Cholesterol modulates the signaling of chemosensory bitter taste receptor T2R14 in human airway cells
Bibliographic record
Abstract
Bitter taste receptors (T2Rs) are a group of 25 chemosensory receptors expressed at significant levels in the human airways. In human airways, bitter taste receptor 14 (T2R14)-mediated physiological response in ameliorating obstructive airway disorders is an active area of investigation. Therefore, understanding various factors regulating the structure and function of T2R14 will be beneficial. We hypothesize that membrane lipids like cholesterol play a regulatory role in T2R14 signaling in airway cells. We confirmed the expression and signaling of T2R14 in primary human airway smooth muscle (HASM) cells and the human airway epithelial cell line (NuLi-1) using immunoblot analysis and intracellular calcium concentration mobilization experiments, respectively. Next, T2R14 signaling was examined in membrane cholesterol-altered environments by methyl-β-cyclodextrin or cholesterol oxidase treatments. In the cells analyzed, cholesterol depletion affected the agonist-induced T2R14 signaling, and cholesterol replenishment rescued its efficacy. An alternative approach for cholesterol depletion (with cholesterol oxidase pretreatment) also negatively affected the agonist potency at T2R14 in HASM cells. To understand the molecular mechanism of interaction between cholesterol and T2R14, we used site-directed mutagenesis coupled with functional assays and examined the role of putative cholesterol-binding motifs (CRAC and CARC) in T2R14. Functional characterization of wild-type and mutant T2R14 receptors suggests that amino acid residues K110, F236, and L239 are crucial in T2R14-cholesterol functional interaction. In conclusion, our results show that cholesterol influences the T2R14 signaling efficacy by forming direct interactions with the receptor and consequently plays a regulatory role in T2R14-mediated signaling in human airway cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".