The pectin metabolizing capacity of the human gut microbiota
Bibliographic record
Abstract
The human gastrointestinal microbiota, densely populated with a diverse array of microorganisms primarily from the bacterial phyla Bacteroidota, Bacillota, and Actinomycetota, is crucial for maintaining health and physiological functions. Dietary fibers, particularly pectin, significantly influence the composition and metabolic activity of the gut microbiome. Pectin is fermented by gut bacteria using carbohydrate-active enzymes (CAZymes), resulting in the production of short-chain fatty acids (SCFAs) such as acetate, propionate, and butyrate, which provide various health benefits. The gastrointestinal microbiota has evolved to produce CAZymes that target different pectin components, facilitating cross-feeding within the microbial community. This review explores the fermentation of pectin by various gut bacteria, focusing on the involved transport systems, CAZyme families, SCFA synthesis capacity, and effects on microbial ecology in the gut. It addresses the complexities of the gut microbiome's response to pectin and highlights the importance of microbial cross-feeding in maintaining a balanced and diverse gut ecosystem. Through a systematic analysis of pectinolytic CAZyme production, this review provides insights into the enzymatic mechanisms underlying pectin degradation and their broader implications for human health, paving the way for more targeted and personalized dietary 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".