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ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder

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 de novo or inherited variants among a variety of multiple molecules. Previous studies have shown t...

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Autores principales: Xiong, Chenchen, Sun, Shaoping, Jiang, Weili, Ma, Lei, Zhang, Junpeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591752/
https://www.ncbi.nlm.nih.gov/pubmed/33173536
http://dx.doi.org/10.3389/fgene.2020.562971
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author Xiong, Chenchen
Sun, Shaoping
Jiang, Weili
Ma, Lei
Zhang, Junpeng
author_facet Xiong, Chenchen
Sun, Shaoping
Jiang, Weili
Ma, Lei
Zhang, Junpeng
author_sort Xiong, Chenchen
collection PubMed
description 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 de novo 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.
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spelling pubmed-75917522020-11-09 ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder Xiong, Chenchen Sun, Shaoping Jiang, Weili Ma, Lei Zhang, Junpeng Front Genet Genetics 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 de novo 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. Frontiers Media S.A. 2020-10-14 /pmc/articles/PMC7591752/ /pubmed/33173536 http://dx.doi.org/10.3389/fgene.2020.562971 Text en Copyright © 2020 Xiong, Sun, Jiang, Ma and Zhang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Xiong, Chenchen
Sun, Shaoping
Jiang, Weili
Ma, Lei
Zhang, Junpeng
ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder
title ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder
title_full ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder
title_fullStr ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder
title_full_unstemmed ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder
title_short ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder
title_sort asdmir: a stepwise method to uncover mirna regulation related to autism spectrum disorder
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591752/
https://www.ncbi.nlm.nih.gov/pubmed/33173536
http://dx.doi.org/10.3389/fgene.2020.562971
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