Bitter taste receptor T2R14 detects quorum sensing molecules from cariogenic <i>Streptococcus mutans</i> and mediates innate immune responses in gingival epithelial cells
Bibliographic record
Abstract
Host-pathogen interactions play an important role in defining the outcome of a disease. Recent studies have shown that the bacterial quorum sensing molecules (QSM) can interact with host cell membrane proteins, mainly G protein-coupled receptors (GPCRs), and induce innate immune responses. However, few studies have examined QSM-GPCR interactions and their influence on oral innate immune responses. In this study, we examined the role of bitter taste receptor T2R14 in sensing competence stimulating peptides (CSPs) secreted by cariogenic bacterium Streptococcus mutans and in mediating innate immune responses in gingival epithelial cells (GECs). Transcriptomic and western blot analyses identify T2R14 to be highly expressed in GECs. Our data show that only CSP-1 from S. mutans induces robust intracellular calcium mobilization compared to CSP-2 and CSP-3. By using CRISPR-Cas9, we demonstrate that CSP-1 induced calcium signaling and secretion of cytokines CXCL-8/IL-8, TNF-α, and IL-6 is mediated through T2R14 in GECs. Interestingly, the NF-kB signaling activated by CSP-1 in GECs was independent of T2R14. CSP-1-primed GECs attracted differentiated HL-60 immune cells (dHL-60) and this effect was abolished in T2R14 knock down GECs and also in cells primed with T2R14 antagonist 6-Methoxyflavone (6-MF). Our findings identify S. mutans CSP-1 as a peptide ligand for the T2R family. Our study establishes a novel host-pathogen interaction between cariogenic S. mutans CSP-1 and T2R14 in GECs leading to an innate immune response. Collectively, these findings suggest T2Rs as potential therapeutic targets to modulate innate immune responses upon oral bacterial infections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".