Identification of key LncRNAs and mRNAs associated with intramuscular fat in pig via WGCNA
Bibliographic record
Abstract
BACKGROUND: Intramuscular fat (IMF) not only directly affects the tenderness, juiciness, and overall flavour of meat but also plays a significant role in influencing consumer preferences for pork. Therefore, exploring key biomarkers that influence IMF deposition is highly important for breeding high-quality pork. IMF is a typical quantitative trait that is regulated by the interaction of multiple coding and noncoding RNAs. Traditional differential analysis methods typically focus on individual genes, making it difficult to identify key genes and their underlying mechanisms accurately. Weighted gene coexpression network analysis (WGCNA) is an efficient and accurate method for identifying and characterizing key pathways and genes associated with complex traits. Therefore, the aim of this study was to construct an mRNA‒lncRNA coexpression network related to IMF using WGCNA to explore and identify potential candidate genes that influence IMF in pigs. RESULTS: Full-length transcriptome sequencing was performed on 31 220-day-old Jiangquan black pigs raised in the same environment, and a gene expression matrix comprising 25,609 genes was constructed. Nine coexpression modules were identified through WGCNA, with the number of genes in these modules ranging from 33 to 3648. The magenta module (corr = 0.7, P < 0.01) and the turquoise module (corr = -0.77, P < 0.01) were significantly associated with IMF deposition. Hub genes in each module were identified on the basis of the screening criteria of GS > 0.4 and MM > 0.8. Combined with enrichment analysis and protein‒protein interaction (PPI) analysis, 18 key mRNAs potentially related to IMF were selected: CRKL, CBL, PDGFRB, DOCK1, YWHAH, HSP90AB1, LOC100524873, NDUFA1, NDUFA11, NDUFA12, NDUFA2, NDUFAB1, NDUFB10, NDUFB3, NDUFB7, NDUFS5, NDUFS6, and UQCR10. To explore the regulatory role of lncRNAs in the process of IMF deposition, we constructed an lncRNA‒mRNA‒pathway network on the basis of the relationships between lncRNAs and key mRNAs, as well as the results of Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. This network includes four key lncRNAs (TGOLN2, LOC100521518, LOC100524915, and LOC100622481) and predicts the potential mechanisms by which lncRNAs regulate IMF deposition. CONCLUSIONS: Through WGCNA, enrichment analysis, and PPI analysis, 18 mRNAs and four lncRNAs potentially involved in IMF deposition were identified, and the lncRNA regulatory pathways were preliminarily explored. Our findings provide new insights into the regulatory mechanisms of pig IMF deposition and lay the foundation for further exploration of the molecular mechanisms underlying pig fat deposition.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".