Adenosine-to-inosine (A-to-I) RNA editing is a neurodevelopmentally regulated epigenetic modification shown to modulate complex behavior in animals. Little is known about human A-to-I editing, but it is thought to constitute one of many molecular mechanisms connecting environmental stimuli and behavioral outputs. Thus, comprehensive exploration of A-to-I RNA editing in human brains may shed light on gene–environment interactions underlying complex behavior in health and disease. Synaptic function is a main target of A-to-I editing, which can selectively recode key amino acids in synaptic genes, directly altering synaptic strength and duration in response to environmental signals. Here, we performed a high-resolution survey of synaptic A-to-I RNA editing in a human population, and examined how it varies in autism, a neurodevelopmental disorder in which synaptic abnormalities are a common finding. Using ultra-deep (>1000 × ) sequencing, we quantified the levels of A-to-I editing of 10 synaptic genes in postmortem cerebella from 14 neurotypical and 11 autistic individuals. A high dynamic range of editing levels was detected across individuals and editing sites, from 99.6% to below detection limits. In most sites, the extreme ends of the population editing distributions were individuals with autism. Editing was correlated with isoform usage, clusters of correlated sites were identified, and differential editing patterns examined. Finally, a dysfunctional form of the editing enzyme adenosine deaminase acting on RNA B1 was found more commonly in postmortem cerebella from individuals with autism. These results provide a population-level, high-resolution view of A-to-I RNA editing in human cerebella and suggest that A-to-I editing of synaptic genes may be informative for assessing the epigenetic risk for autism.
Site-specific adenosine-to-inosine (A-to-I) RNA base conversions, carried out by adenosine deaminase acting on RNA (ADAR) enzymes, exhibit precise regional specificity in the brain and modulate complex behavior in model organisms.1, 2, 3 A-to-I RNA editing is an efficient means to increase RNA complexity, thereby fine-tuning both gene function and dosage.4, 5, 6 The cellular machinery recognizes inosine as guanosine, so A-to-I editing of codons and splicing signals directly modifies protein-coding gene function,6, 7, 8, 9, 10, 11 whereas editing of microRNAs12, 13, 14 and their binding sites15 alters gene expression. This is particularly important in the human brain, the single most complex organ in cellular diversity, connectivity, morphogenesis and responses to environmental stimuli.16
Synaptic function is a major target of A-to-I editing,17 which can fine-tune neurophysiological properties in response to environmental stimuli.18, 19 Canonical signaling pathways acting on the editing enzymes link A-to-I RNA editing to environmental cues: ADARB1 function requires inositol hexakisphosphate,20 and the expression of ADAR is interferon-inducible.21 Several recoding events that directly alter synaptic strength or duration in response to environmental signals have been characterized in rodents.7, 8, 9, 10, 11 For example, mRNAs of the serotonin receptor HTR2C undergo editing in five sites, which markedly alters its G-protein-coupling activity, and hence the relationship between serotonin levels and postsynaptic signal transduction.2, 11 This editing is regulated by exposure to acute stress and chronic treatment with antidepressants.22 Another example is the neurodevelopmentally regulated editing of transcripts encoding the AMPA receptors GRIA2, GRIA3 and GRIA4,23 where arginine to glycine (R/G) recoding of the ligand-binding domains leads to faster desensitization recovery.7 Moreover, glutamine to arginine (Q/R) editing of the transmembrane domains in the kainate receptors GRIK1 and GRIK2 reduces their calcium permeability,8 with varying degrees of editing throughout mouse neurodevelopment.23 Since 0–100% of mRNA molecules can be edited at any given point,24 Q/R and R/G editing of ionotropic glutamate receptors provides an efficient means for fine-tuning the glutamatergic synapse in response to the changing environment.
