Regulation of MHC I expression in lung epithelial cells during inflammation.
Bibliographic record
Abstract
Infections of the respiratory tract are a perennial cause of death worldwide. These diseases typically affect the lung epithelium, composed of three main types of cells: alveolar type I (AT1), alveolar type 2 (AT2), and bronchiolar cells. These three epithelial cells (ECs) types express constitutively low levels of MHC I, representing less than 1% of the levels found in thymic ECs. This weak expression is surprising since it could disturb the elimination of infected or transformed cells. We hypothesized that lung ECs should be able to upregulate their MHC I expression swiftly when needed. We induced lung inflammation in mice using inhalation of LPS. We observed that AT1, AT2, and bronchiolar cells upregulated by 25 times their surface expression of MHC I during LPS-induced inflammation. The concerted production of the three IFN families, thanks to the factors Stat1, Stat2, and Nlrc5, drove this upregulation. The low expression of genes involved in the peptide loading of MHC I molecules nevertheless hampered MHC I upregulation in lung ECs. Discrete gene subsets were selectively differentially regulated by interferon signaling in each type of lung ECs. AT1 increased genes related to cytokine-mediated signaling pathway while AT2 and bronchiolar decreased genes involved in their specialized functions, essential to maintaining the integrity of lung epithelium (epithelium regeneration and cilium movement, respectively). Our results showed a balance between the expression of transcripts involved in immune defenses and transcripts maintaining lung epithelium integrity in lung ECs. It paves the way to a better understanding of antigen presentation in lung ECs, and the pathogenesis of lung inflammation.
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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 | 0.000 |
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".