR7+ CD45RA+) and memory (not naïve) CD154+ CD4+ T cells were analyzed by flow cytometry after 5 hours of stimulation with 200 µmol/L NiSO4., TCR α‐ and β‐chains of sorted nickel‐specific and control cells were studied by high‐throughput sequencing. Results Stimulation of PBMCs with NiSO4induced CD154 expression on ~0.1% (mean) of naïve and memory CD4+ T cells. In allergic donors with recent positive patch test, memory frequencies further increased ~13‐fold and were associated with markers of in vivo activation. CD154 expression was TCR‐mediated since single clones could be specifically restimulated. Among nickel‐specific CD4+ T cells of allergic and non allergic donors, TCRs expressing the α‐chain segment TRAV9‐2 or a histidine in their α‐ or β‐chain complementarity determining region 3 (CDR3) were highly overrepresented. Conclusions Induced CD154 expression represents a reliable method to study nickel‐specific CD4+ T cells. TCRs with particular features respond in all donors, while strongly increased blood frequencies indicate nickel allergy for some donors. Our approach may be extended to other contact allergens for the further development of diagnostic and predictive in vitro tests.
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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) | 0.004 | 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".