Little is known about A-to-I RNA editing in humans, but it has been postulated to be one of the molecular mechanisms connecting environmental inputs and behavioral outputs.18, 25 The increased editing in humans26 as compared with other animals,27 including nonhuman primates,28 has been proposed to generate molecular complexity that might constitute the basis of higher-order cognition.18 Therefore, characterizing A-to-I editing in typically and atypically developed individuals may shed light on environment-dependent epigenetic mechanisms central to human neurodevelopment.
Autism spectrum disorders (ASD; MIM 209850) are highly heritable common neurodevelopmental disorders of complex genetic etiology, characterized by deficits in reciprocal social interaction and repetitive behaviors.29 Several studies characterize synaptic abnormalities in ASD.29, 30, 31, 32 However, the mechanisms for gene–environment interactions and their contribution to the observed synaptic alterations remain unknown. The number of candidate genes is rapidly increasing33, 34, 35 and a main challenge is to identify the context in which they confer risk to ASD. Recent twin studies suggest that the contribution of environmental factors to ASD is larger than previously thought, with lower monozygotic concordance estimates (77–88%), and a surprisingly high dizygotic concordance of 31% (as compared with a sibling recurrence rate of 19%).36, 37 Hence, the identification of mechanisms governing gene–environment interactions relevant to ASD could be informative for risk assessment.38 A-to-I RNA editing is potentially one such mechanism, linking environmental stimuli with synaptic transmission.
Several lines of evidence support an examination of the link between A-to-I editing and ASD. First, model organisms with altered A-to-I editing exhibit maladaptive behaviors characteristic of ASD,2, 39 sometimes with seizures1, 40 or Prader–Willi-like symptoms,41 both of which are typically detected in 25% of children with ASD.42, 43 Second, altered editing of the serotonin receptor HTR2C has been detected in a mouse model of autism44 and in disorders that aggregate in families with ASD,45 including schizophrenia46 and major depression.47 Third, a fly model of Fragile X syndrome, the most common single gene cause of ASD,48 was recently shown to exhibit significant editing alterations, via a direct interaction between the Fragile X gene dFMR1 and the editing enzyme dADAR.49 Finally, independent genomic studies have implicated variants in synaptic genes, the most edited type of genes,17 as a recurring theme in ASD.29, 30, 31, 32
Here, we focus on neurodevelopmentally regulated A-to-I editing that directly alters synaptic function. We precisely quantify and compare the levels of editing across individuals, and characterize the distinct editing landscapes of 10 synaptic genes acting in the human cerebellum, contrasting postmortem brains from typically developed individuals with brains isolated from individuals with ASD. We then (i) specifically examine editing patterns in the glutamatergic and serotonergic systems, (ii) correlate editing with isoform usage and (iii) identify clusters of correlated sites. Importantly, we find that the relative usage of a dysfunctional form of the editing enzyme ADARB1 is significantly higher in ASD.
Materials and Methods
In all, 30 fresh frozen cerebellar samples of deidentified individuals with non-syndromic autism and carefully matched neurotypical individuals were obtained from the National Institute of Child Health and Human Development Brain and Tissue Bank and the Harvard Brain Tissue Resource Center, through the Autism Tissue Program. To minimize confounding factors, matching was based on age, gender, race and postmortem interval. Supplementary Table 1 details the 25 samples that passed quality control measures.
Selection of target genes
With the accumulation of multidisciplinary studies highlighting synaptic alterations in ASD,29, 31, 32, 34, 50, 51, 52, 53, 54 we chose to focus on neurodevelopmentally regulated A-to-I recoding that alters synaptic function. All synaptic genes shown to undergo developmentally dependent A-to-I recoding by Wahlstedt et al.23 were included in this study (Supplementary Table 2).
RNA isolation and quality assurance: Tissue samples were disrupted and RNA isolated in triplicate (mirVana™ miRNA Isolation Kit, Life Technologies, Grand Island, NY, USA), followed by DNase I treatment (DNA-free™, Life Technologies). Samples that seemed intact on 1% agarose gels were quantified on the ND-1000 Spectrophotometer (NanoDrop, Wilmington, DE, USA) and their integrity analyzed using the Agilent 2100 Bioanalyzer Eukaryote Total RNA Nano Series II (Agilent Technologies, Santa Clara, CA, USA). Only samples with RNA integrity number >7 were included in the study, minimizing postmortem degradation artifacts.55 The mean RNA integrity number of the samples used was 8.0 (Supplementary Table 1).
