Inhibition of Mammalian Target of Rapamycin Augments Lipopolysaccharide-Induced Lung Injury and Apoptosis
Why this work is in the frame
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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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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 it