Transcriptomic comparison of liver tissue between Anqing six-end-white pigs and Yorkshire pigs based on RNA sequencing
Bibliographic record
Abstract
Chinese indigenous pig and Western commercial pig breeds show different patterns of lipid metabolism, fat deposition, and fatty acid composition; for these reasons, they have become vitally important models of energy metabolism and obesity in humans. To compare the mechanisms underlying lipid metabolism between Yorkshire pigs (lean type) and Anqing six-end-white pigs (obese type), the liver transcriptomes of six castrated boars with a body weight of approximately 100 kg (three Yorkshire and three Anqing) were analyzed by RNA-seq. The total number of reads produced for each liver sample ranged from 47.05 to 62.6 million. Among 362 differentially expressed genes, 142 were up-regulated and 220 were down-regulated in Anqing six-end-white pigs. Based on these data, 79 GO terms were significantly enriched. The top 10 (the 10 with lowest corrected P-value) significantly enriched GO terms were identified, including lipid metabolic process and carboxylic acid metabolic process. Pathway analysis revealed three significantly enriched KEGG pathways including PPAR signaling pathway, steroid hormone biosynthesis, and retinol metabolism. Based on protein–protein interaction networks, multiple genes responsible for lipid metabolism were identified, such as PCK1, PPARA, and CYP7A1, and these were considered promising candidate genes that could affect porcine liver lipid metabolism and fat deposition. Our results provide abundant transcriptomic information that will be useful for animal breeding and biomedical research.
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".