INF-λ enhances expression of MHC class I molecules on thymic epithelial
Bibliographic record
Abstract
Abstract Introduction The major histocompatibility complex class I (MHC-I) molecules is a cell surface glycoprotein involved in the presentation of endogenously derived peptides, to the TCR of CD8 T cells. Moreover, several vital processes specifically depend on the interaction between TCR and the MHC-I presented by the epithelial cells (ECs). However, the regulation of the MHC-I expression has been studied almost exclusively in hematolymphoid cells and very little is known about this process in ECs. In the present work, we performed a deep analysis of MHC-I expression in primary ECs freshly harvested from the thymus, skin, gut, and lung. Methods and Results Our flow cytometry analysis showed that the abundance of cell surface MHC-I was overall 10- to 100-fold greater in thymic ECs (TECs) than in extrathymic EC types. Our transcriptomic analysis revealed a dominant role of IFN signalling in the differential MHC-I expression found in TECs vs. extrathymic ECs. In fact, superior MHC I expression in TECs is unaffected by deletion of Ifnar1 or Ifngr1, but is lessened by deletion of Aire, Ifnlr1, Stat1 or Nlrc5, and is driven mainly by type IFN-λ produced by TECs. Moreover, Ifnlr1−/− mice showed impaired negative selection of CD8 thymocytes, and at 9 months of age, present autoimmune manifestations. Conclusion and Relevance In conclusion, our study opens the door to a new level of understanding of MHC-I regulation in ECs and will pave the way for a more detailed exploration of the impact of IFN-λ signaling in thymic functions.
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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 | 0.001 |
| 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".