Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 14 (8), 811-818
eCollection

Integrated Multifactor Analysis Explores Core Dysfunctional Modules in Autism Spectrum Disorder

Affiliations

Integrated Multifactor Analysis Explores Core Dysfunctional Modules in Autism Spectrum Disorder

Yan Huang et al. Int J Biol Sci.

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.

Keywords: Autism spectrum disorder; Co-expression; Core dysfunctional module; Multifactor analysis.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Figure 1
Figure 1
Protein-protein interaction network. Nodes in red represent DEGs and blue represent interactors of these DEGs extracted from STRING.
Figure 2
Figure 2
Visualization of WGCNA results. A. Clustering dendrogram of genes. A total of 31 colors corresponding to 30 modules and a gene set containing genes are not included in any module (grey). X-axis represents gene and y-axis represents the height of the gene tree. B. Heatmap plot of the gene network. The heatmap depicts the Topological Overlap Matrix (TOM) among all genes in the analysis. Light color represents low overlap and progressively darker red color represents higher overlap. Blocks of darker colors along the diagonal are the modules.
Figure 3
Figure 3
Functional enrichment results. A. Enrichment results of pivot TFs. The color depth represents p-value, the number of genes represented by the node size B. Enrichment results of pivot miRNAs. Nodes in blue represent KEGG pathway enrichment results and in purple represent GO functional enrichment results. The size of nodes represents the number of genes.
Figure 4
Figure 4
A. The biological processes enriched by 13 pivot TFs of 4 core modules. B. The transcriptional and post-transcriptional regulation for core modules. Circles in red represent DEGs. Triangles, diamonds and “V” in gray represent significant relationship between pivot regulators and genes in module. And the blue lines are interactions among these pivot regulators. C. DEG-DEG interactions between modules. The size of nodes represents the degree.

Similar articles

See all similar articles

Cited by 1 article

References

    1. Parikshak NN, Swarup V, Belgard TG, Irimia M, Ramaswami G, Gandal MJ. et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature. 2016;540:423–7. - PubMed
    1. Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S. et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature. 2011;474:380–4. - PMC - PubMed
    1. Lord C, Bishop SL. Recent advances in autism research as reflected in DSM-5 criteria for autism spectrum disorder. Annu Rev Clin Psychol. 2015;11:53–70. - PubMed
    1. Huang JY, Tian Y, Wang HJ, Shen H, Wang H, Long S. et al. Functional Genomic Analyses Identify Pathways Dysregulated in Animal Model of Autism. CNS Neurosci Ther. 2016;22:845–53. - PMC - PubMed
    1. Hullinger R, Li M, Wang J, Peng Y, Dowell JA, Bomba-Warczak E. et al. Increased expression of AT-1/SLC33A1 causes an autistic-like phenotype in mice by affecting dendritic branching and spine formation. J Exp Med. 2016;213:1267–84. - PMC - PubMed

Publication types

MeSH terms

Feedback