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Integrated systems analysis reveals a molecular network underlying autism spectrum disorders

Jingjing Li, Minyi Shi, Zhihai Ma, Shuchun Zhao, Ghia Euskirchen, Jennifer Ziskin, Alexander Urban, Joachim Hallmayer, Michael Snyder
DOI 10.15252/msb.20145487 | Published online 30.12.2014
Molecular Systems Biology (2014) 10, 774

Author Affiliations

  1. Jingjing Li1,†,
  2. Minyi Shi1,†,
  3. Zhihai Ma1,†,
  4. Shuchun Zhao2,
  5. Ghia Euskirchen1,
  6. Jennifer Ziskin2,
  7. Alexander Urban3,
  8. Joachim Hallmayer3 and
  9. Michael Snyder*,1
  1. 1Department of Genetics, Stanford Center for Genomics and Personalized Medicine Stanford University School of Medicine, Stanford, CA, USA
  2. 2Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
  3. 3Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
  1. ↵*Corresponding author. Tel: +1 650 736 8099; E‐mail: mpsnyder{at}stanford.edu

  1. ↵† These authors contributed equally to this work

View Abstract

Article Figures & Data

Figures

  • Figure 1.
    Figure 1. A modular protein interaction network with modules containing enrichment of autism‐associated genes

    Two topological modules (#2 and #13) on human protein interaction network showed significant enrichment for autism genes (in red). The topological modules are physical clusters on the network where their member genes intensively interact with each other but sparsely interact with non‐member genes on the network. A zoom‐in view of module #13 is also shown, where known autism genes and genes affected by ASD‐associated de novo CNVs were colorized in red and green, respectively. Genes annotated by both were in blue. The false discovery rate indicates its significant enrichment for the known autism genes.

  • Figure 2.
    Figure 2. Candidate genes from sequencing screens

    1. An overview of the identified loci from whole‐genome and exome sequencing. Evolutionary conservation is quantified by GERP++ score, where the higher scores indicate greater selective pressure on the genomic loci. For genes with multiple significant loci, the most conserved residue is considered. Variants absent in the 1,000 Genome dataset are considered rare variants. The genes were colorized based on the fraction of deleterious mutations predicted by MutationTaster among all the identified mutations in the gene (MRI image of the corpus callosum: Allen Institute of Brain Science).

    2. Validation using another larger patient cohort. In this dataset, variants with allele frequencies with increased absolute differences between cases and controls are more likely to affect genes that were also detected in our study (red line). The allele frequency difference is the absolute difference between cases and controls. This trend cannot be observed by 10,000 simulations (blue line for one randomized dataset).

  • Figure 3.
    Figure 3. Expression analysis of the synaptic module

    1. Dichotomized expression of the genes in module #13 across 295 brain sections. Relative abundance of each gene across the 295 brain sections was hierarchically clustered to reveal gene groups exhibiting similar expression patterns across tissues. Group 1 genes showed elevated expression in 175 regions (T1, e.g., corpus callosum) relative to other brain sections, and Group 2 genes showed high expression in 120 regions (T2, e.g., hippocampal regions) relative to other brain sections.

    2. RNA‐sequencing of four different brain regions from a healthy subject. The brain regions include the Brodmann areas 9 (BA9), 40 (BA40), the amygdala (AMY) and the corpus callosum (CC), which revealed the same observation as from the microarray analyses. Group 1 (red) and 2 (blue) genes were compared with 1,000 randomly sampled genes (gray) from the transcriptome in each brain region. The raw FPKM values were normalized into the cumulative density functions based on kernel density estimation. The elevation of Group 2 genes across all brain regions and the greatest increase of Group 1 genes in the corpus callosum were all statistically significant (P < 1e‐5, Wilcoxon rank‐sum test).

    3. RNA‐sequencing of the corpus callosum transcriptomes from six non‐autistic individuals. FPKM quantifies the absolute expression of genes in each group. The two groups have similar expression in the corpus callosum (P > 0.5, Wilcoxon rank‐sum test), which are all above the transcriptome background (P < 4.87e‐6, Wilcoxon rank‐sum test), suggesting that both sub‐components are active in this tissue.

