...
首页> 外文期刊>Molecular biology reports >Screening candidate genes related to tenderness trait in Qinchuan cattle by genome array
【24h】

Screening candidate genes related to tenderness trait in Qinchuan cattle by genome array

机译:利用基因组阵列筛选秦川牛嫩性状相关候选基因

获取原文
获取原文并翻译 | 示例
           

摘要

In order to screen candidate genes related to tenderness trait in Qinchuan cattle, we investigated the gene expression profile of Longuissimus dorsi muscle (LDM) tissue and screened differentially expressed genes in LDM from both male and female Qinchuan cattle at 36months of age utilising Bovine Genome Array. Significance Analysis of Microarrays (SAM) was used to identify the differentially expressed genes, Go (Gene Ontology) and pathways analysis were conducted on which by a free web-based Molecular Annotation System 2.0 (MAS 2.0). Approximately 11,000 probe sets representing 10,000 genes were detected in LDM of 36month old Qinchuan cattle. After SAM analysis of the microarray data, 598 genes were shown to be differentially expressed. These genes were predominantly involved in cell adhesion, collagen fibril organization and synthesis, immune responses and cell-matrix adhesion. They included cell adhesion molecules (CAMs) and ECM-receptor interaction molecules. Real-time PCR was performed to validate nine of the differentially expressed genes identified by microarray. The results suggest that at the transcriptional level the residual hardness caused by connective tissues, stroma protein and muscle tissues could mainly result in tenderness differences between male and female Qinchuan cattle.
机译:为了筛选与秦川牛压痛性状有关的候选基因,我们调查了牛背最长肌(Longuissimus dorsi muscle,LDM)组织的基因表达谱,并利用牛基因组阵列从36个月龄的公母和母秦川牛中筛选了LDM中差异表达的基因。 。使用微阵列的重要性分析(SAM)来识别差异表达的基因Go(Gene Ontology),并通过基于网络的免费分子注释系统2.0(MAS 2.0)进行了途径分析。在36个月大的秦川牛的LDM中检测到约10,000个代表10,000个基因的探针组。在对微阵列数据进行SAM分析后,显示598个基因差异表达。这些基因主要参与细胞粘附,胶原纤维的组织和合成,免疫应答和细胞基质粘附。它们包括细胞粘附分子(CAM)和ECM-受体相互作用分子。进行实时PCR以验证通过微阵列鉴定的九个差异表达基因。结果表明,在转录水平上,结缔组织,基质蛋白和肌肉组织引起的残留硬度可能主要导致秦川公牛和雌性牛的嫩度差异。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号