Differential Role of Toll‐like Receptors in Elicitation of Cardiac Innate Response to IL‐10
Bibliographic record
Abstract
Pattern recognition receptors such as toll‐like receptor 2 (TLR2) and 4 (TLR4), are associated with myocardial ischemia/reperfusion (I/R) injury. Recently, it was suggested that TLR2 is detrimental whereas TLR4 promotes IL‐10‐mediated cardiac cell survival. However the mechanism of molecular balance between these two innate signaling molecules has not been elucidated. We explored the significance of these TLRs in response to IL‐10 in myocardial I/R injury using ex‐vivo, knockout (KO) and shRNA approaches. The ex‐vivo myocardial I/R injury model showed a marked expression of TLR2 and studies in the IL‐10 gene KO (IL‐10 ‐/‐ ) heart indicated a negative regulation of TLR2 by IL‐10. However, 40min reperfusion with IL‐10 after ischemia triggered TLR4 elevation. Inhibition of TLR4 activity using MyD88‐shRNA suggests a MyD88 adaptor molecule dependent activation of TLR4. Increased interleukin‐1 receptor‐associated kinase‐M (IRAK‐M) during I/R injury upregulates IRAK‐2, indicating IRAK‐M/IRAK‐2 requirement in TLR2 signaling. IL‐10 altered these changes significantly, suggesting that IL‐10 dissociates IRAK4 into IRAK 1. Circulating and myocardial levels of TNF‐α were higher in hearts with I/R injury. Consequently, TLR2‐mediated TNF‐α gene activation led to increased apoptosis by TLR2‐mediated IRAK‐M/IRAK‐2 activation. IL‐10 reduced the TNF‐α receptor‐associated increase in TRAIP/TRADD‐induced cell apoptosis during ischemia injury which led to an increase in IL‐1β to mitigate TGF‐βRII‐mediated fibrosis. Our results suggest that this cytokine may be a key therapeutic molecule in restoring heart health from ischemia injury via TLR4 innate signaling pathway (supported by CIHR).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".