Xiong Chenchen, Sun Shaoping, Jiang Weili, Ma Lei, Zhang Junpeng
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.
Department of Medical Engineering, People's Hospital of Yuxi City, Yuxi, China.
Front Genet. 2020 Oct 14;11:562971. doi: 10.3389/fgene.2020.562971. eCollection 2020.
Autism spectrum disorder (ASD) is a class of neurodevelopmental disorders characterized by genetic and environmental risk factors. The pathogenesis of ASD has a strong genetic basis, consisting of rare or inherited variants among a variety of multiple molecules. Previous studies have shown that microRNAs (miRNAs) are involved in neurogenesis and brain development and are closely associated with the pathogenesis of ASD. However, the regulatory mechanisms of miRNAs in ASD are largely unclear. In this work, we present a stepwise method, ASDmiR, for the identification of underlying pathogenic genes, networks, and modules associated with ASD. First, we conduct a comparison study on 12 miRNA target prediction methods by using the matched miRNA, lncRNA, and mRNA expression data in ASD. In terms of the number of experimentally confirmed miRNA-target interactions predicted by each method, we choose the best method for identifying miRNA-target regulatory network. Based on the miRNA-target interaction network identified by the best method, we further infer miRNA-target regulatory bicliques or modules. In addition, by integrating high-confidence miRNA-target interactions and gene expression data, we identify three types of networks, including lncRNA-lncRNA, lncRNA-mRNA, and mRNA-mRNA related miRNA sponge interaction networks. To reveal the community of miRNA sponges, we further infer miRNA sponge modules from the identified miRNA sponge interaction network. Functional analysis results show that the identified hub genes, as well as miRNA-associated networks and modules, are closely linked with ASD. ASDmiR is freely available at https://github.com/chenchenxiong/ASDmiR.
自闭症谱系障碍(ASD)是一类由遗传和环境风险因素所导致的神经发育障碍。ASD的发病机制具有强大的遗传基础,由多种分子中的罕见或遗传变异组成。先前的研究表明,微小RNA(miRNA)参与神经发生和大脑发育,并且与ASD的发病机制密切相关。然而,miRNA在ASD中的调控机制在很大程度上尚不清楚。在这项工作中,我们提出了一种逐步的方法ASDmiR,用于识别与ASD相关的潜在致病基因、网络和模块。首先,我们利用ASD中匹配的miRNA、长链非编码RNA(lncRNA)和mRNA表达数据,对12种miRNA靶标预测方法进行了比较研究。根据每种方法预测的实验证实的miRNA-靶标相互作用的数量,我们选择了识别miRNA-靶标调控网络的最佳方法。基于最佳方法识别出的miRNA-靶标相互作用网络,我们进一步推断miRNA-靶标调控双分子团或模块。此外,通过整合高可信度的miRNA-靶标相互作用和基因表达数据,我们识别出三种类型的网络,包括lncRNA-lncRNA、lncRNA-mRNA和mRNA-mRNA相关的miRNA海绵相互作用网络。为了揭示miRNA海绵的群落,我们从识别出的miRNA海绵相互作用网络中进一步推断miRNA海绵模块。功能分析结果表明,识别出的枢纽基因以及与miRNA相关的网络和模块与ASD密切相关。ASDmiR可在https://github.com/chenchenxiong/ASDmiR上免费获取。