454 cDNA library preparation: A stringent PCR-based library preparation protocol was designed to ensure unbiased templates for multiplexed 454 sequencing, as detailed in Supplementary Information and Supplementary Figure 1.
454 sequencing: Bidirectional GS FLX sequencing was performed as described56 by the 454 Life Sciences Sequencing Center (Branford, CT, USA). ASD and neurotypical pools were sequenced on opposite sides of a two-region PicoTiterPlate. 457 104 reads were obtained, containing 85 709 299 high quality bases (Supplementary Figure 2).
DNA isolation: About 2 g of the same cerebellar tissue samples were disrupted with a mortar and pestle on dry ice, and genomic DNA (gDNA) was extracted using the QIAamp DNA Mini Kit (Qiagen, Valencia, CA, USA).
gDNA genotyping: Sequenom iPLEX genotyping (Sequenom, San Diego, CA, USA) was done at the Boston Children’s Hospital’s SNP Genotyping Facility. In all, 25 single nucleotide polymorphisms were genotyped in four populations. Every sample was genotyped in triplicate, and all triplicates had to agree on the genotype to be considered successful. All passing single nucleotide polymorphisms had a genotype success rate of >96.7%.
Method validation: cDNA and gDNA from all samples were independently analyzed by parallel capture and Illumina sequencing (San Diego, CA, USA), as detailed in Supplementary Information. On average, 3420 cDNA reads and 627 gDNA reads were obtained for every sample at each site tested. The results of the two editing detection methods were tightly correlated, with an average Pearson’s r of 0.923 (mean P=0.002) across six sites. See Supplementary Table 4 for details.
Relative ADARB1 isoform usage analysis: Semiquantitative reverse transcription-PCR and Sanger sequencing were used to measure the relative frequencies of the normal and dysfunctional (NR_027672) ADARB1 isoforms, as detailed in Supplementary Information. Custom TaqMan® Gene Expression Assays (Life Technologies) were used to quantitate the abundance of the dysfunctional ADARB1 isoform relative to GAPDH, as detailed in Supplementary Information.
Correlation between editing and splicing of FLNA: When we amplified across the FLNA Q/R site using 1 primer in exon 43 and 1 in exon 44, an unexpected 720-bp band of varying brightness appeared in all samples. Bidirectional Sanger sequencing revealed that it is a result of intron 43 retention. Its relative abundance was quantitated using Qiagen’s QIAxcel electrophoresis system, and correlated with Q/R editing levels detected by 454 sequencing.
Gene expression profiling: Affymetrix Exon 1.0 ST arrays (Santa Clara, CA, USA) were used to measure global gene expression of each RNA sample. Following quantile normalization, gene expression levels were calculated using the Probe Log Iterative ER algorithm and differences in gene core expression were determined using the Mann–Whitney U-test.
RNA-seq: 10 of the surveyed samples were subject to high-throughput RNA sequencing on Applied Biosystem’s SOLiD platform (Life Technologies). Poly(A)+ RNA was captured (Oligotex mRNA Mini Kit, Qiagen), heat-fragmented (95 °C for 20 min) and prepared for sequencing (Small RNA Expression Kit, Life Technologies) that yielded 114 M reads. Life Technologies’ WT Analysis software package was used to align the reads to the reference genome and expressed sequence tags, identifying 33.4 M uniquely mapped reads.
454 variant calling and quantification: The 454 GS Amplicon Variant Analyzer software (454 Life Sciences) was used to deconvolute samples, align reads to reference sequences, and call variants and haplotypes based on bidirectional sequence changes from the reference.
