Perturbations of gut microbiome and metabolome of pigs infected with <i>Mycoplasma hyorhinis</i>
Bibliographic record
Abstract
BACKGROUND: Mycoplasma hyorhinis is a prevalent respiratory pathogen in swine, causing significant economic loss to pig producers. There is growing evidence that respiratory pathogen infections have a large impact on intestinal microecology. To study the effect of M. hyorhinis infection on gut microbial composition and metabolome profile, pigs were infected with M. hyorhinis. Metagenomic sequencing analysis was performed of fecal samples and a liquid chromatography/tandem mass spectrometry (LC-MS/MS) analysis of gut digesta was made. RESULTS: Pigs infected with M. hyorhinis had enriched Sutterella and Mailhella, and depleted Dechloromonas, Succinatimonas, Campylobacter, Blastocystis, Treponema, and Megasphaera. The pigs infected with M. hyorhinis also had greater abundances of bacterium_0_1xD8_71, Ruminococcus_sp__CAG_353, Firmicutes_bacterium_CAG_194, Firmicutes_bacterium_CAG_534, bacterium_1xD42_87, and lower abundances of Chlamydia_suis, Megasphaera_elsdenii, Treponema_porcinum, Bacteroides_sp__CAG_1060, Faecalibacterium_prausnitzii. Metabolomic analysis revealed that some lipids and lipid-like molecules increased in the small intestine, whereas most lipids and lipid-like molecule metabolites decreased in the large intestine. These altered metabolites induce changes in intestinal sphingolipid metabolism, amino acid metabolism, and thiamine metabolism. CONCLUSION: These findings demonstrate that infection with M. hyorhinis can alter the gut microbial composition and metabolite structure in pigs, which may further affect amino acid metabolism and lipid metabolism in the intestine. © 2023 Society of Chemical Industry.
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".