Recent progress in the genomics of non-syndromic autism spectrum disorder (nsASD) highlights rare, large-effect, germline, heterozygous de novo coding mutations. This distinguishes nsASD from later-onset psychiatric disorders where gene discovery efforts have predominantly yielded common alleles of small effect. These differences point to distinctive opportunities for clarifying the neurobiology of nsASD and developing novel treatments. We argue that the path ahead also presents key challenges, including distinguishing human pathophysiology from the potentially pleiotropic neurobiology mediated by established risk genes. We present our view of some of the conceptual limitations of traditional studies of model organisms, suggest a strategy focused on investigating the convergence of multiple nsASD genes, and propose that the detailed characterization of the molecular and cellular landscapes of developing human brain is essential to illuminate disease mechanisms. Finally, we address how recent advances are leading to novel strategies for therapeutics that target various points along the path from genes to behavior.

Autism spectrum disorders (ASD) are a genetically and phenotypically heterogeneous group of syndromes defined by fundamental impairments in social reciprocity and language development accompanied by highly restrictive interests and/or repetitive behaviors. Recent advances in genetics, genomics, developmental neurobiology, systems biology, monogenic neurodevelopment syndromes, and induced pluripotent stem cells (iPSC) are now offering remarkable insights into their etiologies and converging to provide a clear and immediate path forward from the bench to the bedside.

Purpose of review

Recent characterizations of the transcriptome of the developing human brain by several groups have generated comprehensive datasets on coding and noncoding RNAs that will be instrumental for illuminating the underlying biology of complex neurodevelopmental disorders. This review summarizes recent studies successfully utilizing these data to increase our understanding of the molecular mechanisms of pathogenesis.

Recent findings

Several approaches have successfully integrated developmental transcriptome data with gene discovery to generate testable hypotheses about when and where in the developing human brain disease-associated genes converge. Specifically, these include the projection neurons in the prefrontal and primary motor--somatosensory cortex during mid-fetal development in autism spectrum disorder and the frontal cortex during fetal development in schizophrenia.

Summary

Developmental transcriptome data is a key to interpreting disease-associated mutations and transcriptional changes. Novel approaches integrating the spatial and temporal dimensions of these data have increased our understanding of when and where disease occurs. Refinement of spatial and temporal properties and expanding these findings to other neurodevelopmental disorders will provide critical insights for understanding disease biology.

The cerebral cortex is the hallmark of the mammalian nervous system, and its large size and cellular diversity in humans support our most sophisticated cognitive abilities. Although the basic cellular organization of the cortex is conserved across mammals, cells have diversified during evolution. An increasingly integrated taxonomy of cell types, especially with the advent of single-cell transcriptomic data, has revealed an unprecedented variety of human cortical cell subtypes. Here, we broadly review the cellular composition and diversity of the mammalian brain, and how progenitor pools generate cell subtypes during development. We then discuss human cortical cells that are distinct from rodent cells, as well as the challenges and advantages of using model systems to study human cell types in health and disease.

BACKGROUND: Sex-differential biology may contribute to the consistently male-biased prevalence of autism spectrum disorder (ASD). Gene expression differences between males and females in the brain can indicate possible molecular and cellular mechanisms involved, although transcriptomic sex differences during human prenatal cortical development have been incompletely characterized, primarily due to small sample sizes. METHODS: We performed a meta-analysis of sex-differential expression and co-expression network analysis in 2 independent bulk RNA sequencing datasets generated from cortex of 273 prenatal donors without known neuropsychiatric disorders. To assess the intersection between neurotypical sex differences and neuropsychiatric disorder biology, we tested for enrichment of ASD-associated risk genes and expression changes, neuropsychiatric disorder risk genes, and cell type markers within identified sex-differentially expressed genes (sex-DEGs) and sex-differential co-expression modules. RESULTS: We identified 101 significant sex-DEGs, including Y-chromosome genes, genes impacted by X-chromosome inactivation, and autosomal genes. Known ASD risk genes, implicated by either common or rare variants, did not preferentially overlap with sex-DEGs. We identified 1 male-specific co-expression module enriched for immune signaling that is unique to 1 input dataset. CONCLUSIONS: Sex-differential gene expression is limited in prenatal human cortex tissue, although meta-analysis of large datasets allows for the identification of sex-DEGs, including autosomal genes that encode proteins involved in neural development. Lack of sex-DEG overlap with ASD risk genes in the prenatal cortex suggests that sex-differential modulation of ASD symptoms may occur in other brain regions, at other developmental stages, or in specific cell types, or may involve mechanisms that act downstream from mutation-carrying genes.

A dynamic interplay between intrinsic regional molecular cues and extrinsic factors from the thalamus shape multiple features of early cortical development. It remains uncertain and controversial, however, whether the initial formation of cortical columns depends on neuronal activity, and there is little evidence that cortical lamination or neuronal differentiation is influenced by extrinsic activity. We examined the role of thalamic-derived factors in cortical development by selectively eliminating glutamatergic synaptic transmission from thalamocortical neurons in mice and found that eliminating thalamocortical neurotransmission prevented the formation of "barrel" columns in somatosensory cortex. Interestingly, based on cytoarchitectonic criteria and genetic markers, blocking thalamocortical neurotransmission also perturbed the development of superficial cortical lamina and the morphologic development of neurons. These experiments demonstrate that barrels and aspects of the layer-dependent pattern of cortical cytoarchitecture, gene expression, and neuronal differentiation depend on thalamocortical neurotransmission, extending the apparent influence of extrinsic, presumably activity-dependent factors, on cortical development.

