Biological Network Approach for the Identification of Regulatory Long Non-Coding RNAs Associated With Metabolic Efficiency in Cattle
Biological Network Approach for the Identification of Regulatory Long Non-Coding RNAs Associated With Metabolic Efficiency in Cattle
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DOI：
10.3389/fgene.2019.01130
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发表时间：
2019-11-22
影响因子：
3.7
通讯作者：
Kuehn, Christa
中科院分区：
文献类型：
--
作者：
Nolte, Wietje;Weikard, Rosemarie;Kuehn, Christa
背景：已经发现与不同的家畜饲料效率相关的基因组区域主要在蛋白质编码序列之外。长链非编码RNA（lncRNA）在哺乳动物中可以调节染色质可及性、基因表达和作为重要的代谢调节因子。通过整合表型，转录组学和代谢组学数据与数量性状基因座数据优先共表达网络分析，我们的目的是确定和功能特征lncRNA的代谢效率在牛中具有潜在的关键调节作用。材料与方法：根据公牛的剩余饲料摄入量、奶牛的能量校正乳和两种性别的肌内脂肪含量，将Charolais x Holstein F-2种群的杂交动物（n = 48）分配到高代谢效率组或低代谢效率组。来自空肠、肝脏、骨骼肌和瘤胃的组织样品通过链总RNA测序（RNAseq）进行全局转录组学分析，并且血浆样品用于640种代谢物的谱分析。为了鉴定指定组织内的lncRNA，建立了项目特异性转录组注释。随后，将新的转录物分类为潜在的lncRNA状态，总共产生了属于3，287个位点的7，646个预测的lncRNA转录物。调节影响因子方法分别突出显示空肠、肝脏、肌肉和瘤胃中的92、55、35和73个lncRNA。其随后的高调控影响因子得分表明在基因组中潜在的调控关键功能，所述基因组包括显示差异表达的基因座、组织特异性和与用于剩余采食量或产奶量的数量性状基因座区域重叠的基因座。这些都进行了部分相关和信息论分析与优先级的基因集。结果和结论：独立的，显著的和组特异性的相关性（|R|> 0.8）用于为高代谢效率组和低代谢效率组构建网络，分别产生1，522和1，732个节点。八个lncRNA显示出特别高的连接性（>100个节点）。来自偏相关和信息理论网络的代谢产物和基因，其各自与相应的lncRNA显著相关，被包括在富集分析中，表明八种lncRNA的不同受影响的途径。与代谢效率相关的LncRNA被分类为在功能上参与肝脏氨基酸代谢和蛋白质合成以及骨骼肌细胞中的钙信号传导和神经元型一氧化氮合酶信号传导。
Background: Genomic regions associated with divergent livestock feed efficiency have been found predominantly outside protein coding sequences. Long non-coding RNAs (lncRNA) can modulate chromatin accessibility, gene expression and act as important metabolic regulators in mammals. By integrating phenotypic, transcriptomic, and metabolomic data with quantitative trait locus data in prioritizing co-expression network analyses, we aimed to identify and functionally characterize lncRNAs with a potential key regulatory role in metabolic efficiency in cattle. Materials and Methods: Crossbred animals (n = 48) of a Charolais x Holstein F-2-population were allocated to groups of high or low metabolic efficiency based on residual feed intake in bulls, energy corrected milk in cows and intramuscular fat content in both genders. Tissue samples from jejunum, liver, skeletal muscle and rumen were subjected to global transcriptomic analysis via stranded total RNA sequencing (RNAseq) and blood plasma samples were used for profiling of 640 metabolites. To identify lncRNAs within the indicated tissues, a project-specific transcriptome annotation was established. Subsequently, novel transcripts were categorized for potential lncRNA status, yielding a total of 7,646 predicted lncRNA transcripts belonging to 3,287 loci. A regulatory impact factor approach highlighted 92, 55, 35, and 73 lncRNAs in jejunum, liver, muscle, and rumen, respectively. Their ensuing high regulatory impact factor scores indicated a potential regulatory key function in a gene set comprising loci displaying differential expression, tissue specificity and loci overlapping with quantitative trait locus regions for residual feed intake or milk production. These were subjected to a partial correlation and information theory analysis with the prioritized gene set. Results and Conclusions: Independent, significant and group-specific correlations (|r| > 0.8) were used to build a network for the high and the low metabolic efficiency group resulting in 1,522 and 1,732 nodes, respectively. Eight lncRNAs displayed a particularly high connectivity (>100 nodes). Metabolites and genes from the partial correlation and information theory networks, which each correlated significantly with the respective lncRNA, were included in an enrichment analysis indicating distinct affected pathways for the eight lncRNAs. LncRNAs associated with metabolic efficiency were classified to be functionally involved in hepatic amino acid metabolism and protein synthesis and in calcium signaling and neuronal nitric oxide synthase signaling in skeletal muscle cells.