of evidence; the specific genes that are implicated vary across studies [5–13]. To explore specific genes that might be relevant in our dataset, we reviewed the list of probes that were differentially expressed at a log2-fold change of $0.7. Of these 32 genes, there were two genes associated with synapse/neurotransmitters (LIN7B, SYN1), three genes associated with vesicle transport (VPS29, HSPB1, TUBB2B), one gene associated with brain patterning (GPR56), four genes that are of significance for normal brain function based on monogenic neurologic disorders associated with mutations in those genes (ODZ3, ATP1A2, MOCS2, PTS) and another gene with brain-specific expression (BEX5) (Table S3). Eight of these genes have some evidence in support of an association with autism (http://autismkb.cbi.pku.edu.cn/) [32], two of which (SYN1, PTS) met thresholds for a high level of evidence [32], and concordant changes in the differential expression in autism vs control were found for three of the genes (GPR56, HSPB1, BEX5) between this study and the work of others [13,28,33]. These data provide additional support for the hypotheses that genes of synapse formation/function and of cortical development are differentially expressed in autism [20– 24]. Brain Immune Gene Dysregulation in a Subset of Autistic Brains We also separately analyzed the gene expression data of the three autistic cerebellar outlier samples. Differentially expressed genes between these outliers and matched controls were enriched for NF-kB signaling and cell cycle regulation pathways (Figure S2). The top 300 differentially expressed probes were used to generate a non-redundant gene list and compared with two published differentially expressed autism gene lists in brain [13,28] (Table S6). There was a statistically significant overlap in the lists (P = 1.4E-9; Fisher’s exact test), notably including genes associated with inflammation. While the role of neuroinflammation in autism pathophysiology remains unclear [34], our data emphasize the substantial interindividual heterogeneity of brain immune system gene dysregulation. Brain Gene Expression Profiles and Associations with Specific Clinical Phenotypes We next explored associations between phenotype and transcriptional profile with weighted genome co-expression network analysis (WGCNA) [35]. This approach can potentially identify higher order, systems level correlations. We examined potential associations between expression of all gene modules (ie, clusters of co-expressed genes) in cerebella and ADI-R total or ADI-R domain scores, which are associated with phenotypic severity in the three cardinal domains of autism: impairment of social reciprocity, impairment of verbal or non-verbal communi- cation, and stereotyped or repetitive behaviors. Two gene modules were significantly associated with the social interaction domain of the ADI-R and one gene module significantly associated with the stereotyped and repetitive behavior domain of the ADI-R (Figure 2A). These three modules are characterized by gene ontology enrichments relating to purinergic signaling/immune response, inflammation/immune response, and myelin/myelina- tion, respectively (Figure 2B). There were no associations for other covariates tested (Figure 2A). Correlations between autism behavioral endophenotypes and CNS gene expression patterns have not been reported. Several types of data support the observed associations. Altered immune responses and neuroinflammation are well documented in autism [34] and there are reports of specific associations Transcriptomic and Epigenetic Findings in Autism between immune parameters and deficits in social interactions and communication in autistic individuals [14]. Purinergic signaling is involved with synaptic plasticity and neuron/glia interactions [36] and recent genetic and clinical data implicates a role for purines/ purinergic signaling in autism [32]. In addition, abnormalities of brain white matter are commonly observed in diffusion tensor imaging-based neuroradiological studies of autism [37] and abnormalities of cerebellar white matter have been associated with repetitive behaviors in autistic individuals [38]. Determina- tion of the pathophysiology and clinical meanings of these associations will be important next steps. Validation of Gene Expression Microarray Data Quantitative RT-PCR was used to validate the results of the microarray gene expression analyses (Figure S3). We selected candidates based on strong effect size on the gene expression microarray analyses or biological plausibility based on published data. The results of all 5 statistically significant differentially expressed transcripts by RT-PCR were concordant with micro- array data, confirming the validity of transcriptomic analysis by a gene microarray approach. Brain Whole Genome Methylation Analysis To explore a possible epigenetic basis of the gene expression findings, we obtained high quality methylation data for 9 pairs of age- and gender-matched cerebella and 8 pairs of BA19 cortex and analyzed .27,000 probes for differential methylation at individual CpG dinucleotides enriched in promoter regions of genes. Correspondence analysis indicated that region and age were the major sources of variability in the data (Figure 3A) and, after controlling for these covariates, there were no significantly differentially methylated genes in either autistic cerebellar or BA19 cortex (data not shown). We also examined the genomic proportion of methylated loci in cerebellar and BA19 cortex. There was no significant difference between autism cases and controls within each region, but there was a statistically significant difference of the proportion of global DNA methylation between cerebellar and BA19 cortex, consistent with previous analyses of normal brain [39] (Figure 3B). We then used WGCNA to analyze the full DNA methylation dataset; there were no modules associated with the autistic phenotype in either cerebellar or BA19 cortex (data not shown). Methylation Analyses of Candidate Autism Genes Several investigators previously reported epigenetic alterations in RORA and BCL2 in lymphoblasts [40] or UBE3A, MECP2, and OXTR in brain tissue [41–43] from small numbers of individuals with autism. We evaluated each of these genes using the DNA methylation microarray data and did not find differential methylation for any of the multiple probes associated with each of the genes (Table S9). Although CpG dinucleotides within a particular promoter CpG island are generally correlated with each other and with gene regulation, we could not exclude the possibility that some unmeasured CpG dinucleotides within an island we investigated were differentially methylated. We therefore performed bisulfite pyrosequencing of the most relevant regions of the CpG islands; no differentially methylated CpG dinucleotides were noted in MECP2, UBE3A, BCL2, RORA, OXTR or CEBPD (Figure 3C, Figure S4, Table S7). Strengths and Limitations of the Current Work We recognize several limitations to our study. First, the sample size is necessarily relatively small as it is difficult to PLOS ONE | www.plosone.org 5 September 2012 | Volume 7 | Issue 9 | e44736