Li Jian, Lin Xue, Wang Mingya, Hu Yunyun, Xue Kaiyu, Gu Shuanglin, Lv Li, Huang Saijun, Xie Wei
Key Laboratory of DGHD, MOE, Institute of Life Sciences, Southeast University, Nanjing, 210096, China.
Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China.
BMC Med Genomics. 2020 Mar 26;13(1):54. doi: 10.1186/s12920-020-0693-2.
Autism is a complex disease involving both environmental and genetic factors. Recent efforts have implicated the correlation of genomic imprinting and brain development in autism, however the pathogenesis of autism is not completely clear. Here, we used bioinformatic tools to provide a comprehensive analysis of the autism-related genes, genomic imprinted genes and the spatially and temporally differentially expressed genes of human brain, aiming to explore the relationship between autism, brain development and genomic imprinting.
This study analyzed the distribution correlation between autism-related genes and imprinted genes on chromosomes using sliding windows and statistical methods. The normal brains' gene expression microarray data were reanalyzed to construct a spatio-temporal coordinate system of gene expression during brain development. Finally, we intersected the autism-related genes, imprinted genes and brain spatio-temporally differentially expressed genes for further analysis to find the major biological processes that these genes involved.
We found a positive correlation between the autism-related genes' and imprinted genes' distribution on chromosomes. Through the analysis of the normal brain microarray data, we constructed a spatio-temporal coordinate system of gene expression during human brain development, and obtained 13 genes that are differentially expressed in the process of brain development, which are both autism-related genes and imprinted genes. Furthermore, enrichment analysis illustrated that these genes are mainly involved in the biological processes, such as gamma-aminobutyric acid signaling pathway, neuron recognition, learning or memory, and regulation of synaptic transmission. Bioinformatic analysis implied that imprinted genes regulate the development and behavior of the brain. And its own mutation or changes in the epigenetic modification state of the imprinted control region could lead to some diseases, indicating that imprinted genes and brain development play an important role in diagnosis and prognosis of autism.
This study systematically correlates brain development and genomic imprinting with autism, which provides a new perspective for the study of genetic mechanisms of autism, and selected the potential candidate biomarkers for early diagnosis of autism in clinic.
自闭症是一种涉及环境和遗传因素的复杂疾病。近期研究表明基因组印记与自闭症中的大脑发育存在关联，然而自闭症的发病机制尚不完全清楚。在此，我们使用生物信息学工具对自闭症相关基因、基因组印记基因以及人类大脑在空间和时间上差异表达的基因进行全面分析，旨在探索自闭症、大脑发育与基因组印记之间的关系。
本研究使用滑动窗口和统计方法分析自闭症相关基因与染色体上印记基因的分布相关性。对正常大脑的基因表达微阵列数据进行重新分析，以构建大脑发育过程中基因表达的时空坐标系。最后，我们将自闭症相关基因、印记基因和大脑时空差异表达基因进行交集分析，以进一步找出这些基因所涉及的主要生物学过程。
我们发现自闭症相关基因与印记基因在染色体上的分布呈正相关。通过对正常大脑微阵列数据的分析，我们构建了人类大脑发育过程中基因表达的时空坐标系，并获得了13个在大脑发育过程中差异表达的基因，这些基因既是自闭症相关基因又是印记基因。此外，富集分析表明这些基因主要参与γ-氨基丁酸信号通路、神经元识别、学习或记忆以及突触传递调节等生物学过程。生物信息学分析表明印记基因调节大脑的发育和行为。其自身突变或印记控制区域表观遗传修饰状态的改变可能导致一些疾病，这表明印记基因和大脑发育在自闭症的诊断和预后中起着重要作用。
本研究系统地将大脑发育和基因组印记与自闭症相关联，为自闭症遗传机制的研究提供了新的视角，并在临床上筛选出了用于自闭症早期诊断的潜在候选生物标志物。