Beta-binomial modeling of editing levels: For each sample in each site, the posterior editing density, (editing), was a beta distribution with parameters α=1+number of reads with G mapped to the site, and β=1+number of reads with A mapped to the site (Supplementary Figure 3).
Differential editing analyses: Kolmogorov–Smirnov tests were used to assess the significance of differences in continuous measurements, such as those of editing levels between neurotypical individuals and individuals with ASD. To compare discrete distributions, such as the count of GRIK2 or HTR2C isoforms resulting from combinatorial RNA editing, Pearson’s χ2 test was used. The total number of reads belonging to each of the possible editing isoforms were summed across each group and only isoforms supported by more than five reads in both groups were included in the analysis (following this test’s assumptions).
Correlation between dysfunctional ADARB1 Isoform frequency and overall editing levels: For each sample i, RFi is the relative frequency of the dysfunctional ADARB1 isoform. A sample’s overall editing level, Ei, is the sum of its standardized editing levels across all sites, ∑Zij, where Zij is the standardized editing level of sample i at site j. Pearson’s correlation was calculated between these two metrics, RF and E, across all samples, to measure their linear dependence.
Association between editing and splicing of AMPA receptors: Two-by-two contingency tables were used to summarize the relationships between isoform selection and editing (Supplementary Table 5), and Fisher’s exact test was used to determine the significance of the association between them.
Correlations among editing sites: The Pearson correlation coefficient was calculated to quantify the linear relationships between editing at different sites, among all individuals. Biclustering was then used to identify modules of tightly correlated sites, with the EXPANDER software.57
Independence of editing at neighboring sites: To summarize the relationships between editing at GRIA2 Q/R and Q/R+4 among all individuals, a two-by-two contingency table is shown in Supplementary Table 5. Fisher’s exact test was used for power and significance calculations.
Multiple testing correction: All P-values were Benjamini–Hochberg corrected for multiple testing58 to ensure that the false discovery rate of this entire study is <0.05.
Precise multiplex quantitation of A-to-I RNA editing in human cerebella
For a high-resolution view of A-to-I RNA editing in a human brain population, ultra-deep (>1000 × ) 454 cDNA sequencing and gDNA genotyping were used to detect A/G mixtures on cDNA mapping to homozygous genomic A/A (Supplementary Figure 1). This study focused on all 10 synaptic genes shown in mouse to undergo neurodevelopmentally regulated A-to-I recoding23 that results in well-characterized neurophysiological alterations7, 8, 9, 59, 60, 61 (Supplementary Table 2). Editing was measured in postmortem cerebellum, one of several brain regions implicated in ASD62 by both imaging63 and autopsy64 studies. Cerebellar tissue samples were obtained from neurotypical individuals and individuals with non-syndromic ASD, matched by gender, age, race and postmortem interval (Supplementary Table 1). Pooled, barcoded, bidirectional 454 cDNA sequencing yielded on average 1344 reads of 212 bp per individual per amplicon (Supplementary Figure 2). gDNA was genotyped at 18 well-characterized editing sites with known functional consequences and 7 positions aligned to a mixture of guanosines and adenosines on cDNA. A-to-I edited sites were identified by the presence of a bidirectional cDNA A/G mixture at homozygous A/A gDNA positions. Editing levels were modeled by a beta-binomial distribution, resulting in a posterior editing density for each individual at each site. This model considers both the fraction of edited reads and the total number of reads covering a site, to produce a distribution describing the level of editing and our confidence in that measurement (Supplementary Figure 3).
This approach provided a high-confidence data set of synaptic A-to-I RNA editing from individuals with ASD and matched neurotypical individuals, with an average 95% confidence interval (CI) length of 0.038. For independent validation, the same cDNA and gDNA were analyzed by parallel capture and >3000 × Illumina sequencing in six sites among all individuals (Supplementary Information). A tight correlation between the two editing detection methods is shown in Supplementary Figure 4 and Supplementary Table 4, with an average correlation coefficient of 0.923.
