Bitter Taste Receptors in Bacterial Infections and Innate Immunity
Bibliographic record
Abstract
BACKGROUND: Bitter taste receptors (TAS2Rs), originally identified for their role in gustation, are now recognized for their functions in extraoral tissues, particularly in innate immune responses. TAS2Rs detect bacterial quorum sensing molecules (QSMs) and other metabolites, enabling the host to sense and respond to pathogenic threats across mucosal surfaces. OBJECTIVE: This review synthesizes current knowledge of TAS2Rs in the context of bacterial infection, emphasizing their mechanisms of immune modulation, genetic polymorphisms, tissue-specific expression, and therapeutic potential. METHODS: A comprehensive literature review was conducted, incorporating in vitro, ex vivo, and in vivo studies investigating TAS2R expression, signaling pathways, and immune functions in response to bacterial pathogens across respiratory, gastrointestinal, and oral tissues. RESULTS: TAS2Rs detect bacterial QSMs, triggering calcium signaling cascades, nitric oxide (NO) release, antimicrobial peptide secretion, and cytokine responses. In respiratory epithelium, TAS2R38 and TAS2R14 modulate mucociliary clearance and NO-mediated bacterial killing. In the oral cavity, TAS2R14 and TAS2R38 influence cytokine production, bacterial uptake, and antimicrobial responses. Intestinal TAS2Rs regulate host defense via genotype-specific pathways, as seen with TAS2R10 and TAS2R43. Polymorphisms in TAS2Rs affect infection susceptibility and immune responses, with implications for diseases like cystic fibrosis, chronic rhinosinusitis, dental caries, and periodontitis. Notably, TAS2R-mediated responses are highly tissue- and bacteria-dependent, with distinct signaling and outcomes observed depending on the pathogen and the local immune environment. CONCLUSIONS: TAS2Rs play an essential role in host-pathogen interactions across multiple mucosal surfaces. Their ability to detect bacterial signals and activate innate immune defenses positions them as promising therapeutic targets. Future studies should focus on in vivo validation, genetic diversity, and receptor-ligand specificity using emerging tools like cryo-electron microscopy and transgenic models.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".