The porphyran degradation system is complete, phylogenetically and geographically diverse across the gut microbiota of East Asian populations
Bibliographic record
Abstract
The human gut microbiota can acquire new catabolic functions by integrating genetic material coming from the environment, for example from food-associated bacteria. An illustrative example of that is the acquisition by the human gut microbiota of Asian populations of genes coming from marine bacteria living on the surface of red algae that are incorporated into their diet when eating maki-sushi. To better understand the function and evolution of this set of algal genes corresponding to a polysaccharide utilization locus (PUL) dedicated to the degradation of porphyran, the main polysaccharide of the red algae Porphyra sp., we characterized it biochemically, assessed its genetic diversity and investigated its geographical distribution in large public worldwide datasets. We first demonstrated that both methylated and unmethylated fractions are catabolized without the help of external enzymes. By scanning the genomic data of more than 10,000 cultivated isolates as well as metagenomic data from more than 14,000 worldwide individuals, we found that the porphyran PUL is present in 17 different Phocaeicola/Bacteroides species (including 12 species that were not known to carry it), as well as in two Parabacteroides species and two genera from the Bacillota phylum, highlighting multiple lateral transfers within the gut microbiota. We then analyzed the prevalence of this porphyran PUL across 32 countries and showed that it exists in appreciable frequencies (>1%) only in East Asia (Japan, China, Korea). Finally, we identified three major PUL haplotypes which frequencies significantly differ between these East Asian countries. This geographic structure likely reflects the rate of bacterial horizontal transmission between individuals.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".