Interferon-γ Induces X-linked Inhibitor of Apoptosis-associated Factor-1 and Noxa Expression and Potentiates Human Vascular Smooth Muscle Cell Apoptosis by STAT3 Activation
Bibliographic record
Abstract
Interferon (IFN)-gamma actions on the vessel wall play an important role in the pathogenesis of arteriosclerosis, yet the contribution of different IFN-gamma signaling pathways to the phenotypic modulation of vascular smooth muscle cells (VSMCs) are poorly understood. We investigated the effects of IFN-gamma on VSMCs and arteries through interactions involving signal transducer and activator of transcription (STAT) proteins. In addition to STAT1 activation, IFN-gamma consistently phosphorylated STAT3 in human VSMCs but weakly or not at all in human endothelial cells or mouse VSMCs. STAT3 activation resulted in nuclear translocation of this transcription factor. By selectively inhibiting STAT3 and not STAT1 signaling, we identified a number of candidate IFN-gamma-inducible, STAT3-dependent gene products by microarray analysis. Results for selected genes, including the pro-apoptotic molecules X-linked inhibitor of apoptosis associated factor-1 (XAF1) and Noxa, were verified by real time quantitative reverse transcription-PCR and immunoblot analyses. IFN-gamma-induced STAT3 and STAT1 signaling in VSMCs demonstrated reciprocal inhibition. STAT3 activation by IFN-gamma sensitized VSMCs to apoptosis triggered by both death receptor- and mitochondrial-mediated pathways. Knock down of XAF1 and Noxa expression inhibited the priming of VSMCs to apoptotic stimuli by IFN-gamma. Finally, we confirmed the in vivo relevance of our observations using a chimeric animal model of immunodeficient mice bearing human coronary artery grafts in which the expression of XAF1 and Noxa as well as the pro-apoptotic effects induced by IFN-gamma were dependent on STAT3. The data suggest STAT1-independent signaling by IFN-gamma via STAT3 that promotes the death of human VSMCs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".