Configured for the Human Gut Microbiota: Molecular Mechanisms of Dietary β-Glucan Utilization
Bibliographic record
Abstract
The β-glucans are a disparate group of structurally diverse polysaccharides, whose members are widespread in human diets as components of the cell walls of plants, algae, and fungi (including yeasts), and as bacterial exopolysaccharides. Individual β-glucans from these sources have long been associated with positive effects on human health through metabolic and immunological effects. Remarkably, the β-configured glucosidic linkages that define these polysaccharides render them inaccessible to the limited repertoire of digestive enzymes encoded by the human genome. As a result, the various β-glucans become fodder for the human gut microbiota (HGM) in the lower gastrointestinal tract, where they influence community composition and metabolic output, including fermentation to short chain fatty acids (SCFAs). Only recently, however, have the specific molecular systems that enable the utilization of β-glucans by select members of the HGM been fully elucidated by combined genetic, biochemical, and structural biological approaches. In the context of β-glucan structures and their effects on human nutrition and health, we summarize here the functional characterization of individual polysaccharide utilization loci (PULs) responsible for the saccharification of mixed-linkage β(1→3)/β(1→4)-glucans, β(1→6)-glucans, β(1→3)-glucans, β(1→2)-glucans, and xyloglucans in symbiotic human gut bacteria. These exemplar PULs serve as well-defined biomarkers for the prediction of β-glucan metabolic capability in individual bacterial taxa and across the global human population.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".