Interactive Role of TLR4 and TLR2 in Cardiac Functions During Stressful Conditions
Bibliographic record
Abstract
An appropriate balance between anti‐inflammatory interleukin‐10 (IL‐10) and pro‐inflammatory tumor necrosis factor‐α (TNF‐α) cytokines has been suggested for a normal functioning of the heart. It has been suggested that toll‐like receptor 4 (TLR4) promotes IL‐10‐mediated cardiac cell survival while another receptor, TLR2, from the same family is detrimental. We examined the interactive role of these two innate signaling molecules (TLR4 and TLR2) under stressful conditions including interleukin‐10 knockout (IL‐10−/−) mice, global ischemia/reperfusion (I/R) injury rat hearts and shRNA experimental models. Circulating and myocardial levels of TNF‐α as well as apoptosis and fibrosis were higher in IL‐10−/− hearts. Increase in TLR2 in IL‐10−/− hearts indicated its negative regulation by IL‐10. The ex‐vivo I/R also caused a marked upregulation of TLR2 and TNF‐α as well as apoptotic and fibrotic signals. However, 40 min reperfusion with IL‐10 in the I/R hearts, triggered an increase in TLR4 expression. Increase in interleukin‐1 receptor‐associated kinase‐M (IRAK‐M) and IRAK‐2 activity during I/R injury suggested their role in TLR2 signaling. Inhibition of TLR4 activity as a consequence of RNAi‐mediated suppression of myeloid differentiation gene 88 (MyD88) suggested a MyD88‐dependent activation of TLR4. Inclusion of IL‐10 during reperfusion, also significantly downregulated the expression of IRAK‐2, TRAIP and apoptotic signals, caspase 3 and Bax/Bclxl ratio. IL‐10 reduced the TNF‐α receptor‐associated increase in TRAIP/TRADD ‐induced apoptosis during ischemia injury which led to an increase in IL‐1β to mitigate TGF‐βRII‐mediated fibrosis. IL‐10 mitigation of these changes suggests that IL‐10 stimulation through TLR4 signaling, dissociates IRAK‐4 into IRAK‐1 instead of IRAK‐2 and may be an important therapeutic approach in restoring heart health from I/R injury. Support or Funding Information Supported by Canadian Institutes of Health Research and Research Manitoba.
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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 | 0.000 |
| 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".