S100B Interaction With the Receptor for Advanced Glycation End Products (RAGE)
Bibliographic record
Abstract
RATIONALE: Post-myocardial infarction ventricular remodeling is associated with the expression of a variety of factors including S100B that can potentially modulate myocyte apoptosis. OBJECTIVE: This study was undertaken to investigate the expression and function of S100B and its receptor, the receptor for advanced glycation end products (RAGE) in both postinfarction myocardium and in a rat neonatal myocyte culture model. METHODS AND RESULTS: In a rat model of myocardial infarction following coronary artery ligation, we demonstrate in periinfarct myocytes, upregulation of RAGE, induction of S100B, and release into plasma with consequent myocyte apoptosis. Using a coimmunoprecipitation strategy, we demonstrate a direct interaction between S100B and RAGE. In rat neonatal cardiac myocyte cultures, S100B at concentrations > or = 50 nmol/L induced myocyte apoptosis, as evidenced by increased terminal DNA fragmentation, TUNEL, cytochrome c release from mitochondria to cytoplasm, phosphorylation of extracellular signal-regulated kinase (ERK)1/2 and p53, increased expression and activity of proapoptotic caspase-3, and decreased expression of antiapoptotic Bcl-2. Transfection of a full-length cDNA of RAGE or a dominant-negative mutant of RAGE resulted in increased or attenuated S100B-induced myocyte apoptosis, respectively. Inhibition of ERK1/2 by U0126/PD-98059 or overexpression of a dominant negative p53 comparably inhibited S100B-induced myocyte apoptosis. CONCLUSIONS: These results suggest that interaction of RAGE and its ligand S100B after myocardial infarction may play a role in myocyte apoptosis by activating ERK1/2 and p53 signaling. This receptor-mediated mechanism is uniquely amenable to therapeutic intervention.
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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) | 0.001 | 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) | 0.002 | 0.001 |
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".