Xylan utilisation promotes adaptation of <i>Bifidobacterium pseudocatenulatum</i> to the human gastrointestinal tract
Bibliographic record
Abstract
Dietary carbohydrates impact the composition of the human gut microbiota. However, the relationship between carbohydrate availability for individual bacteria and their growth in the intestinal environment remains unclear. Here, we show that the availability of long-chain xylans (LCX), one of the most abundant dietary fibres in the human diet, promotes the growth of Bifidobacterium pseudocatenulatum in the adult human gut. Genomic and phenotypic analyses revealed that the availability of LCX-derived oligosaccharides is a fundamental feature of B. pseudocatenulatum, and that some but not all strains possessing the endo-1,4-β-xylanase (BpXyn10A) gene grow on LCX by cleaving the xylose backbone. The BpXyn10A gene, likely acquired by horizontal transfer, was incorporated into the gene cluster for LCX-derived oligosaccharide utilisation. Co-culturing with xylanolytic Bacteroides spp. demonstrated that LCX-utilising strains are more competitive than LCX non-utilising strains even when LCX-derived oligosaccharides were supplied. In LCX-rich dietary interventions in adult humans, levels of endogenous B. pseudocatenulatum increased only when BpXyn10A was detected, indicating that LCX availability is a fitness determinant in the human gut. Our findings highlight the enhanced intestinal adaptability of bifidobacteria via polysaccharide utilisation, and provide a cornerstone for systematic manipulation of the intestinal microbiota through dietary intervention using key enzymes that degrade polysaccharide as biomarkers.
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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) | 0.001 | 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".