Abstract 214: S100A6 Promotes Myocyte Survival by Interacting with the Receptor for Advanced Glycation End Products and Activating Akt
Bibliographic record
Abstract
The receptor for advanced ligation end-products (RAGE) recognizes ligands from diverse families including the S100 calcium binding proteins S100A6 and S100B. In a rat model of myocardial infarction, we have reported the induction of S100B, the upregulation of S100A6 and RAGE mRNA and protein in peri-infarct left ventricular (LV) myocardium, increases in S100B and S1006 serum levels and demonstrated a direct interaction between S100B and S100A6 with RAGE in peri-infarct LV myocardium. To determine the functional role of the interaction of S100A6 and S100B with RAGE, we stimulated rat neonatal cardiac myocyte cultures transfected with a RAGE gene or a dominant-negative cytoplasmic deletion mutant of RAGE with S100B and/or S100A6 for 48 hrs. In RAGE overexpressing myocytes, although both S100 proteins induced the formation of reactive oxygen species, S100B > 10 nM induced myocyte apoptosis, as evidenced by increased terminal deoxynucleotidyltransferase-mediated UTP end labeling (TUNEL), FITC annexin V flow cytometry, cytochrome C release, phosphorylation of ERK1/2 and p53, and increased activity of caspase-3, whereas S100A6 < 10 nM inhibited basal myocyte apoptosis, increased the phosphorylation of Akt and the expression of NF-κB and in combination with S100B inhibited S100B-induced myocyte apoptosis [6.3±0.9% (vehicle), 16.1±0.1.2% (S100B), 4.1±0.4% (S100A6), 6.4±1.5% (S100B+S100A6) (TUNEL positive nuclei)]. The upstream Akt blocker LY294002, inhibited the RAGE dependent anti-apoptotic effects of S100A6 on basal- and S100B-induced myocyte apoptosis. In myocytes expressing dominant-negative RAGE, the contrasting effects of S100B and S100A6 on myocyte apoptosis were absent regardless of Akt inhibition. In conclusion, in a RAGE-dependent manner, S100A6 inhibits myocyte apoptosis via Akt activation.
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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) | 0.006 | 0.002 |
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".