RNA-Seq and lipidomics reveal different adipogenic processes between bovine perirenal and intramuscular adipocytes
Bibliographic record
Abstract
Adipogenesis involves complex interactions between transcription and metabolic signalling. Exploration of the developmental characteristics of intramuscular adipocyte will provide targets for enhancing beef cattle marbling without increasing obesity. Few reports have compared bovine perirenal and intramuscular adipocyte transcriptomes using the combined analysis of transcriptomes and lipid metabolism to explore differences in adipogenic characteristics. We identified perirenal preadipocytes (PRA) and intramuscular preadipocytes (IMA) in Qinchuan cattle. We found that IMA were highly prolific in the early stages of adipogenesis, while PRA shows a stronger adipogenic ability in the terminal differentiation. Bovine perirenal and intramuscular adipocytes were detected through the combined analysis of the transcriptome and metabolome. More triglyceride was found to be upregulated in perirenal adipocytes; however, more types and amounts of unsaturated fatty acids were detected in intramuscular adipocytes, including eicosapentaenoic acid (20:5 n-3; EPA) and docosahexaenoic acid (22:6 n-3; DHA). Furthermore, differentially expressed genes in perirenal and intramuscular adipocytes were positively correlated with the eicosanoid, phosphatidylcholine (PC), phosphatidyl ethanolamine (PE), and sphingomyelin contents. Associated differential metabolic pathways included the glycerolipid and glycerophospholipid metabolisms. Our research findings provide a basis for the screening of key metabolic pathways or genes and metabolites involved in intramuscular fat production in cattle.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".