First glimpses of the neurobiology of autism spectrum disorder

Neurometabolite differences in Autism as assessed with Magnetic Resonance Spectroscopy: A systematic review and meta-analysis

1H-Magnetic Resonance Spectroscopy (MRS) is a non-invasive technique that can be used to quantify the concentrations of metabolites in the brain in vivo. MRS findings in the context of autism are inconsistent and conflicting. We performed a systematic review and meta-analysis of MRS studies measuring glutamate and gamma-aminobutyric acid (GABA), as well as brain metabolites involved in energy metabolism (glutamine, creatine), neural and glial integrity (e.g. n-acetyl aspartate (NAA), choline, myo-inositol) and oxidative stress (glutathione) in autism cohorts. Data were extracted and grouped by metabolite, brain region and several other factors before calculation of standardised effect sizes. Overall, we find significantly lower concentrations of GABA and NAA in autism, indicative of disruptions to the balance between excitation/inhibition within brain circuits, as well as neural integrity. Further analysis found these alterations are most pronounced in autistic children and in limbic brain regions relevant to autism phenotypes. Additionally, we show how study outcome varies due to demographic and methodological factors , emphasising the importance of conforming with standardised consensus study designs and transparent reporting.

Vulnerability of the Hippocampus to Insults: Links to Blood-Brain Barrier Dysfunction

The hippocampus is a critical brain substrate for learning and memory; events that harm the hippocampus can seriously impair mental and behavioral functioning. Hippocampal pathophysiologies have been identified as potential causes and effects of a remarkably diverse array of medical diseases, psychological disorders, and environmental sources of damage. It may be that the hippocampus is more vulnerable than other brain areas to insults that are related to these conditions. One purpose of this review is to assess the vulnerability of the hippocampus to the most prevalent types of insults in multiple biomedical domains (i.e., neuroactive pathogens, neurotoxins, neurological conditions, trauma, aging, neurodegenerative disease, acquired brain injury, mental health conditions, endocrine disorders, developmental disabilities, nutrition) and to evaluate whether these insults affect the hippocampus first and more prominently compared to other brain loci. A second purpose is to consider the role of hippocampal blood-brain barrier (BBB) breakdown in either causing or worsening the harmful effects of each insult. Recent research suggests that the hippocampal BBB is more fragile compared to other brain areas and may also be more prone to the disruption of the transport mechanisms that act to maintain the internal milieu. Moreover, a compromised BBB could be a factor that is common to many different types of insults. Our analysis indicates that the hippocampus is more vulnerable to insults compared to other parts of the brain, and that developing interventions that protect the hippocampal BBB may help to prevent or ameliorate the harmful effects of many insults on memory and cognition.

Complex Autism Spectrum Disorder in a Patient with a Novel De Novo Heterozygous MYT1L Variant

Autism spectrum disorder (ASD) comprises a group of complex neurodevelopmental features seen in many different forms due to variable causes. Highly impactful ASD-susceptibility genes are involved in pathways associated with brain development, chromatin remodeling, and transcription regulation. In this study, we investigate a proband with complex ASD. Whole genome sequencing revealed a novel de novo missense mutation of a highly conserved amino acid residue (NP_001289981.1:p.His516Gln; chr2:1917275; hg38) in the MYT1L neural transcription factor gene. In combination with in silico analysis on gene effect and pathogenicity, we described the proband's phenotype and made comparisons with previously reported cases to explore the spectrum of clinical features in MYT1L single nucleotide variant (SNV) cases. The phenotype-genotype correlation showed a high degree of clinical similarity with previously reported cases of missense variants in MYT1L, indicating MYT1L as the causal gene for the observed phenotype in our proband. The variant was also predicted to be damaging according to multiple in silico pathogenicity predicting tools. This study expands the clinical description of SNVs on the MYT1L gene and provides insight into its contribution to ASD.

A hybrid Stacking-SMOTE model for optimizing the prediction of autistic genes

PURPOSE

Autism spectrum disorder(ASD) is a disease associated with the neurodevelopment of the brain. The autism spectrum can be observed in early childhood, where the symptoms of the disease usually appear in children within the first year of their life. Currently, ASD can only be diagnosed based on the apparent symptoms due to the lack of information on genes related to the disease. Therefore, in this paper, we need to predict the largest number of disease-causing genes for a better diagnosis.

