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Record W4293217953 · doi:10.3390/metabo12090785

Nuclear Receptor Pathways Mediating the Development of Boar Taint

2022· review· en· W4293217953 on OpenAlexaff
Christine Bone, E. James Squires

Bibliographic record

VenueMetabolites · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGinseng Biological Effects and Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBoar taintSkatoleNuclear receptorAndrostenonePregnane X receptorReceptorTranscription factorSignal transductionBiologyBOARBiochemistryChemistryEndocrinologyGeneGenetics

Abstract

fetched live from OpenAlex

The nuclear receptors PXR, CAR, and FXR are activated by various ligands and function as transcription factors to control the expression of genes that regulate the synthesis and metabolism of androstenone and skatole. These compounds are produced in entire male pigs and accumulate in the fat to cause the development of a meat quality issue known as boar taint. The extent of this accumulation is influenced by the synthesis and hepatic clearance of androstenone and skatole. For this reason, PXR, CAR, and FXR-mediated signaling pathways have garnered interest as potential targets for specialized treatments designed to reduce the development of boar taint. Recent research has also identified several metabolites produced by gut microbes that act as ligands for these nuclear receptors (e.g., tryptophan metabolites, short-chain fatty acids, bile acids); however, the connection between the gut microbiome and boar taint development is not clear. In this review, we describe the nuclear receptor signaling pathways that regulate the synthesis and metabolism of boar taint compounds and outline the genes involved. We also discuss several microbial-derived metabolites and dietary additives that are known or suspected nuclear receptor ligands and suggest how these compounds could be used to develop novel treatments for boar taint.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.998
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.054
GPT teacher head0.299
Teacher spread0.245 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2022
Admission routes1
Has abstractyes

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