Geranylgeranyl transferase 1 modulates autophagy and apoptosis in human airway smooth muscle
Bibliographic record
Abstract
Geranylgeranyl transferase 1 (GGT1) is involved in the posttranslational prenylation of signaling proteins, such as small GTPases. We have shown that blocking the formation of isoprenoids with statins regulates survival of human lung mesenchymal cells; thus, we tested the hypothesis that GGT1 may specifically modulate programmed cell death pathways in these cells. To this end, human airway smooth muscle (HASM) cells were treated with the selective GGT1 inhibitor GGTi-298. Apoptosis was seen using assays for cellular DNA content and caspase activation. Induction of autophagy was observed using transmission electron microscopy, immunoblotting for LC3 lipidation and Atg5-12 complex content, and confocal microscopy to detect formation of lysosome-localized LC3 punctae. Notably, GGT1 inhibition induced expression of p53-dependent proteins, p53 upregulated modulator of apoptosis (Noxa), and damage-regulated autophagy modulator (DRAM), this was inhibited by the p53 transcriptional activation inhibitor cyclic-pifithrin-α. Inhibition of autophagy with bafilomycin-A1 or short-hairpin RNA silencing of Atg7 substantially augmented GGTi-298-induced apoptosis. Overall, we demonstrate for the first time that pharmacological inhibition of GGT1 induces simultaneous p53-dependent apoptosis and autophagy in HASM. Moreover, autophagy regulates apoptosis induction. Thus, our findings identify GGT1 as a key regulator of HASM cell viability.
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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 | 0.000 | 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) | 0.001 | 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".