  • Figure 4.
    Figure 4. Cell‐type expression of module #13 in oligodendrocytes

    1. Immunohistochemistry analysis in the corpus callosum. Staining of LRP2 in the human corpus callosum reveals that the major cell population in the corpus callosum is the oligodendrocytes (the blue round nuclei), which express LRP2 stained in brown. A zoom‐in view is shown in the inset.

    2. Neural cell‐type expression of the orthologous module #13 in mouse brain. Gene expression in different neural cell types was hierarchically clustered into the three major cell types in brain (neurons, oligodendrocytes and astrocytes). The clustering grouped genes in module #13 into a neuron cluster and a glial cluster, enriched for Group 1 and 2 genes, respectively. The fraction of Group 1 (red) and 2 (blue) genes in the glial and neuronal clusters were represented by the pie charts, with statistical significance determined by a chi‐square test.

    3. Overall expression of module #13 in cultured oligodendrocyte precursor cells (OPCs). Group 1 and 2 were expressed at a similar level as the transcriptome background in OPCs. The statistical significance was determined by Wilcoxon rank‐sum test, and the error bars represent one standard error.

    4. The role of the module in oligodendrocyte (OL) development. Differentiation of OPCs into mature myelinating OLs (MOG+) led to a significant up‐regulation of Group 1 genes (left, OPCs → mature OLs). On the other hand, conditional knockout (CKO) of the master myelination factor MRF from mature OLs led to a significant up‐regulation of Group 2 genes (right, mature OLs → MRF CKO). The statistical significance was determined by Wilcoxon rank‐sum test.

    5. A proposed model. Up‐regulation is associated with, or likely to contribute to, the differentiation of OPCs into mature myelinating OLs. The mature OLs acquire their myelination capacity by activating the MRF‐mediated regulatory network, which also serves to repress expression of Group 2 genes.

  • Figure 5.
    Figure 5. Integrative analysis of the genetic alteration in this study

    1. Enrichment of the differentially expressed genes in module #13. RNA‐sequencing of the corpus callosum of autism patients and their matched controls. Enrichment was not observed for the genes in the human synaptome or the collection of known autism genes (excluding genes in this module). Statistical significance was determined by hypergeometric test.

    2. The mutation pattern of the genes from the innermost layers of the interaction network (K ≥ 10) to the periphery layer (K = 1). Genes in the central and periphery layers in this module are more likely to be affected, while the trend cannot be observed in 10,000 random simulations. For individual bins, significant enrichment and depletion were observed in the central layers (K ≥ 10) and the intermediate layers (3 ≤ K < 6), respectively. Statistical significance of the enrichment was determined by hypergeometric test. 10,000 random permutations were performed to determine the statistical significance of the curve.

    3. Compositional bias of the mutated genes in central layers. The mutated genes in central layers are more biased toward the corpus callosum‐specific subcomponent; this trend is not observed in background or other mutated genes with varying degree of K. Statistical significance of the enrichment was determined by hypergeometric test.

    4. Positive correlation between network coreness and gene expression in the corpus callosum. RNA‐sequencing of the corpus callosum of six non‐autistic individuals revealed a positive correlation, suggesting the central layers may play critical roles in the corpus callosum. Two outlier genes, DYNLL1 and BCAS1, are separately labeled due to their extreme expression in this tissue. The correlation coefficient r and its statistical significance were computed using Spearman's correlation.

    5. Predicted sub‐complexes within this module. Genes in this module are topologically clustered to form sub‐complexes, with the significantly mutated genes labeled in blue, mis‐expressed genes in the corpus callosum labeled in green, and both in red. Two clusters, #6 for SHANK‐DLGAP complexes and #6 for LRP2, and its binding partners, are enriched for the mis‐expressed or mutated genes, respectively. Statistical significance of the enrichment was determined by hypergeometric test.

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