Interaction between Bacteroides and HG-type pectins with different molecular weights
Bibliographic record
Abstract
Pectins, a complex class of polysaccharides, are prominently represented by HG-chains, which are both abundant and extensively studied. Bacteroides species exhibit a remarkable ability to metabolize plant- and animal-derived polysaccharides, including the degradation of HG-type pectins through polysaccharide utilization locus (PUL). However, the effects of structurally diverse HG-type pectins on PUL expression, metabolite production, and metabolic pathways of Bacteroides remain unclear. Addressing this, this study chose HG-type pectins with different molecular weights from citrus (CP) and pomelo (PP), identified Bacteroides species capable of utilizing these pectins, studied the resulting metabolites through non-targeted metabolomics coupled with short-chain fatty acids analysis, and examined the activation of PUL and metabolic pathways by transcriptomic studies. The results showed that Bacteroides thetaiotaomicron A4 and Bacteroides caccae K9 could utilize HG-type pectins with different molecular weights. The production of propionic acid by Bacteroides thetaiotaomicron A4 was significantly affected by the molecular weight of pectins. Utilizing CP, B. thetaiotaomicron A4 enriched metabolites such as carbohydrates, amino acids, peptides, amines, and significantly enhanced pathways such as sphingolipid metabolism and drug metabolism (other enzymes) through PUL75, PUL63, and PUL55. In contrast, when using PP, B. thetaiotaomicron A4 enriched similar metabolites and further upregulated pathways related to sphingolipid metabolism and pyrimidine metabolism. The molecular weight of HG-type pectins differentially affected the expression of carbohydrate-active enzymes and metabolic pathways, resulting in different metabolite profiles. This study aims to contribute to the understanding of structure-activity relationship between pectins and gut microbiota and to inform precision nutrition strategies.
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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) | 0.001 | 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 | 0.001 |
| 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".