A Subset of Human Autoreactive CD1c-Restricted T Cells Preferentially Express TRBV4-1+ TCRs and Recognize Phospholipids
Bibliographic record
Abstract
Abstract Unlike the highly polymorphic, peptide-presenting conventional MHC molecules, CD1 consists of a family of monomorphic, lipid-presenting proteins. Recent studies have revealed the molecular basis of mycobacterial lipid recognition by CD1a-c-restricted T cells. In addition to foreign lipids, subsets of CD1a-c-restricted T cells recognize self-lipids, which may have implications for human diseases such as autoimmunity and cancer. And yet, the molecular identity of these self-reactive T cells remains largely elusive. In this study, using a novel CD1c+ artificial antigen-presenting cell (aAPC)-based system, we have isolated human CD1c-restricted autoreactive T cells and characterized them at the molecular level. By employing the human cell line K562, deficient in MHC class I/II and CD1 expression, as a backbone, we generated an aAPC expressing CD1c as the sole antigen-presenting molecule with costimulatory molecules, CD80 and CD83. When stimulated with this CD1c+ aAPC endogenously presenting self-lipids, a subpopulation of primary human CD4+ T cells from multiple donors consistently upregulated CD154 (CD40L) in a CD1c-specific manner. These activated CD4+ T cells preferentially expressed TRBV4-1+ TCRs. Interestingly, TRAV usage and CDR3 sequences of these TRBV4-1+ T cells were diverse. Clonotypic analyses of the reconstituted TRBV4-1+ TCRs demonstrated that the heterogeneity of the CDR3 sequences greatly impacted the strength of CD1c-restricted autoreactivity. Furthermore, cell-free assays using recombinant CD1c loaded with distinct lipids identified several phospholipid species as potential self-ligands. These data provide new insights into the molecular identity of human autoreactive CD1c-restricted T cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".