Transgenic Inducible MHC I Overexpression in Mouse Alveolar Type 2 Cells
Bibliographic record
Abstract
Abstract The major histocompatibility complex class I (MHC I) is crucial in adaptive immunity, enabling CD8+ T cells to detect and eliminate infected and cancerous cells. Recent studies have uncovered significant variability in MHC I expression across tissues, challenging the traditional belief of uniform expression. Lung epithelial cells (LECs) express meager amounts of MHC I, which preserves the lung epithelium from excessive inflammation but renders it more susceptible to cancer and infection. Despite MHC I overexpression in various immunopathologies, its precise role in disease initiation or progression remains unclear due to the absence of suitable in vivo models for studying MHC I overexpression. This study introduces a novel mouse model with targeted surface MHC I upregulation. Leveraging a conditional Cre-lox system, we augmented Nlrc5 expression to specifically upregulate MHC I in alveolar type 2 (AT2) LECs, known for their low basal expression of MHC I and significant overexpression in disease. Our model demonstrated a rapid and sustained tenfold increase in MHC I surface expression persisting for up to a year without triggering pathology or inflammation. Comprehensive characterization and validation of this model indicated that MHC I overexpression does not serve as a primary initiator of respiratory diseases under steady-state conditions and shows a therapeutic window for increasing MHC I without significant damage to the lung epithelium. This adaptable model offers insights into the effects of tissue-specific MHC I regulation and presents new avenues for therapeutic development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".