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. 2011 May 25;474(7351):380-4.
doi: 10.1038/nature10110.

Transcriptomic analysis of autistic brain reveals convergent molecular pathology

Irina Voineagu  1 , Xinchen Wang, Patrick Johnston, Jennifer K Lowe, Yuan Tian, Steve Horvath, Jonathan Mill, Rita M Cantor, Benjamin J Blencowe, Daniel H Geschwind
Affiliations

Transcriptomic analysis of autistic brain reveals convergent molecular pathology

Irina Voineagu et al. Nature. .

Abstract

Autism spectrum disorder (ASD) is a common, highly heritable neurodevelopmental condition characterized by marked genetic heterogeneity. Thus, a fundamental question is whether autism represents an aetiologically heterogeneous disorder in which the myriad genetic or environmental risk factors perturb common underlying molecular pathways in the brain. Here, we demonstrate consistent differences in transcriptome organization between autistic and normal brain by gene co-expression network analysis. Remarkably, regional patterns of gene expression that typically distinguish frontal and temporal cortex are significantly attenuated in the ASD brain, suggesting abnormalities in cortical patterning. We further identify discrete modules of co-expressed genes associated with autism: a neuronal module enriched for known autism susceptibility genes, including the neuronal specific splicing factor A2BP1 (also known as FOX1), and a module enriched for immune genes and glial markers. Using high-throughput RNA sequencing we demonstrate dysregulated splicing of A2BP1-dependent alternative exons in the ASD brain. Moreover, using a published autism genome-wide association study (GWAS) data set, we show that the neuronal module is enriched for genetically associated variants, providing independent support for the causal involvement of these genes in autism. In contrast, the immune-glial module showed no enrichment for autism GWAS signals, indicating a non-genetic aetiology for this process. Collectively, our results provide strong evidence for convergent molecular abnormalities in ASD, and implicate transcriptional and splicing dysregulation as underlying mechanisms of neuronal dysfunction in this disorder.

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Figures

Figure 1
Figure 1. Gene expression changes in autism cerebral cortex
A. Heatmap of top 200 genes differentially expressed between autism and control cortex samples. Scaled expression values are color-coded according to the legend on the left. The dendrogram depicts hierarchical clustering based on the top 200 DE genes. The top bar (A/C) indicates the disease status: red-autism, black-control. The bottom bars show additional variables for each sample: sex (grey-male, black-female), brain area (black-temporal, grey-frontal), co-morbidity of seizures (green-autism case with seizure disorder, red-autism case without seizure disorder, black-control), age, RNA integrity number (RIN) and post mortem interval (PMI). The corresponding scale for quantitative variables is shown on the left. B. Top: Venn diagram depicting the overlap between genes DE in frontal and temporal cortex. Bottom: Venn diagram describing the overlap between genes DE in the initial cohort (DS1) and the replication cohort (DS2). Differential expression in the initial cohort was assessed at an FDR<0.05 and fold changes>1.3. The statistical criteria were relaxed to p<0.05 for the replication dataset since it involved fewer samples. C. Expression fold changes for all genes DE in the initial cohort are plotted on the x-axis against the fold changes for the same genes in the replication cohort on the y-axis. Green-genes downregulated in the autism group in both datasets, red-genes upregulated in the autism group in both datasets, grey-genes with opposite direction of variation in the two datasets. Horizontal lines show fold change threshold for significance. D. (left) Diagram depicting the number of genes showing significant expression differences between frontal and temporal cortex in control samples (top) and autism samples (bottom) at FDR<0.05. (right) Top 20 genes differentially expressed between frontal and temporal cortex in control samples. All of the genes shown are also differentially expressed between frontal and temporal cortex in fetal midgestation brain, but show no significant expression differences between frontal and temporal cortex in autism. The horizontal bars depict p values for differential expression between frontal and temporal cortex in the autism and control groups.
Figure 2
Figure 2. Gene co-expression modules associated with autism
A, D Top-heatmap of genes belonging to the co-expression module. Bottom-corresponding module eigengene values (y-axis) across samples (x-axis) . Red-autism, grey-controls. B, E Visualization of the M12 and M16 modules respectively. The top 150 connections are shown for the each module. Genes with the highest correlation with the module eigengene value (i.e intramodular hubs) are shown in larger size. C, F Relevant gene ontology categories enriched in the M12 and M16 modules
Figure 3
Figure 3. A2BP1-dependent differential splicing events
A. Top A2BP1-specific differential splicing events. DS events showing the most significant differences in alternative splicing between low-A2BP1 autism cases and controls as well as DS differences consistent with the A2BP1 binding site position. The horizontal axis depicts the percentage of transcripts including the alternative exon. Red-autism samples, black-control samples. B. Relevant gene ontology categories enriched in the set of genes containing exons differentially spliced between low-A2BP1 autism cases and controls.
Figure 4
Figure 4. GWAS set enrichment analysis
A. GWAS set enrichment analysis using the discovery AGRE cohort from Wang et al. For each gene set (DE genes, M12 and M16) the null distribution of the enrichment score generated by 10000 random permutations is shown (x-axis) and the enrichment score for the gene set is depicted by a red vertical line. A p value < 0.01 was considered significant to correct for multiple comparisons. B. GWAS signal enrichment of DE genes and the autism-associated co-expression modules M12 and M16. Enrichment p values are shown for an autism GWAS dataset (Wang et al., AGRE discovery cohort) as well as two control datasets consisting of GWAS studies of non-psychiatric traits: Han et al. (Negative control 1) and Cooper et al. (Negative control 2). The red line marks the p value threshold for significance.
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