Heme oxygenase‐1 is Transcriptionally Regulated by Akt in Human Vascular Smooth Muscle Cells (VSMC) in response to oxidative stress and inflammation
Bibliographic record
Abstract
Background Inflammation and oxidative stress play a key role in vascular injury in atherogenesis. Heme‐oxygenase 1 (HO‐1) is the rate limiting enzyme in the conversion of heme to carbon monoxide, biliverdin and iron and exerts anti‐inflammatory and anti‐oxidant effects in VSMC. However, the mechanisms underlying HO‐1 activation in response to these atherogenic stimuli are not known. Recent evidence from our work suggests that HO‐1‐mediated cytoprotection is dependent on Akt activity. Objective We investigated the role of Akt in transcriptional regulation of HO‐1 in response to oxidative stress. Methods Human aortic smooth muscle cells (HASMC) were transduced with a retroviral vector (MSCV) expressing Akt, rendered quiescent and exposed to 300 μM of H2O2. HO‐1 mRNA was quantified 6 hr after exposure to H2O2 using RT‐PCR. HO‐1 promoter activity was assessed in COS‐7 cells using a pGL3‐luc reporter construct containing the full length human HO‐1 promoter. Results H2O2 increase HO‐1 mRNA and protein levels in a time‐dependent fashion. Akt overexpression potentiated H2O2 induced HO‐1 mRNA synthesis and increased protein expression. The effect of H2O2 on HO‐1 transcription was abrogated by knockdown of Akt using siRNA. In parallel with induction of HO‐1 message, H2O2 increased HO‐1 promoter activity several fold in COS‐7 cells. Akt siRNA significantly reduced H2O2 induced HO‐1 promoter activity. Conclusion Akt upregulates oxidative stress‐induced HO‐1 activity at the transcriptional level. This mechanism of HO‐1 induction may underlie the dependence of HO‐1 on Akt for protection against oxidative stress‐induced injury in VSMC. Supported by grants from CIHR and HSFC to L.G. Melo
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 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) | 0.002 | 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".