Age-dependent brain gene expression and copy number anomalies in autism suggest distinct pathological processes at young versus mature ages

Abstract

Autism is a highly heritable neurodevelopmental disorder, yet the genetic underpinnings of the disorder are largely unknown. Aberrant brain overgrowth is a well-replicated observation in the autism literature; but association, linkage, and expression studies have not identified genetic factors that explain this trajectory. Few studies have had sufficient statistical power to investigate whole-genome gene expression and genotypic variation in the autistic brain, especially in regions that display the greatest growth abnormality. Previous functional genomic studies have identified possible alterations in transcript levels of genes related to neurodevelopment and immune function. Thus, there is a need for genetic studies involving key brain regions to replicate these findings and solidify the role of particular functional pathways in autism pathogenesis. We therefore sought to identify abnormal brain gene expression patterns via whole-genome analysis of mRNA levels and copy number variations (CNVs) in autistic and control postmortem brain samples. We focused on prefrontal cortex tissue where excess neuron numbers and cortical overgrowth are pronounced in the majority of autism cases. We found evidence for dysregulation in pathways governing cell number, cortical patterning, and differentiation in young autistic prefrontal cortex. In contrast, adult autistic prefrontal cortex showed dysregulation of signaling and repair pathways. Genes regulating cell cycle also exhibited autism-specific CNVs in DNA derived from prefrontal cortex, and these genes were significantly associated with autism in genome-wide association study datasets. Our results suggest that CNVs and age-dependent gene expression changes in autism may reflect distinct pathological processes in the developing versus the mature autistic prefrontal cortex. Our results raise the hypothesis that genetic dysregulation in the developing brain leads to abnormal regional patterning, excess prefrontal neurons, cortical overgrowth, and neural dysfunction in autism.

Figure 1. Dysregulated gene expression in developmental M-Pathways and Process Networks in young autistic prefrontal cortex.

From differentially expressed genes of young autistic vs. young control cases (Table S2), networks were created using MetaCore Network Analysis. Colors on fold change graph correspond to circles on the right depicting network maps in each category. Yellow = differentially expressed genes in two or more functional domains. Overlapping circles = differentially expressed genes common to two domains.

Figure 2. Dysregulated gene expression in top three Map Folders in adult autistic prefrontal cortex and differentially affected M-Pathways of young and adult autistic cases.

(A) Graph on the right shows fold change of genes in cell differentiation, mitogenic signaling, and apoptosis and survival Map Folders. Colors on fold change graph correspond to circles on the left depicting network maps in each category. From each category of differentially expressed genes in adult autistic vs. adult control posthoc comparison (Table S4), gene networks on the left were created using MetaCore Network Analysis. (B) Top differentially affected M-pathway comparisons of dysregulated genes between adult and young autistic cases. Bars represent significance of listed pathways. Orange = adult; blue = young; faded color = FDR>0.1 or p>0.05.

Figure 3. Dysregulated gene expression in top two MetaCore Map Folders in autism independent of age.

Graph on left shows fold change of genes in DNA damage response and apoptosis and survival Map Folders. The third most significant map folder is depicted in Figure S3. Colors on fold change graph correspond to circles on the left depicting network maps in each category. From each category of differentially expressed genes in the all autistic vs. all control comparison (Table S5), networks were created using MetaCore Network Analysis.

Figure 4. MetaCore process networks and network maps in autism based on gene deletions located in CNVs from DLPFC, and genetic association results.

Autistic cases had significant enrichment of MetaCore process networks in genes contained within total CNVs (A) and filtered CNVs (i.e., not present in the Database of Genomic Variants) using PennCNV (B) and CNVision (C). Blue bars = controls; red bars = autistic cases. (D) MetaCore network analysis of the top three enriched MetaCore process networks in (B). Symbols in (D) represent gene types or associations. Genes with blue circles were identified autistic CNVs; genes without circles were summoned by the database to complete network. Pink lines = canonical pathway connections. (E) Gene sets tested in set-based association analysis using PLINK in Broad/JHMI and CHOP datasets, number of genes in each set, database from which genes were taken and p-values of associations.