Bitter Taste Receptor T2R14 Modulates Gram-Positive Bacterial Internalization and Survival in Gingival Epithelial Cells
Bibliographic record
Abstract
Bitter-taste receptors (T2Rs) have emerged as key players in host–pathogen interactions and important modulators of oral innate immunity. Previously, we reported that T2R14 is expressed in gingival epithelial cells (GECs) and interacts with competence stimulating peptides (CSPs) secreted by the cariogenic Streptococcus mutans. The underlying mechanisms of the innate immune responses and physiological effects of T2R14 on Gram-positive bacteria are not well characterized. In this study, we examined the role of T2R14 in internalization and growth inhibitory effects on Gram-positive bacteria, namely Staphylococcus aureus and S. mutans. We utilized CRISPR-Cas9 T2R14 knockdown (KD) GECs as the study model to address these key physiological mechanisms. Our data reveal that the internalization of S. aureus is significantly decreased, while the internalization of S. mutans remains unaffected upon knockdown of T2R14 in GECs. Surprisingly, GECs primed with S. mutans CSP-1 resulted in an inhibition of growth for S. aureus, but not for S. mutans. The GECs infected with S. aureus induced T2R14-dependent human β-defensin-2 (hBD-2) secretion; however, S. mutans–infected GECs did not induce hBD-2 secretion, but induced T2R14 dependent IL-8 secretion. Interestingly, our results show that T2R14 KD affects the cytoskeletal reorganization in GECs, thereby inhibiting S. aureus internalization. Our study highlights the distinct mechanisms and a direct role of T2R14 in influencing physiological responses to Gram-positive bacteria in the oral cavity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".