Sex and Disease Regulate MHC I Expression in Human Lung Epithelial Cells
Bibliographic record
Abstract
ABSTRACT Major histocompatibility complex class I (MHC I) molecules present endogenous peptides to CD8+ T-cells for immunosurveillance of infections and cancers. Recent studies revealed unexpected heterogeneity in MHC I expression among cells of different lineages. While respiratory diseases rank among the leading causes of mortality, studies in mice showed that lung epithelial cells (LECs) express lower MHC I levels than all other tested cell types. The present study aimed to evaluate MHC I expression in human LECs from parenchymal explants using single-cell RNA sequencing (scRNA-seq) and immunostaining of primary human LECs. After confirming the low constitutive MHC I expression in human LECs, we observed a significant upregulation of MHC I across three chronic respiratory diseases: chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), and cystic fibrosis (CF). Additionally, we unveiled an unexpected sexual dimorphism in MHC I expression in both health and disease, with males exhibiting higher levels of MHC I under steady-state conditions. Gene expression analyses suggest that differential redox balance between sexes is instrumental in this dimorphism. Our study unveils the complex interplay between MHC I expression, sex, and respiratory diseases. Since, in other models, MHC I upregulation contributes to the development of immunopathologies, we propose that it might have a similar impact on chronic lung diseases. NEW & NOTEWORTHY This study shows that MHC I expression is very low in healthy LECs but escalates significantly in three chronic respiratory diseases, potentially contributing to disease progression. Furthermore, sex-specific divergences in LEC MHC I levels hint at distinct susceptibilities to chronic lung inflammation between males and females. Graphical abstract
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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) | 0.002 | 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".