RNA-Seq Reveals Differentially Expressed Genes and Pathways Affecting Intramuscular Fat Metabolism in Huangshan Black Chicken Population
Bibliographic record
Abstract
Intramuscular fat (IMF) plays an important role in meat quality due to its positive correlation with juiciness, tenderness, and flavor. However, for chickens, the molecular mechanisms underlying IMF deposition in thigh muscle have not yet been determined. Here, to identify candidate genes and signaling pathways related to IMF deposition, we deeply explored the chicken transcriptome from thigh muscles of Huangshan Black Chickens with extremely high and low phenotypic values for intramuscular fat content. A total of 128 genes differentially expressed genes (DEGs) were detected, of which 94 were up-regulated and 34 were down-regulated. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways revealed these DEGs (including FABP4, G0S2, PLIN1, SCD1, LFABP, SLC1A6, SLC45A3, ACSBG1, LY86, ST8SIA5, SNAI2, HPGD, EDN2, and THRSP) were significantly enriched in lipid biosynthetic process, steroid biosynthetic and metabolic process, fatty acid metabolic process, and regulation of unsaturated fatty acid metabolic pathways. Additionally, we concluded an interaction network related to lipid metabolism, which might be contributed to the IMF deposition in chicken. Overall, we proposed some new candidate genes and interaction networks that can be associated with IMF deposition and used as biomarkers in meat quality improvement.
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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 | 0.000 |
| 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".