Dataset Information

Integrated multifactor analysis explores core dysfunctional modules in autism spectrum disorder.

ABSTRACT: Autism spectrum disorder (ASD) is a complex neurodevelopmental disease in early childhood, and growing up to be a major cause of disability in children. However, the underlying molecular mechanism of ASD remains elusive. Hence, we represented integrated multifactor analysis exploring dysfunctional modules based on RNA-Seq data from corpus callosum in 6 patients with ASD and 6 normal individuals. According to protein-protein interactions (PPIs) and WGCNA, we performed co-expression modules analysis for ASD-associated genes, and identified 25 modules with differentially expressed genes (DEGs), observing that genes in these modules were significantly involved in various biological processes in nervous system, sensory system, phylogenetic system and variety of signaling pathways. Then, based on transcriptional and post-transcriptional regulations, integrating transcription factor (TF)-target and RNA-associated interactions, significant regulators of co-expression modules were identified as pivot regulators, including 67 pivot TFs, 13 pivot miRNAs and 6 pivot lncRNAs. GO and KEGG pathway enrichment analysis demonstrated that the pivot miRNAs significantly enriched in neural or mental-associated biological progresses. The pivot TFs were mainly involved in various regulation of transcription, immune system and organs development. Finally, our work deciphered a multifactor dysfunctional co-expression subnetwork involved in ASD, helps uncover core dysfunctional modules for this disease and improves our understanding of its underlying molecular mechanism.

SUBMITTER: Huang Y

PROVIDER: S-EPMC6036758 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Integrated multifactor analysis explores core dysfunctional modules in autism spectrum disorder.

Huang Yan Y Chang Zhenghong Z Li Xiaodan X Liang Shuang S Yi Ying Y Wu Lijie L

International journal of biological sciences 20180522 8

Autism spectrum disorder (ASD) is a complex neurodevelopmental disease in early childhood, and growing up to be a major cause of disability in children. However, the underlying molecular mechanism of ASD remains elusive. Hence, we represented integrated multifactor analysis exploring dysfunctional modules based on RNA-Seq data from corpus callosum in 6 patients with ASD and 6 normal individuals. According to protein-protein interactions (PPIs) and WGCNA, we performed co-expression modules analys ...[more]

PMID: 29989084

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Project description:Endoplasmic reticulum (ER) stress has been demonstrated to play important roles in a variety of human diseases. However, their relevance to autism spectrum disorder (ASD) remains largely unknown. Herein, we aimed to investigate the expression patterns and potential roles of the ER stress regulators in ASD. The ASD expression profiles GSE111176 and GSE77103 were compiled from the Gene Expression Omnibus (GEO) database. ER stress score determined by the single sample gene set enrichment analysis (ssGSEA) was significantly higher in ASD patients. Differential analysis revealed that there were 37 ER stress regulators dysregulated in ASD. Based on their expression profile, the random forest and artificial neuron network techniques were applied to build a classifier that can effectively distinguish ASD from control samples among independent datasets. Weighted gene co-expression network analysis (WGCNA) screened out the turquoise module with 774 genes was closely related to the ER stress score. Through the overlapping results of the turquoise module and differential expression ER stress genes, hub regulators were gathered. The TF/miRNA-hub gene interaction networks were created. Furthermore, the consensus clustering algorithm was performed to cluster the ASD patients, and there were two ASD subclusters. Each subcluster has unique expression profiles, biological functions, and immunological characteristics. In ASD subcluster 1, the FAS pathway was more enriched, while subcluster 2 had a higher level of plasma cell infiltration as well as the BCR signaling pathway and interleukin receptor reaction reactivity. Finally, the Connectivity map (CMap) database was used to find prospective compounds that target various ASD subclusters. A total of 136 compounds were significantly enriched. In addition to some specific drugs which can effectively reverse the differential gene expression of each subcluster, we found that the PKC inhibitor BRD-K09991945 that targets Glycogen synthase kinase 3β (GSK3B) might have a therapeutic effect on both ASD subtypes that worth of the experimental validation. Our finding proved that ER stress plays a crucial role in the diversity and complexity of ASD, which may inform both mechanistic and therapeutic assessments of the disorder.

Dataset Information

Integrated multifactor analysis explores core dysfunctional modules in autism spectrum disorder.

Publications

Integrated multifactor analysis explores core dysfunctional modules in autism spectrum disorder.

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