Regulation of MHC Class I Expression in Lung Epithelial Cells during Inflammation
Bibliographic record
Abstract
Abstract Lung infections are a perennial leading cause of death worldwide. The lung epithelium comprises three main cell types: alveolar type I (AT1), alveolar type II (AT2), and bronchiolar cells. Constitutively, these three cell types express extremely low amounts of surface MHC class I (MHC I) molecules, that is, <1% of levels found on medullary thymic epithelial cells (ECs). We report that inhalation of the TLR4 ligand LPS upregulates cell surface MHC I by ∼25-fold on the three subtypes of mouse lung ECs. This upregulation is dependent on Nlrc5, Stat1, and Stat2 and caused by a concerted production of the three IFN families. It is nevertheless hampered, particularly in AT1 cells, by the limited expression of genes instrumental in the peptide loading of MHC I molecules. Genes involved in production and response to cytokines and chemokines were selectively induced in AT1 cells. However, discrete gene subsets were selectively downregulated in AT2 or bronchiolar cells following LPS inhalation. Genes downregulated in AT2 cells were linked to cell differentiation and cell proliferation, and those repressed in bronchiolar cells were primarily involved in cilium function. Our study shows a delicate balance between the expression of transcripts maintaining lung epithelium integrity and transcripts involved in Ag presentation in primary lung ECs.
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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) | 0.001 | 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".