Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism (Dec 2014)

Weighted gene correlation network analysis (WGCNA) identifies 12 co-expression modulesAbstract

Recent studies of genomic variation associated with autism have suggested the existence of extreme heterogeneity. Large-scale transcriptomics should complement these results to identify core molecular pathways underlying autism. Here we report results from a large-scale RNA sequencing effort, utilizing region-matched autism and control brains to identify neuronal and microglial genes robustly dysregulated in autism cortical brain. Remarkably, we note that a gene expression module corresponding to M2-activation states in microglia is negatively correlated with a differentially expressed neuronal module, implicating dysregulated microglial responses in concert with altered neuronal activity-dependent genes in autism brains. These observations provide pathways and candidate genes that highlight the interplay between innate immunity and neuronal activity in the aetiology of autism.


We provide transcriptomic evidence for type I interferon and M2-activation state abnormalities in autism that may lead to a variety of pathologic and phenotypic consequences. We further note that there is a strong negative correlation between two differentially co-expressed modules, mod5 (activated M2-state microglia genes) and mod1 (synaptic transmission genes; r=−0.92). Recently, microglia have been identified as cells capable of restoring neural function in the ASD-model MECP2 knockout mice37. We observe, for the first time, that M2-activation state microglia genes, in particular, are altered in autism, potentially driven by type I interferon responses. This process may drive changes in neural progenitor cell proliferation and connectivity with resultant altered activity-dependent neural expression profiles in post-natal development38, 39. The linkage of this pathway to autism may lead to more accurate and predictive models of idiopathic disease that might contribute to the identification of effective therapeutic approaches.

Paper at Nature Communications