High dynamic range of editing across sites and individuals
The editing levels of 25 sites were robustly measured and found to range from 2.5 to 99.6% (Figure 1a and Supplementary Figure 5). Unexpectedly broad distributions of editing levels were found across neurotypical individuals and carefully matched individuals with ASD in those sites previously shown to be neurodevelopmentally regulated in mice23 (average coefficient of variation (CV) 54.8%). Only one site, GRIA2 Q/R, showed similar levels of editing among all individuals (CV=0.5%), consistent with reports that editing of this site is essential and unchanged throughout development.23, 65 No relationships between editing levels and age, gender, race, postmortem interval or RNA integrity were detected (Supplementary Figure 6).
By inspection of the individual posterior editing densities across all sites (Supplementary Figure 5), we noticed that the extremes of most editing distributions are individuals with ASD. To quantify this observation, the point estimates of the editing levels of each individual at each site were transformed to Z scores and those 2 or −2 were considered extreme, representing editing levels that are at least two s.d. away from the mean. In 20 of 25 sites, individuals with ASD were at the extreme of the spectrum of editing seen for that site (Figure 1b). Having extreme synaptic editing levels is highly informative of ASD, with a positive predictive value p(ASD||Zediting|>2) of 0.78. Outliers at different sites are different individuals with ASD (Supplementary Figures 5 and 7), and editing of more than three s.d. away from the mean was specific to individuals with ASD (Supplementary Figure 7). The overall editing variance in ASD was more than twofold that of neurotypical individuals (ASD variance=0.58, median neurotypical variance of equally sized subsamples=0.24, Brown–Forsythe P=5.6e−3). The major contributors to this increased variance are 30% of the individuals with ASD (Supplementary Figure 8).
Patterns of editing in the glutamatergic and serotonergic systems
Next, editing patterns were specifically examined in the glutamatergic and serotonergic systems, which have been shown to be implicated in ASD.29, 66 GRIK2, whose genomic locus has been repeatedly linked and associated with ASD,66 is edited in three sites, leading to the formation of eight protein isoforms that differ in their calcium permeability.23 Differential GRIK2 editing between individuals with ASD and neurotypical individuals is depicted in Figure 2, both on a single site and isoform-wide levels. Kolmogorov–Smirnov tests were used to assess the significance of differences in continuous measurements, such as those of editing levels between neurotypical individuals and individuals with ASD. To compare discrete distributions, such as the count of GRIK2 or HTR2C isoforms resulting from combinatorial RNA editing, Pearson’s χ2 test was used. The relative frequencies of GRIK2 isoforms were significantly different between individuals with ASD and neurotypical individuals (P<1e−4, χ2 test). Differences were also detected in the editing of GRIA4 R/G (P<3.6e−2, Kolmogorov–Smirnov test, Supplementary Figure 9). Other comparisons of glutamate receptors did not reach statistical significance, as this study is not powered to detect small effect sizes (Supplementary Figure 10).
Editing was also examined in the serotonin receptor HTR2C, which is targeted by the only Food and Drug Administration-approved drugs to treat autistic symptoms, risperidone and aripiprazole. This receptor undergoes RNA editing at five sites, which markedly alters its G-protein-coupling activity. HTR2C editing may create up to 24 different protein isoforms, characterized by distinct molecular and behavioral phenotypes.2, 6 Comparing the relative isoform frequencies of individuals with ASD and neurotypical individuals revealed significant differences (P<1e−4, χ2), the strongest in IDV, MNV and ISI isoforms—all underrepresented in ASD (Figure 3). However, the variance within ASD is much larger than that between neurotypical and ASD individuals, suggesting that this finding should be considered with caution and revisited with a larger sample size.
