Glycyrrhizic Acid and Compound Probiotics Supplementation Alters the Intestinal Transcriptome and Microbiome of Weaned Piglets Exposed to Deoxynivalenol
Bibliographic record
Abstract
Deoxynivalenol (DON) is a widespread mycotoxin that affects the intestinal health of animals and humans. In the present study, we performed RNA-sequencing and 16S rRNA sequencing in piglets after DON and glycyrrhizic acid and compound probiotics (GAP) supplementation to determine the changes in intestinal transcriptome and microbiota. Transcriptome results indicated that DON exposure altered intestinal gene expression involved in nutrient transport and metabolism. Genes related to lipid metabolism, such as PLIN1, PLIN4, ADIPOQ, and FABP4 in the intestine, were significantly decreased by DON exposure, while their expressions were significantly increased after GAP supplementation. KEGG enrichment analysis showed that GAP supplementation promoted intestinal digestion and absorption of proteins, fats, vitamins, and other nutrients. Results of gut microbiota composition showed that GAP supplementation significantly improved the diversity of gut microbiota. DON exposure significantly increased Proteobacteria, Actinobacteria, and Bacillus abundances and decreased Firmicutes, Lactobacillus, and Streptococcus abundances; however, dietary supplementation with GAP observably recovered their abundances to normal. In addition, predictive functions by PICRUSt analysis showed that DON exposure decreased lipid metabolism, whereas GAP supplementation increased immune system. This result demonstrated that dietary exposure to DON altered the intestinal gene expressions related to nutrient metabolism and induced disturbances of intestinal microbiota, while supplementing GAP to DON-contaminated diets could improve intestinal health for piglets.
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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 | 0.001 |
| 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".