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Comparative transcriptomic analysis reveals region-specific expression patterns in different beef cuts

BACKGROUND: Beef cuts in different regions of the carcass have different meat quality due to their distinct physiological function. The objective of this study was to characterize the region-specific expression differences using comparative transcriptomics analysis among five representative beef cut...

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Detalles Bibliográficos
Autores principales: Zhang, Tianliu, Wang, Tianzhen, Niu, Qunhao, Zheng, Xu, Li, Haipeng, Gao, Xue, Chen, Yan, Gao, Huijiang, Zhang, Lupei, Liu, George E., Li, Junya, Xu, Lingyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9123670/
https://www.ncbi.nlm.nih.gov/pubmed/35596128
http://dx.doi.org/10.1186/s12864-022-08527-3
Descripción
Sumario:BACKGROUND: Beef cuts in different regions of the carcass have different meat quality due to their distinct physiological function. The objective of this study was to characterize the region-specific expression differences using comparative transcriptomics analysis among five representative beef cuts (tenderloin, longissimus lumborum, rump, neck, chuck). RESULTS: We obtained 15,701 expressed genes in 30 muscle samples across five regions from carcass meat. We identified a total of 80 region-specific genes (RSGs), ranging from three (identified in the rump cut) to thirty (identified in the longissimus lumborum cut), and detected 25 transcription factors (TFs) for RSGs. Using a co-expression network analysis, we detected seven region-specific modules, including three positively correlated modules and four negatively correlated modules. We finally obtained 91 candidate genes related to meat quality, and the functional enrichment analyses showed that these genes were mainly involved in muscle fiber structure (e.g., TNNI1, TNNT1), fatty acids (e.g., SCD, LPL), amino acids (ALDH2, IVD, ACADS), ion channel binding (PHPT1, SNTA1, SUMO1, CNBP), protein processing (e.g., CDC37, GAPDH, NRBP1), as well as energy production and conversion (e.g., ATP8, COX8B, NDUFB6). Moreover, four candidate genes (ALDH2, CANX, IVD, PHPT1) were validated using RT-qPCR analyses which further supported our RNA-seq results. CONCLUSIONS: Our results provide valuable insights into understanding the transcriptome regulation of meat quality in different beef cuts, and these findings may further help to improve the selection for health-beneficial meat in beef cattle. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08527-3.