Differential relative ADARB1 isoform usage in individuals with ASD
The sites examined here are predominantly edited by ADARB1 (Supplementary Table 2). To explore the regulatory basis of the observed editing differences, ADARB1 expression and relative isoform usage were examined. In rodents, the function of ADARB1 has been shown to be developmentally regulated via alternative splicing that creates an inactive protein, while its overall expression remains fairly constant throughout development.23, 67 In humans, a dysfunctional ADARB1 isoform (ADAR2g, NR_027672), resulting from alternative skipping of the exon harboring two double-stranded RNA-binding domains, has been found to constitute about 20% of adult cerebellar ADARB1 mRNA.68 As such, the presence of this double-stranded RNA-binding domains-encoding exon was assayed by semi-quantitative reverse transcription-PCR in all samples. Although individuals with ASD and neurotypical individuals showed similar levels of overall ADARB1 expression (Supplementary Figure 11), the relative usage of the inactive ADARB1 isoform was significantly more common in individuals with ASD (Figure 4 and Supplementary Figure 12, P=3.0e−3, Mann–Whitney U-test). Exon skipping was confirmed by bidirectional sequencing in all samples. In all, 10 of the surveyed samples were also subject to RNA-seq, and the relative ADARB1 exon usage determined by RNA-seq correlated with the semi-quantitative reverse transcription-PCR results (Supplementary Information). In addition, an increased relative usage of the dysfunctional ADARB1 isoform in ASD was observed in an independent cohort of nine cerebellar tissue samples (Supplementary Information). The relative frequency of the dysfunctional isoform is correlated with overall editing levels (Pearson’s r=0.48, P=3.2e−2), as measured by the sum of the standardized editing scores across all sites. Finally, the abundance of the inactive ADARB1 isoform was quantitated in all samples using TaqMan® Gene Expression Assays (Supplementary Information). The dysfunctional form was overexpressed in individuals with ASD (P=7.3e−3, Mann–Whitney U-test), with a significant correlation between semi-quantitative and quantitative PCR results (Pearson’s P=2.0e−5).
Tight relationships between recoding and splicing in three AMPA receptors and filamin-A
The long 454 reads (Supplementary Figure 2) were used to study regulatory characteristics of human RNA editing, including the previously hypothesized6, 69 relationships between recoding and alternative splicing selection in three AMPA receptors (GRIA2, GRIA3 and GRIA4) and filamin-A (FLNA). GRIA2, GRIA3 and GRIA4 each contain two mutually exclusive exons that modulate desensitization kinetics, termed flip and flop.7 Editing at the 3′ end of the exon immediately preceding the flip/flop module was strongly associated with the flop isoform in GRIA4 (odds ratio (OR)=101.9 (95% CI 87.8–118.2)), GRIA3 (OR=30.6 (26.8–34.8)) and to a lesser extent GRIA2 (OR=2.6 (2.4–2.8); all P-values <1e−300, Fisher’s exact test, Figure 5a and Supplementary Table 5). Consistent with the detection of differential editing at GRIA4 R/G between individuals with ASD and neurotypical individuals, differential GRIA4 isoform usage was also identified (P<3.6e−2, Kolmogorov–Smirnov test, Supplementary Figure 13).
Another relationship between recoding and splicing was examined in FLNA, where editing at the 3′ end of exon 43 was found to be significantly correlated with intron 43 retention (Pearson’s P<6e−3, Supplementary Figure 14). This intron retention introduces a frameshift and is not included in any human Refseq FLNA isoform. Alignment of the retained intron sequence to expressed sequence tags and short transcriptome reads revealed its expression in several tissues from multiple organisms. Thus, it likely represents a conserved splicing event that possibly regulates FLNA dosage. All retained introns were found to contain an unknown A-to-I editing site at their center, verified by gDNA genotyping. Whether this editing is a cause or effect of the intron retention remains to be elucidated.
