The Role of the Gut Microbiome on Mercury Toxicology: Protein-Rich Diet Alters Gut Microbiota Composition and Increases Methylmercury Demethylation
Bibliographic record
Abstract
Methylmercury (MeHg) exposure is a global health concern particularly among coastal populations that rely on fish and marine mammals for sustenance. It has been shown that diet can modulate the MeHg kinetics and body burden. The gut microbiome has been identified as a major factor affecting contaminant metabolism, but little is known about the role of gut microbiota on mercury transformations. The objective of this study was to examine the effects of diet on the human gut microbiome and its subsequent effect on MeHg metabolism. Using a series of batch experiments, we evaluated the effects of a changing nutritional component of the diet (by altering relative abundances of carbohydrate or protein) in the gut microbial community structure of two individuals. We measured mercury methylation and demethylation rates using a stable isotope tracer and conducted high throughput sequencing of the 16S rRNA metagenomic analysis to determine the microbial community structure. We observed >90% decrease in initial MeHg concentration in protein-rich diet mixed with the fecal slurry of individual A. Moreover, two microbial taxa were significantly increased from non-detectable to a total of 20% in relative proportion. Individual B gut microbiota did not exhibit similar results. A mixture of different ratios of gut microbiota of individual A and B showed similar demethylation rate as in individual A indicating that the phenotype can be conserved. Our results show that the gut microbiome of some individuals contains microbiota that can be promoted by a protein-rich diet and can increase demethylation of MeHg. The mechanism of the demethylation of MeHg needs to be characterized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".