S100A8 and S100A9 Mediate Endotoxin-Induced Cardiomyocyte Dysfunction via the Receptor for Advanced Glycation End Products
Bibliographic record
Abstract
Cardiovascular dysfunction as a result of sepsis is the leading cause of death in the critically ill. Cardiomyocytes respond to infectious pathogens with a Toll-like receptor-initiated proinflammatory response in conjunction with a decrease in contractility, although the downstream events linking Toll-like receptor activation and reduced cardiac contractility remain to be elucidated. Using microarray analysis of cardiac tissue exposed to systemic lipopolysaccharide (LPS), we discovered that 2 small calcium-regulating proteins (S100A8 and S100A9) are highly upregulated. HL-1 cardiomyocytes, isolated primary cardiomyocytes, and live mice were exposed to LPS, whereas beating HL-1 cells had S100A8 and S100A9 overexpressed and their calcium flux quantified. Using in vivo microbubble technology, we delivered S100A8 and S100A9 to normal mouse hearts; using the same technology, we inhibited S100A9 production in mouse hearts and subsequently exposed them to LPS. Coimmunoprecipitation of S100A8 and S100A9 identified interaction with RAGE (the receptor for advanced glycation end products), the cardiac function and postreceptor signaling of which were investigated. HL-1 cardiomyocytes, isolated primary cardiomyocytes, and whole hearts exposed to LPS have large increases in S100A8 and S100A9. Cardiac overexpression of S100A8 and S100A9 led to a RAGE-dependent decrease in calcium flux and, in the intact mouse, to a decreased cardiac ejection fraction, whereas knockdown of S100A9 attenuated LPS-induced cardiac dysfunction. Cardiomyocytes exposed to LPS express S100A8 and S100A9, leading to a RAGE-mediated decrease in cardiomyocyte contractility. This finding provides a novel mechanistic link between circulating pathogen-associated molecular products and subsequent cardiac dysfunction.
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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".