Cis and trans relationships across editing sites
To learn more about the regulation of human RNA editing, clusters of tightly correlated editing sites across all individuals were identified. First, Pearson’s correlation was calculated to quantify the linear dependencies between editing at different sites, among all individuals. Biclustering was then used to identify modules of tightly correlated sites with an average correlation coefficient >0.7 between all pairs of sites in the cluster, across all samples (Supplementary Table 6). The cluster with the strongest correlation contained six sites in four genes (GRIA2 R/G, CYFIP2 K/E, GRIA3 R/G-1 and GRIK2 I/V, Q/R and Y/C) (Pearson’s r=0.8, P<1e−4), suggesting that these sites may be co-regulated (Figure 5b). Some neighboring sites, including GRIA2 Q/R and Q/R+4, showed independent editing (Fisher’s exact P=1, Power >0.97, Figure 5c and Supplementary Table 5), suggesting that their editing is either spatiotemporally distinct and/or carried out by different complexes.
This study represents an initial examination in ASD of A-to-I RNA editing, a form of gene–environment interaction that fine-tunes synaptic function in response to environmental stimuli. Human genes that undergo editing are more often involved in processes that are affected in neurodevelopmental disorders,17, 18 and A-to-I editing is overall increased in the human lineage.26, 27, 28 Although little is known about the impact of RNA editing in humans, there is compelling evidence supporting a key role for A-to-I editing in modulating complex behavior in animals.1, 2, 3, 41, 44, 70 Mice and flies with altered editing levels recapitulate several behavioral homologs to human ASD.1, 41, 44 Gene-specific studies in animals demonstrate the causal relationship between editing levels and specific maladaptive behaviors,1, 10 and between specific environmental exposures and editing levels.71 Therefore, the results presented here shed light on the role of an epigenetic mechanism that connects environmental signals and downstream behavioral outputs, integrating genetic and environmental information.
DNA sequence variation in a number of different loci is strongly associated with ASD, but no individual locus is altered in >2% of cases.66 In contrast, 30% of the individuals with ASD examined here showed extreme levels of synaptic RNA editing, suggesting that A-to-I editing may be both a marker for and mechanism of ASD. It has been recently shown that typical synaptic protein synthesis occurs within an optimal range and deviations in either direction can lead to cognitive impairments.72 This study suggests that a similar inverse U relationship might exist between synaptic A-to-I editing levels and the ASD phenotype. In ASD, it now appears that increased variance can be found in different levels of function, ranging from synaptic A-I editing and protein synthesis,72 through mitochondrial73 and immune74 function, to cellular and systems neuroanatomy.75 A plausible generalization of these findings is that many biological processes exhibit increased variance in ASD as a result of many etiologies disrupting ASD function and homeostasis. We speculate that altered A-to-I editing could act as a common compensatory mechanism for the wide range of synaptic abnormalities in ASD, as affected neurons try to maintain synaptic homeostasis. Alternatively, altered editing could be a direct consequence of ASD-causing mutations. In either case, larger sets of genes, brain regions and individuals should be examined to understand the potential contribution of A-to-I editing to ASD.
Access to the raw sequence reads can be found at the National Database for Autism Research under accession number NDARCOL0001951.
We thank Oliver St Lawrence, Jamie Jett, Benjamin Boese and Tim Harkins at 454, our wonderful lab mates, Professor David Bartel, Thutrang Nguyen, Eran Mick and Elena Helman for their tremendous help. Human tissue was obtained from the National Institute of Child Health and Human Development Brain and Tissue Bank for Developmental Disorders at the University of Maryland, Baltimore, MD, USA contract HHSN275200900011C, re. no. N01-HD-9-0011, and from The Harvard Brain Tissue Resource Center, through the Autism Tissue Program. The Harvard Brain Tissue Resource Center is supported by grant MH068855. The Molecular Genetics Core Facility at Children’s Hospital Boston Intellectual and Developmental Disabilities Research Center (IDDRC) is supported by grant NIH-P30-HD18655. This study was generously supported by the Nancy Lurie Marks Family Foundation, The Roche Applied Science Sequencing Grant Program, Autism Speaks, Simons Foundation and the NIH Grant 1R01MH085143-01.
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Supplementary Information accompanies the paper on the Molecular Psychiatry website (http://www.nature.com/mp)
Nature Reviews Genetics (2018)