Bacteria of the human gut microbiome catabolize red seaweed glycans with carbohydrate-active enzyme updates from extrinsic microbes
Bibliographic record
Abstract
Humans host an intestinal population of microbes--collectively referred to as the gut microbiome--which encode the carbohydrate active enzymes, or CAZymes, that are absent from the human genome. These CAZymes help to extract energy from recalcitrant polysaccharides. The question then arises as to if and how the microbiome adapts to new carbohydrate sources when modern humans change eating habits. Recent metagenome analysis of microbiomes from healthy American, Japanese, and Spanish populations identified putative CAZymes obtained by horizontal gene transfer from marine bacteria, which suggested that human gut bacteria evolved to degrade algal carbohydrates-for example, consumed in form of sushi. We approached this hypothesis by studying such a polysaccharide utilization locus (PUL) obtained by horizontal gene transfer by the gut bacterium Bacteroides plebeius. Transcriptomic and growth experiments revealed that the PUL responds to the polysaccharide porphyran from red algae, enabling growth on this carbohydrate but not related substrates like agarose and carrageenan. The X-ray crystallographic and biochemical analysis of two proteins encoded by this PUL, BACPLE_01689 and BACPLE_01693, showed that they are β-porphyranases belonging to glycoside hydrolase families 16 and 86, respectively. The product complex of the GH86 at 1.3 Å resolution highlights the molecular details of porphyran hydrolysis by this new porphyranase. Combined, these data establish experimental support for the argument that CAZymes and associated genes obtained from extrinsic microbes add new catabolic functions to the human gut microbiome.
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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".