A Subset of Human Autoreactive CD1c-Restricted T Cells Preferentially Expresses TRBV4-1+ TCRs
Bibliographic record
Abstract
Abstract In humans, a substantial portion of T cells recognize lipids presented by the monomorphic CD1 proteins. Recent studies have revealed the molecular basis of mycobacterial lipid recognition by CD1c-restricted T cells. Subsets of CD1c-restricted T cells recognize self-lipids in addition to foreign lipids, which may have implications in human diseases involving autoimmunity and malignancy. However, the molecular identity of these self-reactive T cells remains largely elusive. In this study, using a novel CD1c+ artificial APC (aAPC)-based system, we isolated human CD1c-restricted autoreactive T cells and characterized them at the molecular level. By using the human cell line K562, which is deficient in MHC class I/II and CD1 expression, we generated an aAPC expressing CD1c as the sole Ag-presenting molecule. When stimulated with this CD1c+ aAPC presenting endogenous lipids, a subpopulation of primary CD4+ T cells from multiple donors was consistently activated, as measured by CD154 upregulation and cytokine production in a CD1c-specific manner. These activated CD4+ T cells preferentially expressed TRBV4-1+ TCRs. Clonotypic analyses of the reconstituted TRBV4-1+ TCR genes confirmed CD1c-restricted autoreactivity of this repertoire, and the strength of CD1c reactivity was influenced by the diversity of CDR3β sequences. Finally, alanine scanning of CDR1 and CDR2 sequences of TRBV4-1 revealed two unique residues, Arg30 and Tyr51, as critical in conferring CD1c-restricted autoreactivity, thus elucidating the molecular basis of the observed V gene bias. These data provide new insights into the molecular identity of human autoreactive CD1c-restricted T cells.
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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) | 0.002 | 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".