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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".