Xing Kai, Liu Huatao, Zhang Fengxia, Liu Yibing, Shi Yong, Ding Xiangdong, Wang Chuduan
Animal Science and Technology College, Beijing University of Agriculture, Beijing, China.
Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China.
J Anim Sci Biotechnol. 2021 Aug 20;12(1):100. doi: 10.1186/s40104-021-00616-9.
Fat deposition is an important economic consideration in pig production. The amount of fat deposition in pigs seriously affects production efficiency, quality, and reproductive performance, while also affecting consumers' choice of pork. Weighted gene co-expression network analysis (WGCNA) is effective in pig genetic studies. Therefore, this study aimed to identify modules that co-express genes associated with fat deposition in pigs (Songliao black and Landrace breeds) with extreme levels of backfat (high and low) and to identify the core genes in each of these modules.
We used RNA sequences generated in different pig tissues to construct a gene expression matrix consisting of 12,862 genes from 36 samples. Eleven co-expression modules were identified using WGCNA and the number of genes in these modules ranged from 39 to 3,363. Four co-expression modules were significantly correlated with backfat thickness. A total of 16 genes (RAD9A, IGF2R, SCAP, TCAP, SMYD1, PFKM, DGAT1, GPS2, IGF1, MAPK8, FABP, FABP5, LEPR, UCP3, APOF, and FASN) were associated with fat deposition.
RAD9A, TCAP, SMYD1, PFKM, GPS2, and APOF were the key genes in the four modules based on the degree of gene connectivity. Combining these results with those from differential gene analysis, SMYD1 and PFKM were proposed as strong candidate genes for body size traits. This study explored the key genes that regulate porcine fat deposition and lays the foundation for further research into the molecular regulatory mechanisms underlying porcine fat deposition.
脂肪沉积是生猪生产中的一个重要经济考量因素。猪的脂肪沉积量严重影响生产效率、品质和繁殖性能,同时也影响消费者对猪肉的选择。加权基因共表达网络分析(WGCNA)在猪的遗传研究中很有效。因此,本研究旨在识别与猪(松辽黑猪和长白猪品种)背膘厚度处于极端水平(高和低)的脂肪沉积相关基因共表达的模块,并识别这些模块中的核心基因。
我们使用在不同猪组织中生成的RNA序列构建了一个由来自36个样本的12,862个基因组成的基因表达矩阵。使用WGCNA识别出11个共表达模块,这些模块中的基因数量从39到3,363不等。四个共表达模块与背膘厚度显著相关。共有16个基因(RAD9A、IGF2R、SCAP、TCAP、SMYD1、PFKM、DGAT1、GPS2、IGF1、MAPK8、FABP、FABP5、LEPR、UCP3、APOF和FASN)与脂肪沉积相关。
基于基因连接度,RAD9A、TCAP、SMYD1、PFKM、GPS2和APOF是四个模块中的关键基因。将这些结果与差异基因分析的结果相结合,提出SMYD1和PFKM作为体型性状的有力候选基因。本研究探索了调控猪脂肪沉积的关键基因,为进一步研究猪脂肪沉积的分子调控机制奠定了基础。