Cynaropicrin Increases [Ca<sup>2+</sup>]<sub>i</sub> and Ciliary Beat Frequency in Human Airway Epithelial Cells by Inhibiting SERCA
Bibliographic record
Abstract
Mucociliary clearance (MCC) is a host defense mechanism of the respiratory system. Beating cilia plays a crucial role in the MCC process and ciliary beat frequency (CBF) is activated by several factors including elevations of the intracellular cAMP concentration ([cAMP]i), intracellular Ca2+ concentration ([Ca2+]i), and intracellular pH (pHi). In this study, we investigated whether an artichoke-extracted component cynaropicrin could be a beneficial compound for improving MCC. We found that cynaropicrin increased [cAMP]i using A549 cells bearing Pink Flamindo. Then, we also confirmed that cynaropicrin elevates CBF using airway epithelial ciliated cells (AECCs). We next investigated the effects of cynaropicrin on the alternation of [Ca2+]i, and pHi. Cynaropicrin increased [Ca2+]i, but not pHi. Further experiments also found that cynaropicrin increased [cAMP]i primarily by raising [Ca2+]i. To elucidate the mechanisms of cynaropicrin to increase [Ca2+]i, we investigated the alternation of the effects of cynaropicrin on [Ca2+]i using several compounds. BTP-2 and ruthenium red (RuR) inhibited cynaropicrin-induced [Ca2+]i increase and RuR reduced also [cAMP]i. These results suggest that cynaropicrion increased [Ca2+]i by augmenting the Ca2+ influx and that the increase of [cAMP]i by cynaropicrin was induced by [Ca2+]i elevation. Interestingly, cynaropicrin decreased the Ca2+ concentration in the endoplasmic reticulum following inhibition of sarco-endoplasmic reticulum Ca2+-ATPase (SERCA). SERCA activator CDN1163 abolished this effect. Furthermore, RuR and Ca2+-free conditions suppressed the increase of CBF. In conclusion, cynaropicrin inhibits SERCA, induces store-operated calcium entry, and thereby increases CBF.
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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".