Inhibition of Mammalian Target of Rapamycin Augments Lipopolysaccharide-Induced Lung Injury and Apoptosis
Bibliographic record
Abstract
Acute lung injury during bacterial infection is associated with neutrophilic inflammation, epithelial cell apoptosis, and disruption of the alveolar-capillary barrier. TLR4 is required for lung injury in animals exposed to bacterial LPS and initiates proinflammatory responses in part via the transcription factor NF-κB. Ligation of TLR4 also initiates a proapoptotic response by activating IFN-β and STAT1-dependent genes. We recently demonstrated that mammalian target of rapamycin (mTOR), a key controller of cell growth and survival, can physically interact with STAT1 and suppress the induction of STAT1-dependent apoptosis genes. We therefore hypothesized that the mTOR inhibitor rapamycin would increase LPS-induced apoptosis and lung injury in vivo. Rapamycin increased lung injury and cellular apoptosis in C57BL/6J mice exposed to intratracheal LPS for 24 h. Rapamycin also augmented STAT1 activation, and the induction of STAT1-dependent genes that mediate cellular apoptosis (i.e., Fas, caspase-3). LPS-induced lung injury was attenuated in STAT1 knockout mice. In addition, LPS and IFN-β-induced apoptosis was absent in cultured cells lacking STAT1, and, unlike in wild-type cells, a permissive effect of rapamycin was not observed. In contrast to its effect on STAT1, rapamycin inhibited NF-κB activation in vivo and reduced selected markers of inflammation (i.e., neutrophils in the bronchoalveolar lavage fluid, TNF-α). Therefore, although it inhibits NF-κB and neutrophilic inflammation, rapamycin augments LPS-induced lung injury and apoptosis in a mechanism that involves STAT1 and the induction of STAT1-dependent apoptosis genes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".