CD8+ T Cells Are Necessary for Recognition of Allelic, But Not Locus-Mismatched or Xeno-, HLA Class I Transplantation Antigens
Bibliographic record
Abstract
Although HLA transgenic mice (HLA TgM) could provide a powerful approach to investigate human MHC-specific T cell responsiveness, the extent to which these molecules are recognized by the mouse immune system remains unclear. We established TgM expressing HLA class I alleles A2, B7, or B27 in their fully native form (HLAnat) or as hybrid molecules (HLAhyb) of the HLA alpha1/alpha2 domains linked to the H-2Kb alpha3, transmembrane, and cytoplasmic domains (i.e., to maintain possible species-specific interactions). Comparison of each as xeno- (i.e., by non-TgM) vs allo- (i.e., by TgM carrying an alternate HLA allele) transplantation Ags revealed the following: 1) Although HLAhyb molecules induced stronger xeno-CD8+ T cell responses in vitro, additional effector mechanisms must be active in vivo because HLAnat skin grafts were rejected faster by non-TgM; 2) gene knockout recipients showed that xenorejection of HLAnat and, unexpectedly, HLAhyb grafts doesn't depend on CD8+ or CD4+ T cells or B cells; 3) each HLAhyb strain developed tolerance to "self" but rejected allele- (-B27 vs -B7) and locus- (-B vs -A) mismatched grafts, the former requiring CD8+ T cells, the latter by CD8+ T cell-independent mechanisms. The finding that recognition of xeno-HLAhyb does not require CD8+ T cells while recognition of the identical molecule in a strictly allo context does, demonstrates an alpha1/alpha2 domain-dependent difference in effector mechanism(s). Furthermore, the CD8+ T cell-independence of locus-mismatched rejection suggests the degree of similarity between self and non-self alpha1/alpha2 determines the effector mechanism(s) activated. The HLA Tg model provides a unique approach to characterize these mechanisms and develop tolerance protocols in the context of human transplantation Ags.
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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".