METHODS

A hybrid stacking ensemble model with Synthetic Minority Oversampling TEchnique (Stack-SMOTE) is proposed to predict the genes associated with ASD. The proposed model uses the gene ontology database to measure the similarities between the genes using a hybrid gene similarity function(HGS). HGS is effective in measuring the similarity as it combines the features of information gain-based methods and graph-based methods. The proposed model solves the imbalanced ASD dataset problem using the Synthetic Minority Oversampling Technique (SMOTE), which generates synthetic data rather than duplicates the data to reduce the overfitting. Sequentially, a gradient boosting-based random forest classifier (GBBRF) is introduced as a new combination technique to enhance the prediction of ASD genes. Moreover, the GBBRF classifier combined with random forest(RF), k-nearest neighbor, support vector machine(SVM), and logistic regression(LR) to form the proposed Stacking-SMOTE model to optimize the prediction of ASD genes.

RESULTS

The proposed Stacking-SMOTE model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database and a set of candidates ASD genes.The results of the proposed model-based SMOTE outperform other reported undersampling and oversampling techniques. Sequentially, the results of GBBRF achieve higher accuracy than using the basic classifiers. Moreover, the experimental results show that the proposed Stacking-SMOTE model outperforms the existing ASD prediction models with approximately 95.5% accuracy.

CONCLUSION

The proposed Stacking-SMOTE model demonstrates that SMOTE is effective in handling the autism imbalanced data. Sequentially, the integration between the gradient boosting and random forest classifier (GBBRF) support to build a robust stacking ensemble model(Stacking-SMOTE).

DGH-GO: dissecting the genetic heterogeneity of complex diseases using gene ontology

BACKGROUND

Complex diseases such as neurodevelopmental disorders (NDDs) exhibit multiple etiologies. The multi-etiological nature of complex-diseases emerges from distinct but functionally similar group of genes. Different diseases sharing genes of such groups show related clinical outcomes that further restrict our understanding of disease mechanisms, thus, limiting the applications of personalized medicine approaches to complex genetic disorders.

RESULTS

Here, we present an interactive and user-friendly application, called DGH-GO. DGH-GO allows biologists to dissect the genetic heterogeneity of complex diseases by stratifying the putative disease-causing genes into clusters that may contribute to distinct disease outcome development. It can also be used to study the shared etiology of complex-diseases. DGH-GO creates a semantic similarity matrix for the input genes by using Gene Ontology (GO). The resultant matrix can be visualized in 2D plots using different dimension reduction methods (T-SNE, Principal component analysis, umap and Principal coordinate analysis). In the next step, clusters of functionally similar genes are identified from genes functional similarities assessed through GO. This is achieved by employing four different clustering methods (K-means, Hierarchical, Fuzzy and PAM). The user may change the clustering parameters and explore their effect on stratification immediately. DGH-GO was applied to genes disrupted by rare genetic variants in Autism Spectrum Disorder (ASD) patients. The analysis confirmed the multi-etiological nature of ASD by identifying four clusters of genes that were enriched for distinct biological mechanisms and clinical outcome. In the second case study, the analysis of genes shared by different NDDs showed that genes causing multiple disorders tend to aggregate in similar clusters, indicating a possible shared etiology.

CONCLUSION

DGH-GO is a user-friendly application that allows biologists to study the multi-etiological nature of complex diseases by dissecting their genetic heterogeneity. In summary, functional similarities, dimension reduction and clustering methods, coupled with interactive visualization and control over analysis allows biologists to explore and analyze their datasets without requiring expert knowledge on these methods. The source code of proposed application is available at https://github.com/Muh-Asif/DGH-GO.

HEC-ASD: a hybrid ensemble-based classification model for predicting autism spectrum disorder disease genes

PURPOSE

Autism spectrum disorder (ASD) is the most prevalent disease today. The causes of its infection may be attributed to genetic causes by 80% and environmental causes by 20%. In spite of this, the majority of the current research is concerned with environmental causes, and the least proportion with the genetic causes of the disease. Autism is a complex disease, which makes it difficult to identify the genes that cause the disease.

