CD1a and bound lipids drive T‐cell responses in human skin disease
Bibliographic record
Abstract
In addition to serving as the main physical barrier with the outside world, human skin is abundantly infiltrated with resident αβ T cells that respond differently to self, infectious, microbiome, and noxious stimuli. To study skin T cells during infection and inflammation, experimental biologists track T-cell surface phenotypes and effector functions, which are often interpreted with the untested assumption that MHC proteins and peptide antigens drive measured responses. However, a broader perspective is that CD1 proteins also activate human T cells, and in skin, Langerhans cells (LCs) are abundant antigen presenting cells that express extremely high levels of CD1a. The emergence of new experimental tools, including CD1a tetramers carrying endogenous lipids, now show that CD1a-reactive T cells comprise a large population of resident T cells in human skin. Here, we review studies showing that skin-derived αβ T cells directly recognize CD1a proteins, and certain bound lipids, such as contact dermatitis allergens, trigger T-cell responses. Other natural skin lipids inhibit CD1a-mediated T-cell responses, providing an entry point for the development of therapeutic lipids that block T-cell responses. Increasing evidence points to a distinct role of CD1a in type 2 and 22 T-cell responses, providing new insights into psoriasis, contact dermatitis, and other T-cell-mediated skin diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".