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Weighted gene co-expression network analysis identified hub genes critical to fatty acid composition in Gushi chicken breast muscle
- Source :
- BMC Genomics, Vol 24, Iss 1, Pp 1-11 (2023)
- Publication Year :
- 2023
- Publisher :
- BMC, 2023.
-
Abstract
- Abstract Background The composition and content of fatty acids in the breast muscle are important factors influencing meat quality. In this study, we investigated the fatty acid composition and content in the breast muscle of Gushi chickens at different developmental stages (14 weeks, 22 weeks, and 30 weeks). Additionally, we utilized transcriptomic data from the same tissue and employed WGCNA and module identification methods to identify key genes associated with the fatty acid composition in Gushi chicken breast muscle and elucidate their regulatory networks. Results Among them, six modules (blue, brown, green, light yellow, purple, and red modules) showed significant correlations with fatty acid content and metabolic characteristics. Enrichment analysis revealed that these modules were involved in multiple signaling pathways related to fatty acid metabolism, including fatty acid metabolism, PPAR signaling pathway, and fatty acid biosynthesis. Through analysis of key genes, we identified 136 genes significantly associated with fatty acid phenotypic traits. Protein–protein interaction network analysis revealed that nine of these genes were closely related to fatty acid metabolism. Additionally, through correlation analysis of transcriptome data, we identified 51 key ceRNA regulatory networks, including six central genes, 7 miRNAs, and 28 lncRNAs. Conclusion This study successfully identified key genes closely associated with the fatty acid composition in Gushi chicken breast muscle, as well as their post-transcriptional regulatory networks. These findings provide new insights into the molecular regulatory mechanisms underlying the flavor characteristics of chicken meat and the composition of fatty acids in the breast muscle.
Details
- Language :
- English
- ISSN :
- 14712164
- Volume :
- 24
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- BMC Genomics
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.151e8d690a694397a81e90c2681d0f09
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s12864-023-09685-8