METHODS

Hybrid ensemble-based classification (HEC-ASD) model for predicting ASD genes using gradient boosting machines is proposed. The proposed model utilizes gene ontology (GO) to construct a gene functional similarity matrix using hybrid gene similarity (HGS) method. HGS measures the semantic similarity between genes effectively. It combines the graph-based method, such as Wang method with the number of directed children's nodes of gene term from GO. Moreover, an ensemble gradient boosting classifier is adapted to enhance the prediction of genes forming a robust classification model.

RESULTS

The proposed model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database. The experimental results are promising as they improve the classification performance for predicting ASD genes. The results are compared with other approaches that used gene regulatory network (GRN), protein to protein interaction network (PPI), or GO. The HEC-ASD model reaches the highest prediction accuracy of 0.88% using ensemble learning classifiers.

CONCLUSION

The proposed model demonstrates that ensemble learning technique using gradient boosting is effective in predicting autism spectrum disorder genes. Moreover, the HEC-ASD model utilized GO rather than using PPI network and GRN.

Dopaminergic Gene Dosage Reveals Distinct Biological Partitions between Autism and Developmental Delay as Revealed by Complex Network Analysis and Machine Learning Approaches

The neurobiological mechanisms underlying Autism Spectrum Disorders (ASD) remains controversial. One factor contributing to this debate is the phenotypic heterogeneity observed in ASD, which suggests that multiple system disruptions may contribute to diverse patterns of impairment which have been reported between and within study samples. Here, we used SFARI data to address genetic imbalances affecting the dopaminergic system. Using complex network analysis, we investigated the relations between phenotypic profiles, gene dosage and gene ontology (GO) terms related to dopaminergic neurotransmission from a polygenic point-of-view. We observed that the degree of distribution of the networks matched a power-law distribution characterized by the presence of hubs, gene or GO nodes with a large number of interactions. Furthermore, we identified interesting patterns related to subnetworks of genes and GO terms, which suggested applicability to separation of clinical clusters (Developmental Delay (DD) versus ASD). This has the potential to improve our understanding of genetic variability issues and has implications for diagnostic categorization. In ASD, we identified the separability of four key dopaminergic mechanisms disrupted with regard to receptor binding, synaptic physiology and neural differentiation, each belonging to particular subgroups of ASD participants, whereas in DD a more unitary biological pattern was found. Finally, network analysis was fed into a machine learning binary classification framework to differentiate between the diagnosis of ASD and DD. Subsets of 1846 participants were used to train a Random Forest algorithm. Our best classifier achieved, on average, a diagnosis-predicting accuracy of 85.18% (sd 1.11%) on the test samples of 790 participants using 117 genes. The achieved accuracy surpassed results using genetic data and closely matched imaging approaches addressing binary diagnostic classification. Importantly, we observed a similar prediction accuracy when the classifier uses only 62 GO features. This result further corroborates the complex network analysis approach, suggesting that different genetic causes might converge to the dysregulation of the same set of biological mechanisms, leading to a similar disease phenotype. This new biology-driven ontological framework yields a less variable and more compact domain-related set of features with potential mechanistic generalization. The proposed network analysis, allowing for the determination of a clearcut biological distinction between ASD and DD (the latter presenting much lower modularity and heterogeneity), is amenable to machine learning approaches and provides an interesting avenue of research for the future.

MYT1L in the making: emerging insights on functions of a neurodevelopmental disorder gene

Large scale human genetic studies have shown that loss of function (LoF) mutations in MYT1L are implicated in neurodevelopmental disorders (NDDs). Here, we provide an overview of the growing number of published MYT1L patient cases, and summarize prior studies in cells, zebrafish, and mice, both to understand MYT1L's molecular and cellular role during brain development and consider how its dysfunction can lead to NDDs. We integrate the conclusions from these studies and highlight conflicting findings to reassess the current model of the role of MYT1L as a transcriptional activator and/or repressor based on the biological context. Finally, we highlight additional functional studies that are needed to understand the molecular mechanisms underlying pathophysiology and propose key questions to guide future preclinical studies.

