Xenogeneic β2-Microglobulin Substitution Affects Functional Binding of MHC Class I Molecules by CD8+ T Cells
Bibliographic record
Abstract
NK cells and CD8+ T cells bind MHC-I molecules using distinct topological interactions. Specifically, murine NK inhibitory receptors bind MHC-I molecules at both the MHC-I H chain regions and beta2-microglobulin (beta2m) while TCR engages MHC-I molecules at a region defined solely by the class I H chain and bound peptide. As such, alterations in beta2m are not predicted to influence functional recognition of MHC-I by TCR. We have tested this hypothesis by assessing the capability of xenogeneic beta2m to modify the interaction between TCR and MHC-I. Using a human beta2m-transgenic C57BL/6 mouse model, we show that human beta2m supports formation and expression of H-2K(b) and peptide:H-2K(b) complexes at levels nearly equivalent to those in wild-type mice. Despite this finding, the frequencies of CD8+ single-positive thymocytes in the thymus and mature CD8+ T cells in the periphery were significantly reduced and the TCR Vbeta repertoire of peripheral CD8+ T cells was skewed in the human beta2m-transgenic mice. Furthermore, the ability of mouse beta2m-restricted CTL to functionally recognize human beta2m+ target cells was diminished compared with their ability to recognize mouse beta2m+ target cells. Finally, we provide evidence that this effect is achieved through subtle conformational changes occurring in the distal, peptide-binding region of the MHC-I molecule. Our results indicate that alterations in beta2m influence the ability of TCR to engage MHC-I during normal T cell physiology.
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) | 0.001 | 0.000 |
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".