Small interfering RNA knocks down heat shock factor-1 (HSF-1) and exacerbates pro-inflammatory activation of NF-κB and AP-1 in vascular smooth muscle cells
Bibliographic record
Abstract
OBJECTIVES: Heat shock and elevated expression of heat shock proteins suppress activation of the pro-inflammatory transcription factor NF-kappaB. We hypothesized that knocking down the expression of heat shock factor-1 (HSF-1) with RNAi technology would exacerbate angiotensin (Ang) II-induced inflammatory injury in vascular smooth muscle cells (VSMC). METHODS: Rat aorta A10 cells and human intestinal smooth muscle cells were grown without transfection or with transfection with HSF-1 small interfering RNA (siRNA), or negative control siRNA. Cells were stimulated with Ang II (100 nM) to activate the NF-kappaB signaling pathway. RESULTS: HSF-1 siRNA significantly knocked down HSF-1 expression, and one of the downstream heat shock proteins (Hsp), Hsp27, in both cells lines. HSF-1 siRNA also affected cells stressed with heat shock or Ang II treatment. Ang II induced activation of NF-kappaB and AP-1 in untransfected VSMCs, however, Ang II induced significantly higher activities of these pro-inflammatory transcription factors in HSF-1 siRNA transfected cells. Control siRNA had no apparent effect on HSF-1 and Hsp27 expression and Ang II-induced NF-kappaB and AP-1 activation. CONCLUSIONS: These data indicate that the knock down of HSF-1 exacerbates Ang II-induced inflammation in VSMCs, and suggests that heat shock proteins protect against inflammatory injury by suppression of pro-inflammatory transcription factors such as NF-kappaB and AP-1.
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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 | 0.001 |
| 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".