A comprehensive characterization of neuronal cell types, their distributions, and patterns of connectivity is critical for understanding the properties of neural circuits and how they generate behaviors. Here we review the experiences of the BRAIN Initiative Cell Census Consortium, ten pilot projects funded by the U.S. BRAIN Initiative, in developing, validating, and scaling up emerging genomic and anatomical mapping technologies for creating a complete inventory of neuronal cell types and their connections in multiple species and during development. These projects lay the foundation for a larger and longer-term effort to generate whole-brain cell atlases in species including mice and humans.

BACKGROUND: Whole-exome sequencing studies in autism spectrum disorder (ASD) have identified de novo mutations in novel candidate genes, including the synaptic gene Eighty-five Requiring 3A (EFR3A). EFR3A is a critical component of a protein complex required for the synthesis of the phosphoinositide PtdIns4P, which has a variety of functions at the neural synapse. We hypothesized that deleterious mutations in EFR3A would be significantly associated with ASD. METHODS: We conducted a large case/control association study by deep resequencing and analysis of whole-exome data for coding and splice site variants in EFR3A. We determined the potential impact of these variants on protein structure and function by a variety of conservation measures and analysis of the Saccharomyces cerevisiae Efr3 crystal structure. We also analyzed the expression pattern of EFR3A in human brain tissue. RESULTS: Rare nonsynonymous mutations in EFR3A were more common among cases (16 / 2,196 = 0.73%) than matched controls (12 / 3,389 = 0.35%) and were statistically more common at conserved nucleotides based on an experiment-wide significance threshold (P = 0.0077, permutation test). Crystal structure analysis revealed that mutations likely to be deleterious were also statistically more common in cases than controls (P = 0.017, Fisher exact test). Furthermore, EFR3A is expressed in cortical neurons, including pyramidal neurons, during human fetal brain development in a pattern consistent with ASD-related genes, and it is strongly co-expressed (P < 2.2 × 10(-16), Wilcoxon test) with a module of genes significantly associated with ASD. CONCLUSIONS: Rare deleterious mutations in EFR3A were found to be associated with ASD using an experiment-wide significance threshold. Synaptic phosphoinositide metabolism has been strongly implicated in syndromic forms of ASD. These data for EFR3A strengthen the evidence for the involvement of this pathway in idiopathic autism.

BACKGROUND: De novo loss-of-function (dnLoF) mutations are found twofold more often in autism spectrum disorder (ASD) probands than their unaffected siblings. Multiple independent dnLoF mutations in the same gene implicate the gene in risk and hence provide a systematic, albeit arduous, path forward for ASD genetics. It is likely that using additional non-genetic data will enhance the ability to identify ASD genes. METHODS: To accelerate the search for ASD genes, we developed a novel algorithm, DAWN, to model two kinds of data: rare variations from exome sequencing and gene co-expression in the mid-fetal prefrontal and motor-somatosensory neocortex, a critical nexus for risk. The algorithm casts the ensemble data as a hidden Markov random field in which the graph structure is determined by gene co-expression and it combines these interrelationships with node-specific observations, namely gene identity, expression, genetic data and the estimated effect on risk. RESULTS: Using currently available genetic data and a specific developmental time period for gene co-expression, DAWN identified 127 genes that plausibly affect risk, and a set of likely ASD subnetworks. Validation experiments making use of published targeted resequencing results demonstrate its efficacy in reliably predicting ASD genes. DAWN also successfully predicts known ASD genes, not included in the genetic data used to create the model. CONCLUSIONS: Validation studies demonstrate that DAWN is effective in predicting ASD genes and subnetworks by leveraging genetic and gene expression data. The findings reported here implicate neurite extension and neuronal arborization as risks for ASD. Using DAWN on emerging ASD sequence data and gene expression data from other brain regions and tissues would likely identify novel ASD genes. DAWN can also be used for other complex disorders to identify genes and subnetworks in those disorders.

Posttranscriptional RNA modifications by adenosine-to-inosine (A-to-I) editing are abundant in the brain, yet elucidating functional sites remains challenging. To bridge this gap, we investigate spatiotemporal and genetically regulated A-to-I editing sites across prenatal and postnatal stages of human brain development. More than 10,000 spatiotemporally regulated A-to-I sites were identified that occur predominately in 3' UTRs and introns, as well as 37 sites that recode amino acids in protein coding regions with precise changes in editing levels across development. Hyper-edited transcripts are also enriched in the aging brain and stabilize RNA secondary structures. These features are conserved in murine and non-human primate models of neurodevelopment. Finally, thousands of cis-editing quantitative trait loci (edQTLs) were identified with unique regulatory effects during prenatal and postnatal development. Collectively, this work offers a resolved atlas linking spatiotemporal variation in editing levels to genetic regulatory effects throughout distinct stages of brain maturation.