Extensive transfer of genes for edible seaweed digestion from marine to human gut bacteria
Bibliographic record
Abstract
Summary Humans harbor numerous species of colonic bacteria that digest the fiber polysaccharides in commonly consumed terrestrial plants. More recently in history, regional populations have consumed edible macroalgae seaweeds containing unique polysaccharides. It remains unclear how extensively gut bacteria have adapted to digest these nutrients and use these abilities to colonize microbiomes around the world, especially outside Asia. Here, we show that the ability of gut bacteria to digest seaweed polysaccharides is more pervasive than previously appreciated. Using culture-based approaches, we show that known Bacteroides genes involved in seaweed degradation have mobilized into many members of this genus. We also identify several previously unknown examples of marine bacteria-derived genes, and their corresponding mobile DNA elements, that are involved in degrading seaweed polysaccharides. Some of these genes reside in gut-resident, Gram-positive Firmicutes, for which phylogenetic analysis suggests an origin in the Epulopiscium gut symbionts of marine fishes. Our results are important for understanding the metabolic plasticity of the human gut microbiome, the global exchange of genes in the context of dietary selective pressures and identifying new functions that can be introduced or engineered to design and fill orthogonal niches for a future generation of engineered probiotics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| 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 | 0.000 | 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".