Chemosensory bitter taste receptors (T2Rs) are activated by multiple antibiotics
Bibliographic record
Abstract
Many medications including antibiotics taste bitter. The potency of these antibiotics on the 25 bitter taste receptors (T2Rs) in humans remains poorly understood. Here we characterize by sensory and structure-function analyses how antibiotics frequently used to treat airway infections in cystic fibrosis activate multiple human T2Rs. The potency of the broad-spectrum antibiotics, tobramycin, levofloxacin, and azithromycin on the highly expressed T2Rs in airways, T2R4, T2R14, and T2R20 was pursued. The amino acids and structural features of T2R4, T2R14, and T2R20 important for antibiotic binding were characterized by mutational analysis in heterologous cell-based assays. Strikingly, extracellular loop 2 in T2Rs performs a key function in binding to antibiotics with contribution from residues in transmembrane helices. Our results suggest that different antibiotics activate multiple T2Rs with different potencies. An understanding of the nonantibiotic and physiologic effects mediated through T2Rs on the host cells is much needed.-Jaggupilli, A., Singh, N., De Jesus, V. C., Gounni, M. S., Dhanaraj, P., Chelikani, P. Chemosensory bitter taste receptors (T2Rs) are activated by multiple antibiotics.
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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 | 0.001 |
| 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 | 0.000 |
| 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".