Metagenomic data-mining reveals contrasting microbial populations responsible for trimethylamine formation in human gut and marine ecosystems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Existing metagenome datasets from many different environments contain untapped potential for understanding metabolic pathways and their biological impact. Our interest lies in the formation of trimethylamine (TMA), a key metabolite in both human health and climate change. Here, we focus on bacterial degradation pathways for choline, carnitine, glycine betaine and trimethylamine N -oxide (TMAO) to TMA in human gut and marine metagenomes. We found the TMAO reductase pathway was the most prevalent pathway in both environments. Proteobacteria were found to contribute the majority of the TMAO reductase pathway sequences, except in the stressed gut, where Actinobacteria dominated. Interestingly, in the human gut metagenomes, a high proportion of the Proteobacteria hits were accounted for by the genera Klebsiella and Escherichia. Furthermore Klebsiella and Escherichia harboured three of the four potential TMA-production pathways (choline, carnitine and TMAO), suggesting they have a key role in TMA cycling in the human gut. In addition to the intensive TMAO–TMA cycling in the marine environment, our data suggest that carnitine-to-TMA transformation plays an overlooked role in aerobic marine surface waters, whereas choline-to-TMA transformation is important in anaerobic marine sediments. Our study provides new insights into the potential key microbes and metabolic pathways for TMA formation in two contrasting environments.
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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 | 0.001 | 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) | 0.000 | 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 it