Superior ability of dietary fiber utilization in obese breed pigs linked to gut microbial hydrogenotrophy
Bibliographic record
Abstract
Abstract Dietary fiber is widely recognized for its benefits to human health. Individual variations in the ability to degrade dietary fiber are influenced by the gut microbiome that may be associated with the host’s metabolic phenotype and genetic diversity. This is exemplified by the distinct fiber digestibility observed in obese (e.g. Meishan) and lean-breed (e.g. Yorkshire) pigs. However, the underlying mechanisms remain unclear. The present study found that with the same diet under the same environment, the obese-type Meishan pigs showed greater dietary fiber digestibility and harbored higher abundances of polysaccharide-degrading bacteria (Bacteroides, Treponema, and Paraprevotella) compared to lean-type Yorkshire pigs. Metatranscriptomic profiling revealed that the elevated presence of Bacteroides contributed to the enrichment of carbohydrate-active enzymes, particularly those degrading arabinoxylan, indicating a preference for arabinoxylan as a substrate in Meishan pigs. Further enzymatic-product measurements, combined with microbial enzyme profiles, validated greater microbial conversion of xylose into short-chain fatty acids (SCFAs) in Meishan pigs. Additionally, higher abundances of hydrogenotrophic microbes (Methanobrevibacter and Blautia) were detected in the Meishan gut, along with the enrichment of methanogenesis and acetogenesis pathways. To determine whether methanogenesis drives inter-breed variation in arabinoxylan degradation, an in vitro experiment using the methanogen inhibitor, 2-bromoethanesulfonate was performed. The results confirmed that Meishan gut microbiome effectively reduced hydrogen accumulation through methanogenesis, promoting arabinoxylan degradation. Conversely, inhibiting methanogenesis by 2-bromoethanesulfonate led to hydrogen accumulation, reduced SCFAs, β-xylosidase activity, and Bacteroides abundances. These findings demonstrate that the Meishan pigs have superior ability of dietary fiber utilization with greater microbial conversion to more SCFAs, which is linked to stronger hydrogenotrophic methanogenesis. This study reinforces the role of gut microbial hydrogenotrophy in dietary fiber utilization in pigs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".