Cell-Specific Transcriptomic and Mito-Nuclear Imbalance in Lungs Under Intermittent Hypoxia in Adult Male Mice
Bibliographic record
Abstract
Abstract Obstructive sleep apnea and its characteristic intermittent hypoxia (IH) are widely recognized as significant contributors to various pulmonary diseases, including asthma, pulmonary arterial hypertension, fibrosis, and chronic obstructive pulmonary disease. While single-cell RNA sequencing (scRNA-seq) has provided valuable insights into cell-type-specific responses to IH, previous studies have primarily focused on post-hypoxic recovery states, leaving immediate molecular responses during active IH exposure unexplored. To address this critical knowledge gap, we investigated real-time transcriptional responses to IH at single-cell resolution in lung tissue using male mice (n=3/group) exposed to either normoxia or IH (30 cycles/h, nadir 6% O 2 , 12 h/day) for 14 days, with tissue collection during active IH exposure. Our analysis revealed pronounced cell-type-specific transcriptional reprogramming, particularly in airway smooth muscle cells (ASMC), arterial endothelial cells (AEC), and lymphatic endothelial cells (LEC). These changes were characterized by enrichment in pathways related to epithelial-to-mesenchymal transition (ASMC, LEC), myogenesis (ASMC), and antioxidant defenses (AEC, LEC). Most cell types demonstrated substantial upregulation of genes encoding mitochondrial complex I-IV proteins and TCA cycle enzymes accompanied by a decreased expression of genes encoded by mitochondrial DNA that was markedly present in LEC, AEC, and cells of the alveolar-capillary unit, revealing a mito-nuclear imbalance. These findings provide novel insights into the immediate cellular responses to IH, showing previously uncharacterized metabolic reorganization that may underlie the development of IH-related pulmonary complications. This improved understanding of early molecular events during active IH exposure advances our knowledge of sleep apnea-related lung pathologies and may inform future therapeutic strategies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".