Combinatorial Therapy with Acetylation and Methylation Modifiers Attenuates Lung Vascular Hyperpermeability in Endotoxemia-Induced Mouse Inflammatory Lung Injury
Bibliographic record
Abstract
Impairment of tissue fluid homeostasis and migration of inflammatory cells across the vascular endothelial barrier are crucial factors in the pathogenesis of acute lung injury (ALI). The goal for treatment of ALI is to target pathways that lead to profound dysregulation of the lung endothelial barrier. Although studies have shown that chemical epigenetic modifiers can limit lung inflammation in experimental ALI models, studies to date have not examined efficacy of a combination of DNA methyl transferase inhibitor 5-Aza 2-deoxycytidine and histone deacetylase inhibitor trichostatin A (herein referred to as Aza+TSA) after endotoxemia-induced mouse lung injury. We tested the hypothesis that treatment with Aza+TSA after lipopolysaccharide induction of ALI through epigenetic modification of lung endothelial cells prevents inflammatory lung injury. Combinatorial treatment with Aza+TSA mitigated the increased endothelial permeability response after lipopolysaccharide challenge. In addition, we observed reduced lung inflammation and lung injury. Aza+TSA also significantly reduced mortality in the ALI model. The protection was ascribed to inhibition of the eNOS-Cav1-MLC2 signaling pathway and enhanced acetylation of histone markers on the vascular endothelial-cadherin promoter. In summary, these data show for the first time the efficacy of combinatorial Aza+TSA therapy in preventing ALI in lipopolysaccharide-induced endotoxemia and raise the possibility of an essential role of DNA methyl transferase and histone deacetylase in the mechanism of ALI.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".