Cytochrome P450 2D6 as a Model Antigen
Bibliographic record
Abstract
Cytochrome P450 2D6 (CYP2D6) has been identified as the major autoantigen in type 2 autoimmune hepatitis (AIH). However, because of a lack of appropriate animal models, the etiology of AIH is still poorly understood. We generated a mouse model for AIH using the human CYP2D6 as a triggering molecule for autoimmunity. We infected wild-type FVB mice with an adenovirus expressing human CYP2D6 (Ad-2D6) to break self-tolerance to the mouse CYP2D6 homologues. Ad-2D6-infected mice showed persistent features of liver damage including hepatic fibrosis, cellular infiltrations, focal-to-confluent necrosis and generation of anti-CYP2D6 antibodies, which predominantly recognized the identical immunodominant epitope recognized by LKM-1 antibodies from AIH patients. Interestingly, Ad-2D6 infection of transgenic mice expressing the human CYP2D6 (CYP2D6 mice) resulted in delayed kinetics and reduced severity of liver damage. However, the quantity and quality of anti-CYP2D6 antibodies was only moderately reduced in CYP2D6 mice. In contrast, the frequency of CYP2D6-specific CD4 and CD8 T cells was dramatically decreased in CYP2D6 mice, indicating the presence of a strong T cell tolerance to human CYP2D6 established in CYP2D6 mice, but not in wild-type mice. CYP2D6-specific T cells reacted to human CYP2D6 peptides with intermediate homology to the mouse homologues, but not to those with high homology, indicating that molecular mimicry rather than molecular identity breaks tolerance and subsequently causes severe persistent autoimmune liver damage. The CYP2D6 model provides a platform to investigate mechanisms involved in the immunopathogenesis of autoimmune-mediated chronic hepatic injury and evaluate possible ways of therapeutic interference.
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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 | 0.001 | 0.001 |
| 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".