Commonalities and distinctions between two neurodevelopmental disorder subtypes associated with SCN2A and SCN8A variants and literature review

This study was aimed to analyze the commonalities and distinctions of voltage‐gated sodium channels, Nav1.2, Nav1.6, in neurodevelopmental disorders. An observational study was performed including two patients with neurodevelopmental disorders. The demographic, electroclinical, genetic, and neuropsychological characteristics were analyzed and compared with each other and then with the subjects carrying the same genetic variants reported in the literature. The clinical features of one of them argued for autism spectrum disorder and developmental delay, the other for intellectual disability, diagnoses confirmed by the neuropsychological assessment. The first patient was a carrier of SCN2A (p.R379H) variant while the second was carrier of SCN8A (p.E936K) variant, both involving the pore loop of the two channels. The results of this study suggest that the neurodevelopmental disorders without overt epilepsy of both patients can be the consequences of loss of function of Nav1.2/Nav1.6 channels. Notably, the SCN2A variant, with an earlier expression timing in brain development, resulted in a more severe phenotype as autism spectrum disorder and developmental delay, while the SCN8A variant, with a later expression timing, resulted in a less severe phenotype as intellectual disability.

De novo GRIN2A variants associated with epilepsy and autism and literature review

N-methyl-D-aspartate receptors (NMDAR) are di- or tri-heterotetrameric ligand-gated ion channels composed of two obligate glycine-binding GluN1 subunits and two glutamate-binding GluN2 or GluN3 subunits, encoded by GRIN1, GRIN2A-D, and GRIN3A-B receptor genes respectively. Each NMDA receptor subtype has different temporal and spatial expression patterns in the brain and varies in the cell types and subcellular localization resulting in different functions. They play a crucial role in mediating the excitatory neurotransmission, but are also involved in neuronal development and synaptic plasticity, essential for learning, memory, and high cognitive functions. Among genes coding NMDAR subunits, GRIN2B is predominantly associated with neurodevelopmental disorders such as intellectual disability, developmental delay, autism, attention-deficit/hyperactivity disorder and, further, schizophrenia, Alzheimer's disease. The GRIN2A seems to be predominantly associated with a more definite phenotype including an epileptic spectrum ranging from Landau-Kleffner syndrome to benign childhood epilepsy with centrotemporal spikes, speech or language impairment, intellectual disability/developmental delay often in comorbidity. On the contrary, the occurrence of autism spectrum disorders, unlike GRIN2B-associated disorders, is questionable. To contribute to elucidate the latter issue and to better define the genotype/phenotype correlation, we report the clinical and neuropsychological profile of two patients featuring autism disorder, intellectual disability, language impairment, and focal epilepsy, associated with previously unreported heterozygous de novo GRIN2A pathogenic variants. We hypothesize that the unusual phenotype may be the result of interactions of tri-heterotetrameric 2GluN1/GluN2A-D/GluN3A-B subunits with mutated GluN2A subunit and/or the dysfunction may be influenced by other unknown modifier genes and/or environmental factors.

A MYT1L syndrome mouse model recapitulates patient phenotypes and reveals altered brain development due to disrupted neuronal maturation

Human genetics have defined a new neurodevelopmental syndrome caused by loss-of-function mutations in MYT1L, a transcription factor known for enabling fibroblast-to-neuron conversions. However, how MYT1L mutation causes intellectual disability, autism, ADHD, obesity, and brain anomalies is unknown. Here, we developed a Myt1l haploinsufficient mouse model that develops obesity, white-matter thinning, and microcephaly, mimicking common clinical phenotypes. During brain development we discovered disrupted gene expression, mediated in part by loss of Myt1l gene-target activation, and identified precocious neuronal differentiation as the mechanism for microcephaly. In contrast, in adults we discovered that mutation results in failure of transcriptional and chromatin maturation, echoed in disruptions in baseline physiological properties of neurons. Myt1l haploinsufficiency also results in behavioral anomalies, including hyperactivity, muscle weakness, and social alterations, with more severe phenotypes in males. Overall, our findings provide insight into the mechanistic underpinnings of this disorder and enable future preclinical studies.

