Genetic advances in autism: heterogeneity and convergence on shared pathways

Abstract

The autism spectrum disorders (ASD) are a heterogeneous set of developmental disorders characterized at their core by deficits in social interaction and communication. Current psychiatric nosology groups this broad set of disorders with strong genetic liability and multiple etiologies into the same diagnostic category. This heterogeneity has challenged genetic analyses. But shared patient resources, genomic technologies, more refined phenotypes, and novel computational approaches have begun to yield dividends in defining the genetic mechanisms at work. Over the last five years, a large number of autism susceptibility loci have emerged, redefining our notion of autism's etiologies, and reframing how we think about ASD.

Figure 1. Epistatic and Pleiotropic Relationships of Autism Susceptibility Candidate Genes

Analyzing the MET-PI3K-AKT pathway can identify epistatic relationships of ASCG. Genes that are in gray have been associated with ASD, Tuberous Sclerosis (TSC1 and 2) or Fragile X (FMRP), syndromes that are comorbid with ASD. In the left most pathway, MET is seen interacting with PLAUR. PLAUR is the receptor for the tissue specific plasminogen activator (PA), a protease that cleaves HGF into its active form. SERPINE inhibits PA from activating HGF. After HGF binds MET, MET signals downstream through the canonical PI3K-AKT pathway that has a potential terminal endpoint of translational regulation by FMR1 and CYFIP. We propose that the core ASD phenotype may be represented by biological pathway convergence, while the pleiotropic nature of each gene provides the overlying phenotypic variability. For example, MET can signal through multiple receptor complexes that define the functional outcome. For example, MET clustering of NMDA receptors can affect long-term potentiation; while MET binding with members of the plexin receptor family dictate neural migration or axon guidance functions.

Figure 2. A social network for autism susceptibility candidate genes

Analysis of the relationships between 33 ASCG (pink emblems) and associated syndrome genes (green emblems) were analyzed using the Ingenuity Pathway Analysis (http://www.ingenuity.com). Direct (solid lines) and indirect (dashed lines) associations between the ASCG demonstrate the close relationship, less than 2 interactions between each ASCG. For clarity, we did not show all of the associations identified, but focus primarily on direct associations that link ASCG. Recently published interactions linking FMR1, CYFIP1, and JAKMIP1 by gene regulation, and A2BP1 (FOX1) with NLGN3 by splicing regulation were added as custom interactions (blue lines). The androgen receptor (orange emblem) and three of its interactions with ASCG (orange lines) are highlighted to demonstrate the correlation with the extreme male brain hypothesis. This is a first attempt to explore the connectivity between these genes, and therefore is not comprehensive. We do not mean to imply a single pathway as causative, as there may be many pathways that could be implicated.

Figure 3. Functional annotation of the autism susceptibility candidate genes

Annotation was performed using the gene ontology (GO) nomenclature assignments from the National Center for Biotechnology Information (NCBI) for the 33 ASCG used for the Ingenuity analysis (A). GO terminologies are divided into function, process and component. For easier visualization, neuro-specific annotations were compiled using the DAVID Bioinformatics database (http://david.abcc.ncifcrf.gov/) as a histogram (B). The 33 genes from the previous analysis were divided by their GO category (blue bars), and as a percentage of the total genes annotated within the same GO category (green bars). The significance of the enrichment is denoted by p-values, shown adjacent to the GO category. The limitations of the GO terminology are that the categories utilize general descriptors to categorize protein function; some functions are not present in the database, and some protein lack annotation, excluding them from analysis. For example, FMR1 is known to function in dendritic morphogenesis; but this function is not described in its GO annotation. Despite limitations, the GO annotations clearly show that ASD affects multiple processes in the brain suggesting models that address a single aspect of biological function are likely to be too limiting.