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Record W2903366567 · doi:10.2131/jts.43.717

Monomethylmercury degradation by the human gut microbiota is stimulated by protein amendments

2018· article· en· W2903366567 on OpenAlexafffund
Galen Guo, Emmanuel Yumvihoze, Alexandre J. Poulain, Hing Man Chan

Bibliographic record

VenueThe Journal of Toxicological Sciences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGut floraMicrobiomeBiologyMetabolismHuman microbiomeMicrobial biodegradationCatabolismHuman fecesEnvironmental chemistryChemistryMicrobiologyFecesBiochemistryBacteriaMicroorganismGenetics

Abstract

fetched live from OpenAlex

Monomethylmercury (MMHg) is a potent neurotoxicant that can be bioaccumulated and biomagnified through trophic levels. Human populations whose diets contain MMHg are at risk of MMHg toxicity. The gut microbiota was identified as a potential factor causing variation in MMHg absorption and body burden. However, little is known about the role of gut microbiota on Hg transformations. We conducted a series of in vitro experiments to study the effects of dietary nutrient change on Hg metabolism and the human gut microbiota using anoxic fecal slurry incubations. We used stable Hg isotope tracers to track MMHg production and degradation and characterized the microbiota using high throughput sequencing of the 16S rRNA gene. We show that the magnitude of MMHg degradation is individual dependent and rapidly responds to changes in nutrient amendments, leading to complete degradation of the MMHg present. Although the mechanism involved remains unknown, it does not appear to involve the well-known mer operon. Our data are the first to show a nutrient dependency on the ability of the simulated human gut microbiota to demethylate MMHg. This work provides much-needed insights into individual variations in Hg absorption and potential toxicity.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.048
GPT teacher head0.323
Teacher spread0.275 · 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.

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

Citations27
Published2018
Admission routes2
Has abstractyes

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