MCP-1 Signaling Disrupts Social Behavior by Modulating Brain Volumetric Changes and Microglia Morphology

Autism spectrum disorder (ASD) is a disease characterized by reduced social interaction and stereotypic behaviors and related to macroscopic volumetric changes in cerebellar and somatosensory cortices (SPP). Epidemiological and preclinical models have confirmed that a proinflammatory profile during fetal development increases ASD susceptibility after birth. Here, we aimed to globally identify the effect of maternal exposure to high-energy dense diets, which we refer to as cafeteria diet (CAF) on peripheral and central proinflammatory profiles, microglia reactivity, and volumetric brain changes related to assisting defective social interaction in the mice offspring. We found a sex-dependent effect of maternal exposure to CAF diet or inoculation of the dsARN mimetic Poly (I:C) on peripheral proinflammatory and social interaction in the offspring. Notably, maternal exposure to CAF diet impairs social interaction and favors an increase in anxiety in male but not female offspring. Also, CAF diet exposure or Poly (I:C) inoculation during fetal programming promote peripheral proinflammatory profile in the ASD-diagnosed male but not in females. Selectively, we found a robust accumulation of the monocyte chemoattractant protein-1 (MCP-1) in plasma of ASD-diagnosed males exposed to CAF during fetal development. Biological assessment of MCP-1 signaling in brain confirms that systemic injection of MCP-1-neutralizing antibody reestablished social interaction and blocked anxiety, accompanied by a reduction in cerebellar lobule X (CbX) volume and an increase volume of the primary somatosensory (SSP) cortex in male offspring. These data highlight the contribution of diet-dependent MCP-1 signaling on volumetric brain changes and microglia morphology promoting ASD-like behavior in male mice.

The Mechanisms of CHD8 in Neurodevelopment and Autism Spectrum Disorders

Chromodomain-helicase-DNA-binding protein 8 (CHD8) has been identified as one of the genes with the strongest association with autism. The CHD8 protein is a transcriptional regulator that is expressed in nearly all cell types and has been implicated in multiple cellular processes, including cell cycle, cell adhesion, neuronal development, myelination, and synaptogenesis. Considering the central role of CHD8 in the genetics of autism, a deeper understanding of the physiological functions of CHD8 is important to understand the development of the autism phenotype and potential therapeutic targets. Different CHD8 mutant mouse models were developed to determine autism-like phenotypes and to fully understand their mechanisms. Here, we review the current knowledge on CHD8, with an emphasis on mechanistic lessons gained from animal models that have been studied.

Gene-Environment Interactions in Developmental Neurotoxicity: a Case Study of Synergy between Chlorpyrifos and CHD8 Knockout in Human BrainSpheres

BACKGROUND

Autism spectrum disorder (ASD) is a major public health concern caused by complex genetic and environmental components. Mechanisms of gene-environment (G × E ) interactions and reliable biomarkers associated with ASD are mostly unknown or controversial. Induced pluripotent stem cells (iPSCs) from patients or with clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9)-introduced mutations in candidate ASD genes provide an opportunity to study (G × E ) interactions.

OBJECTIVES

In this study, we aimed to identify a potential synergy between mutation in the high-risk autism gene encoding chromodomain helicase DNA binding protein 8 (CHD8) and environmental exposure to an organophosphate pesticide (chlorpyrifos; CPF) in an iPSC-derived human three-dimensional (3D) brain model.

METHODS

This study employed human iPSC-derived 3D brain organoids (BrainSpheres) carrying a heterozygote CRISPR/Cas9-introduced inactivating mutation in CHD8 and exposed to CPF or its oxon-metabolite (CPO). Neural differentiation, viability, oxidative stress, and neurite outgrowth were assessed, and levels of main neurotransmitters and selected metabolites were validated against human data on ASD metabolic derangements.

