Gene expression changes in children with autism

Abstract

The objective of this study was to identify gene expression differences in blood differences in children with autism (AU) and autism spectrum disorder (ASD) compared to general population controls. Transcriptional profiles were compared with age- and gender-matched, typically developing children from the general population (GP). The AU group was subdivided based on a history of developmental regression (A-R) or a history of early onset (A-E without regression). Total RNA from blood was processed on human Affymetrix microarrays. Thirty-five children with AU (17 with early onset autism and 18 with autism with regression) and 14 ASD children (who did not meet criteria for AU) were compared to 12 GP children. Unpaired t tests (corrected for multiple comparisons with a false discovery rate of 0.05) detected a number of genes that were regulated more than 1.5-fold for AU versus GP (n=55 genes), for A-E versus GP (n=140 genes), for A-R versus GP (n=20 genes), and for A-R versus A-E (n=494 genes). No genes were significantly regulated for ASD versus GP. There were 11 genes shared between the comparisons of all autism subgroups to GP (AU, A-E, and A-R versus GP) and these genes were all expressed in natural killer cells and many belonged to the KEGG natural killer cytotoxicity pathway (p=0.02). A subset of these genes (n=7) was tested with qRT-PCR and all genes were found to be differentially expressed (p<0.05). We conclude that the gene expression data support emerging evidence for abnormalities in peripheral blood leukocytes in autism that could represent a genetic and/or environmental predisposition to the disorder.

Maternal hospitalization with infection during pregnancy and risk of autism spectrum disorders

“…For example, a number of studies have suggested the involvement of genetic factors influencing immune function in the etiology of ASD (Gregg et al, 2008;Voineagu et al, 2011). It is possible that such genetic involvement may act as a confounder (the genetic factor increases risk of both infection and ASD, resulting in a spurious association between infection and ASD) or as an instrument (the genetic factor increases risk of infection which then increases risk of ASD) or as an effect modifier (the genetic factor amplifies the deleterious effect of infection on risk of ASD) -but the latter two does not change the result that maternal infection is associated with ASD.…”

The Role of the Immune System in Autism Spectrum Disorder

“…Many of these genes showed increased variability in the autism cohort, which suggests a general dysregulation of the autistic immune system and overall heterogeneous activation response. Another 2008 study identified 11 genes that were differentially expressed in children with autism, as well as within the subsets of early onset and developmental regression, as compared with the general population (overexpression in autism samples were confirmed for seven of 11 via qPCR) (Gregg et al, 2008). These genes are heavily overrepresented in the ontological KEGG natural killer cytotoxicity pathway, and are highly expressed in NK and CD8 T cells.…”

“…A significant focus of the postnatal immune imbalance has been on specific immune cell subsets, including helper CD4 + and cytotoxic CD8 + T cells (Garbett et al, 2008;Gregg et al, 2008), which may behave differently in individuals with autism (Gupta et al, 1998). A 1986 study found a reduced number of T cells in individuals with autism, as well as an altered helper to suppressor T-cell ratio (Warren et al, 1986), while a supporting 1990 study suggested CD4 + cell levels were decreased in autism (Yonk et al, 1990).…”

Meta-analysis of differentially expressed genes in autism based on gene expression data

“…The characteristics of studies included in this analysis are listed in Table 1 (Nishimura et al, 2007;Gregg et al, 2008;Hu et al, 2009a,b;Alter et al, 2011;Voineagu et al, 2011;Kuwano et al, 2011;Ginsberg et al, 2012). The data sets for GSE30573 and GSE28521 were examined in the same study, but GSE30573 was RNA -seqdata set, and the data sets we applied were almost microarray data sets except for GSE30573.…”