α-Tocopherol transfer protein mediates protective hypercapnia in murine ventilator-induced lung injury
Bibliographic record
Abstract
Rationale Hypercapnia is common in mechanically ventilated patients. Experimentally, ‘therapeutic hypercapnia’ can protect, but it can also cause harm, depending on the mechanism of injury. Hypercapnia suppresses multiple signalling pathways. Previous investigations have examined mechanisms that were known a priori, but only a limited number of pathways, each suppressed by CO2, have been reported. Objective Because of the complexity and interdependence of processes in acute lung injury, this study sought to fill in knowledge gaps using an unbiased screen, aiming to identify a specifically upregulated pathway. Methods and results Using genome-wide gene expression analysis in a mouse model of ventilator-induced lung injury, we discovered a previously unsuspected mechanism by which CO2 can protect against injury: induction of the transporter protein for α-tocopherol, α-tocopherol transfer protein (αTTP). Pulmonary αTTP was induced by inspired CO2 in two in vivo murine models of ventilator-induced lung injury; the level of αTTP expression correlated with degree of lung protection; and, absence of the αTTP gene significantly reduced the protective effects of CO2. α-Tocopherol is a potent antioxidant and hypercapnia increased lung α-tocopherol in wild-type mice, but this did not alter superoxide generation or expression of NRF2-dependent antioxidant response genes in wild-type or in αTTP−/− mice. In concordance with a regulatory role for α-tocopherol in lipid mediator synthesis, hypercapnia attenuated 5-lipoxygenase activity and this was dependent on the presence of αTTP. Conclusions Inspired CO2 upregulates αTTP which increases lung α-tocopherol levels and inhibits synthesis of a pathogenic chemoattractant.
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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 | 0.001 |
| 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".