Advanced Glycation End-Products Can Activate or Block Bitter Taste Receptors
Bibliographic record
Abstract
Bitter taste receptors (T2Rs) are expressed in several tissues of the body and are involved in a variety of roles apart from bitter taste perception. Advanced glycation end-products (AGEs) are produced by glycation of amino acids in proteins. There are varying sources of AGEs, including dietary food products, as well as endogenous reactions within our body. Whether these AGEs are T2R ligands remains to be characterized. In this study, we selected two AGEs, namely, glyoxal-derived lysine dimer (GOLD) and carboxymethyllysine (CML), based on their predicted interaction with the well-studied T2R4, and its physiochemical properties. Results showed predicted binding affinities (Kd) for GOLD and CML towards T2R4 in the nM and μM range, respectively. Calcium mobilization assays showed that GOLD inhibited quinine activation of T2R4 with IC50 10.52 ± 4.7 μM, whilst CML was less effective with IC50 32.62 ± 9.5 μM. To characterize whether this antagonism was specific to quinine activated T2R4 or applicable to other T2Rs, we selected T2R14 and T2R20, which are expressed at significant levels in different human tissues. A similar effect of GOLD was observed with T2R14; and in contrast, GOLD and CML activated T2R20 with an EC50 of 79.35 ± 29.16 μM and 65.31 ± 17.79 μM, respectively. In this study, we identified AGEs as novel T2R ligands that caused either activation or inhibition of different T2Rs.
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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 | 0.000 |
| 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".