Toll-like receptor stimulation in cardiomyoctes decreases contractility and initiates an NF-κB dependent inflammatory response☆
Bibliographic record
Abstract
OBJECTIVE: The transmembrane receptor family of Toll-like receptors (TLRs) may play a role in initiating early inflammatory and functional responses to danger signals arising from ischemia-reperfusion and inflammatory stimuli. We determined whether Toll-like receptors are expressed in cardiac tissue and whether stimulation with cognate ligands would result in a pro-inflammatory response and decreased cardiomyocyte contractility. METHODS AND RESULTS: We observed mRNA expression of TLR2, TLR3, TLR4, TLR5, TLR7 and TLR9 in both whole heart tissue and a murine cardiomyocyte cell line (HL-1). Ligand activation of TLR2, TLR4 and TLR5, but not TLR3, TLR7 or TLR9, resulted in cardiomyocyte expression of the inflammatory cytokine IL-6, the chemokines KC and MIP-2, and the cell surface adhesion molecule ICAM-1. Activation of these Toll-like receptors was associated with decreased cardiomyocyte contractility. Using transfection of a nuclear factor kappa B (NF-kappaB)-Luciferase reporter plasmid, we found significantly increased NF-kappaB transcriptional activity in response to TLR2, TLR4 and TLR5 activation in cardiomyocytes. Further, a chemical inhibitor of NF-kappaB, pyrrolidine dithiocarbamate (PDTC), as well as transfection using a dominant negative form of IKKbeta, resulted in profound reduction of the TLR-initiated pro-inflammatory response. CONCLUSIONS: Cardiomyocytes express most known Toll-like receptors. Of these, TLR2, TLR4 and TLR5 signal via NF-kappaB, resulting in decreased contractility and a concerted inflammatory response.
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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) | 0.003 | 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".