Human fetal heart specific coexpression network involves congenital heart disease/defect candidate genes.
Human fetal heart specific coexpression network involves congenital heart disease/defect candidate genes.
复制标题
人胎儿心脏特异性共表达网络涉及先天性心脏病/缺陷候选基因
DOI：
10.1038/srep46760
复制
发表时间：
2017-04-24
影响因子：
4.6
通讯作者：
Fu Q
中科院分区：
文献类型：
--
作者：
Wang B;You G;Fu Q
心脏发育是一个复杂的过程，需要动态转录调控。这一过程的紊乱将导致严重的发育缺陷，如先天性心脏病/缺陷（CHD）。冠心病是一组具有高度遗传异质性的复杂疾病，与冠心病相关的常见途径在很大程度上仍然未知。在论文中，我们将重点放在人类胎儿心脏样本中的组织特异性基因上，以探索这种途径。我们使用ENCODE项目的人类胎儿组织RNA微阵列数据集来鉴定具有心脏组织特异性表达的基因。基于这些基因表达水平的Pearson相关系数，构建了转录网络。然后检查这些基因的功能、选择限制和疾病关联。我们的分析确定了一个由316个具有人类胎儿心脏特异性表达的基因组成的网络。该网络高度共调控，在四足动物中表现出进化保守的组织表达模式。这个网络中的基因在冠心病特异性基因和疾病突变中富集。利用转录组学数据，我们发现了一个高度协调的基因网络，可能反映了与冠心病病因相关的共同途径。这样的分析将有助于临床研究中疾病相关基因的鉴定。
Heart development is a complex process requiring dynamic transcriptional regulation. Disturbance of this process will lead to severe developmental defects such as congenital heart disease/defect (CHD). CHD is a group of complex disorder with high genetic heterogeneity, common pathways associated with CHD remains largely unknown. In the manuscript, we focused on the tissue specific genes in human fetal heart samples to explore such pathways. We used the RNA microarray dataset of human fetal tissues from ENCODE project to identify genes with heart tissue specific expression. A transcriptional network was constructed for these genes based on the Pearson correlation coefficients of their expression levels. Function, selective constraints and disease associations of these genes were then examined. Our analysis identified a network consisted of 316 genes with human fetal heart specific expression. The network was highly co-regulated and showed evolutionary conserved tissue expression pattern in tetrapod. Genes in this network are enriched in CHD specific genes and disease mutations. Using the transcriptomic data, we discovered a highly concerted gene network that might reflect a common pathway associated with the etiology of CHD. Such analysis should be helpful for disease associated gene identification in clinical studies.
DOI：
10.1093/bioinformatics/bts048
发表时间：
2012-04-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Niknejad A;Comte A;Parmentier G;Roux J;Bastian FB;Robinson-Rechavi M
通讯作者：
Robinson-Rechavi M