IL‐10 Improves Cardiomyocyte Survival via Downstream MyD88‐dependent TLR4 Innate Signaling of Jak/Stat3 Pathway
Bibliographic record
Abstract
Recent evidence indicates that changes in the balance between anti‐inflammatory (IL‐10) and pro‐inflammatory (TNF‐α) cytokines is crucial in heart failure. IL‐10 has been shown to antagonize the pro‐apoptotic effect of TNF‐α via the activation of Jak/Stat3 pathway. In parallel, IL‐10‐mediated cardiac cell survival is regulated by the Toll‐like receptor 4 (TLR4) innate signaling where myeloid differentiation gene factor 88 (MyD88) plays a key role. However, interplay between these two signaling cascades is unknown. Our objective was to determine the role of MyD88‐dependent TLR4 innate signaling in acquiring adaptive response to IL‐10 via the Jak/Stat3 pathway. We found an increased TLR4 activity in isolated cardiomyocytes overexpressing MyD88 in presence or absence of IL‐10 treatment. Furthermore, inhibition of TLR4 activity by MyD88‐shRNA suggested that TLR4 activation is dependent on MyD88 which also abrogates downstream Akt Ser86 /Jak/Stat3 pathway. On the contrary, Stat3 inhibition by DPP 5, 15 also suppresses IL‐10‐induced activation of TLR4 by IRAK M (An inhibitor of TLR4 signaling) inhibition; this suggests that Jak/Stat3 via a feedback mechanism also regulates TLR4. In addition, IL‐10 helps in the dissociation of interleukin‐1 receptor activated kinase 4 (IRAK4) into IRAK1 instead of IRAK2 and there was down‐regulation of TNF‐α receptor associated inducible protein (TRAIP) and its associated adaptor molecules TRADD and FADD. Data suggest that MyD88/IRAK1/IRAK4 recruitment, blocks TNF‐αR associated cellular changes and suppresses apoptosis. Up‐regulation of TLR4 signaling by IL‐10 involving Akt‐Jak/Stat3 pathway could be a potential therapeutic strategy in cardiovascular diseases (Supported by CIHR).
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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) | 0.002 | 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".