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Expression and Functionality of Bitter Taste Receptors in Ovarian and Prostate Cancer

2017· article· en· W3093412025 on OpenAlexafffundabout
Denis J. Dupré, Louis T. P. Martin, Mark W. Nachtigal

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsUniversity of ManitobaDalhousie University
FundersCanadian Institutes of Health ResearchBeatrice Hunter Cancer Research Institute
KeywordsTasteGPR120UmamiCancerMedicineBitter tasteReceptorCancer researchBreast cancerProstate cancerProstateBiologyInternal medicineG protein-coupled receptorBiochemistry

Abstract

fetched live from OpenAlex

Plants secondary metabolites often are poisonous to protect themselves against predators. The composition of ingested food is sensed by specialized sensory cells located in taste buds in the oral cavity, capable of detecting one of the basic taste qualities (sweet, sour, salty, umami, bitter, and possibly fat). In particular, bitter taste is detected in humans by 25 members of the bitter taste receptor (TAS2R) subfamily of G protein‐coupled receptors (GPCRs). Since plant poisonous metabolites are often bitter, sensing bitterness provides protection against poison consumption. Surprisingly, Tas2Rs are expressed in several extra‐oral tissues. For example, a role for extra‐orally expressed TAS2Rs was shown in airway bronchodilation, where TAS2Rs were expressed in various cell types lining the airways, and their stimulation with bitter drugs caused potent muscle relaxation; suggesting that inhaled bitter compounds could be used therapeutically for treatment of airway diseases. Recently, it was shown that mammary epithelial cells express Tas2Rs, and that the expression of some Tas2Rs is downregulated in breast cancer cells. Few studies have shown expression of Tas2Rs in cancer, and our preliminary results show expression and functionality of Tas2Rs in ovarian and prostate cancer, while it was previously reported for pancreatic and breast cancer. While extra‐oral expression of Tas2Rs was demonstrated in some tissues, their expression in diseases like cancer is poorly characterized. We analyzed data from online cancer databases for genetic alterations and noticed that Tas2R gene amplification was very high in breast and ovarian cancer, while few mutations or deletions were present, when compared to other cancers like prostate cancer. Interestingly, drug resistance in cancer cells is often linked to amplification of genes that change absorption of chemotherapeutic agent by cells. Bitter drugs like noscapine, a ligand for Tas2R14, sensitizes chemoresistant ovarian cancer cells to paclitaxel. It is possible that Tas2Rs could have an important role in the development of new therapeutic strategies, alone or in combination with current drug therapies. After showing the expression of Tas2R subtypes expressed in ovarian cancer samples, we would now like to analyze the signaling pathways activated by these receptors. Our preliminary work shows that Tas2Rs are functional and signal in cancer cells with an effect on apoptosis signaling, among others. Further characterization will be needed to understand the role of these receptors in cancer. Overall, the results from our work demonstrate the expression and functionality of Tas2Rs in cancer, and will help define a subset of Tas2Rs that could potentially be targeted for therapy with bitter drugs. Support or Funding Information Operating funds from Beatrice Hunter Cancer Research Institute, Natural Sciences and Engineering Research Council of Canada (NSERC), and studentships from Canadian Institutes of Health Research and the Cancer Research Training Program of the Beatrice Hunter Cancer Research Institute, with funds provided by Motorcycle Ride for Dad – Nova Scotia Chapters.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.023
GPT teacher head0.281
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2017
Admission routes3
Has abstractyes

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