抑郁症易感基因和抗抑郁药物靶点相关神经细胞类型及相互作用网络分析

Identification of neuronal cell types and interaction network of susceptibility genes of depressive disorder and antidepressant drug targets

基于抑郁症的全基因组关联分析研究(GWAS),对于获得的单核苷酸多态性位点(SNP)使用Haploreg软件进行基因注释,得到SNP注释的102个易感基因.。使用MAGMA软件对GWAS的汇总统计数据做基因水平的分析,获得了270个校正之后显著的基因,两者合并共得到320个抑郁症易感基因。通过药物数据库Drugbank获取133个抗抑郁药物靶点基因。使用EWCE包对抑郁症易感基因和抗抑郁药物靶点在三套脑组织单细胞测序数据中,分别进行神经细胞类型富集分析。结果发现大脑皮质的GABA神经元(抑制性神经元)和谷氨酸能神经元(兴奋性神经元)是抑郁症易感基因和抗抑郁药物靶点共同的神经元。这两种类型的神经细胞可能是抗抑郁药物与抑郁症易感基因相互作用的神经细胞,另外少突胶质前体细胞可能是抑郁症特有的易感神经细胞。使用Network Calculator软件构建网络并进行进行网络拓扑学参数分析。结果表明抑郁症易感基因与抗抑郁药物靶点组成了一个具有显著的相互连接的网络。本研究从单细胞层面揭示抑郁症的遗传机制,在网络层面为寻找新的抗抑郁药物靶点提供了一定的启示。

Our study is based on the genome⁃wide association analysis(GWAS)of depressive disorder,of which single nucleotide polymorphisms(SNPs)are retrieved and annotated by Haploreg software and 102 susceptibility genes are obtained by annotation of SNPs.Then gene⁃level analysis is performed on the summary statistics of GWAS by MAGMA software,and 270 significant genes are obtained after correction.Finally,a total of 320 susceptibility genes are obtained.Besides,133 antidepressant drug target genes are obtained from Drugbank.Then EWCE package is used to analyze cell type enrichment of susceptibility genes of depressive disorder and antidepressant drug targets in three dataset of brain tissue single⁃cell sequencing data respectively,and the results show that GABA neurons(inhibitory neurons)and glutamatergic neurons(excitatory neurons)in the cerebral cortex are the common neurons for susceptibility genes of depressive disorder and antidepressant drug targets,antidepressant drugs may be interacted with susceptibility genes of depressive disorder in these two types of neurons,besides,oligodendrocyte precursor cells may be specific susceptible nerve cells for depressive disorder.Network Calculator software is used to construct the network and conduct network topology parameter analysis and the results show that susceptibility genes of depressive disorder and antidepressant drug targets form a network with significant interactions.Our study provides insights for understanding the genetic mechanism of depressive disorder on the single⁃cell level,as well as investigation of new drug targets by network biology.