Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology

By I. Voineagu, Xinchen Wang, P. Johnston, J. Lowe, Yuan Tian, Steve Horvath, Jonathan Mill, R. Cantor, Benjamin J. Blencowe, Daniel H. Geschwind. Nature, 2011

1,867 citations recorded by the source corpus.

Autism spectrum disorder (ASD) is a common, highly heritable neurodevelopmental condition characterized by marked genetic heterogeneity. Thus, a fundamental question is whether autism represents an aetiologically heterogeneous disorder in which the myriad genetic or environmental risk factors perturb common underlying molecular pathways in the brain. Here, we demonstrate consistent differences in transcriptome organization between autistic and normal brain by gene co-expression network analysis. Remarkably, regional patterns of gene expression that typically distinguish frontal and temporal cortex are significantly attenuated in the ASD brain, suggesting abnormalities in cortical patterning. We further identify discrete modules of co-expressed genes associated with autism: a neuronal module enriched for known autism susceptibility genes, including the neuronal specific splicing factor A2BP1 (also known as FOX1), and a module enriched for immune genes and glial markers. Using high-throughput RNA sequencing we demonstrate dysregulated splicing of A2BP1-dependent alternative exons in the ASD brain. Moreover, using a published autism genome-wide association study (GWAS) data set, we show that the neuronal module is enriched for genetically associated variants, providing independent support for the causal involvement of these genes in autism. In contrast, the immune-glial module showed no enrichment for autism GWAS signals, indicating a non-genetic aetiology for this process. Collectively, our results provide strong evidence for convergent molecular abnormalities in ASD, and implicate transcriptional and splicing dysregulation as underlying mechanisms of neuronal dysfunction in this disorder.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9ddba1f5f559341fb713fd7445028cfdd40ed09b

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Citations in the corpus

Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory Biocitizen (2012, background): “researchers looking at genomic profiles and epigenetic changes together with cellular expression information to understand how pathology develops at the molecular level, for example in the cases of autism and rheumatoid arthritis [10,11].”
The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016, positive): “Recent studies of the developing postmortem human brains of individuals affected with autism spectrum disorder or fragile X syndrome have revealed distinct spatiotemporal patterns of transcriptional and post-transcriptional dysregulation (Kwan et al., 2012; Voineagu et al., 2011).”
The human splicing code reveals new insights into the genetic determinants of disease (2014, background): “are extensively alternatively spliced (40), as well as by recent transcriptomic analyses showing consistent deviations in alternative splicing patterns in the cortical regions of ASD cases (41).”
Gene Expression Elucidates Functional Impact of Polygenic Risk for Schizophrenia (2016, positive): “440 441 Consistent with prior studies of the brain transcriptome (27, 67-70), we find gene co442 expression to be organized into modules of distinct cellular and functional categories (data file 443 7).”
Similarity network fusion for aggregating data types on a genomic scale (2014, background): “APBA2 is a neuronal adapter, involved in signal transduction processes found to be strongly associated with neuropsychiatric disorders, such as autism [16] and schizophrenia [17].”
Loss of mTOR-dependent macroautophagy causes autistic-like synaptic pruning deficits (2014, neutral): “…lobe have been confirmed by functional imaging and pathological studies, including disturbed gene transcription profiles (Garbett et al., 2008; Voineagu et al., 2011), increased dendritic spine densities in pyramidal neurons (Hutsler and Zhang, 2010), and reduced functional specialization…”
Gene co-expression analysis for functional classification and gene–disease predictions (2017, background): “diabetes [73], chronic fatigue [74], other diseases [75, 76] and tis-”