Interleukin-1β Reduces L-type Ca2+ Current through Protein Kinase Cϵ Activation in Mouse Heart
Bibliographic record
Abstract
Inflammation is now widely recognized as a key component of heart disease. Patients suffering from arrhythmias and heart failure have increased levels of tumor necrosis factor-α (TNFα) and interleukin-1β (IL-1β). Evidence suggests that these cytokines are important mediators of cardiac remodeling; however, their effects on ion channels and arrhythmogenesis remain incompletely understood. The L-type Ca(2+) current (ICaL) is a major determinant of the plateau phase of cardiac action potential and has a critical excitation-contraction coupling role. Thus, altering its properties could have detrimental effects on cardiac electrical and contractile functions. Accordingly, the objective of this study was to elucidate the effect of TNFα and IL-1β on ICaL, while exploring the underlying regulatory mechanisms. Neonatal mouse ventricular myocytes were treated with a pathophysiological concentration (30 pg/ml) of TNFα and IL-1β for 24 h. Voltage-clamp recordings showed that TNFα had no effect on ICaL, whereas IL-1β decreased the current density by 36%. Although both IL-1β- and TNFα-treated myocytes showed significant increase in reactive oxidative species (ROS), Western blot experiments revealed that only IL-1β increased PKCϵ membrane translocation. The antioxidant N-acetyl-L-cysteine normalized ROS levels and restored ICaL density. Furthermore, the PKCϵ translocation inhibitor ϵ-V1-2 blocked the effect of IL-1β on ICaL. The reduction of ICaL by IL-1β was also seen in cultured adult ventricular myocytes. Overall, chronic IL-1β treatment decreased ICaL density in cardiomyocytes. These effects implicated ROS signaling and PKCϵ activation. These findings could contribute to explain the role of IL-1β in the development of arrhythmia and heart failure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".