RESULTS

Expression of CHD8 protein was significantly lower in CHD8 heterozygous knockout (C H D 8 + / - ) BrainSpheres compared with C H D 8 + / + ones. Exposure to CPF/CPO treatment further reduced CHD8 protein levels, showing the potential (G × E ) interaction synergy. A novel approach for validation of the model was chosen: from the literature, we identified a panel of metabolic biomarkers in patients and assessed them by targeted metabolomics in vitro. A synergistic effect was observed on the cholinergic system, S-adenosylmethionine, S-adenosylhomocysteine, lactic acid, tryptophan, kynurenic acid, and α -hydroxyglutaric acid levels. Neurite outgrowth was perturbed by CPF/CPO exposure. Heterozygous knockout of CHD8 in BrainSpheres led to an imbalance of excitatory/inhibitory neurotransmitters and lower levels of dopamine.

DISCUSSION

This study pioneered (G × E ) interaction in iPSC-derived organoids. The experimental strategy enables biomonitoring and environmental risk assessment for ASD. Our findings reflected some metabolic perturbations and disruption of neurotransmitter systems involved in ASD. The increased susceptibility of CHD 8 + / - BrainSpheres to chemical insult establishes a possibly broader role of (G × E ) interaction in ASD. https://doi.org/10.1289/EHP8580.

Reflections on the genetics-first approach to advancements in molecular genetic and neurobiological research on neurodevelopmental disorders

BACKGROUND

Neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD) and intellectual disability (ID), are common diagnoses with highly heterogeneous phenotypes and etiology. The genetics-first approach to research on NDDs has led to the identification of hundreds of genes conferring risk for ASD, ID, and related symptoms.

MAIN BODY

Although relatively few individuals with NDDs share likely gene-disruptive (LGD) mutations in the same gene, characterization of overlapping functions, protein networks, and temporospatial expression patterns among these genes has led to increased understanding of the neurobiological etiology of NDDs. This shift in focus away from single genes and toward broader gene-brain-behavior pathways has been accelerated by the development of publicly available transcriptomic databases, cell type-specific research methods, and sequencing of non-coding genomic regions.

CONCLUSIONS

The genetics-first approach to research on NDDs has advanced the identification of critical protein function pathways and temporospatial expression patterns, expanding the impact of this research beyond individuals with single-gene mutations to the broader population of patients with NDDs.

A Role for Vasoactive Intestinal Peptide Interneurons in Neurodevelopmental Disorders

GABAergic inhibitory interneurons of the cerebral cortex expressing vasoactive intestinal peptide (VIP-INs) are rapidly emerging as important regulators of network dynamics and normal circuit development. Several recent studies have also identified VIP-IN dysfunction in models of genetically determined neurodevelopmental disorders (NDDs). In this article, we review the known circuit functions of VIP-INs and how they may relate to accumulating evidence implicating VIP-INs in the mechanisms of prominent NDDs. We highlight recurring VIP-IN-mediated circuit motifs that are shared across cerebral cortical areas and how VIP-IN activity can shape sensory input, development, and behavior. Ultimately, we extract a set of themes that inform our understanding of how VIP-INs influence pathogenesis of NDDs. Using publicly available single-cell RNA sequencing data from the Allen Institute, we also identify several underexplored disease-associated genes that are highly expressed in VIP-INs. We survey these genes and their shared related disease phenotypes that may broadly implicate VIP-INs in autism spectrum disorder and intellectual disability rather than epileptic encephalopathy. Finally, we conclude with a discussion of the relevance of cell type-specific investigations and therapeutics in the age of genomic diagnosis and targeted therapeutics.

Pre-eclampsia and long-term health outcomes for mother and infant: an umbrella review

BACKGROUND

Pre-eclampsia is a pregnancy-associated condition with complex disease mechanisms and a risk factor for various long-term health outcomes for the mother and infant.

OBJECTIVE

To summarise evidence on the association of pre-eclampsia with long-term health outcomes arising in women and/or infants.

SEARCH STRATEGY

PubMed, EMBASE, Scopus and ISI Web of Science were searched from inception to July 2020.

SELECTION CRITERIA

Systematic reviews and meta-analyses examining associations between pre-eclampsia and long-term health outcomes in women and their infants.

DATA COLLECTION AND ANALYSIS

Data were extracted by two independent reviewers. We re-estimated the summary effect size by random-effects and fixed-effects models, the 95% confidence interval, the 95% prediction interval, the between-study heterogeneity, any evidence of small-study effects and excess significance bias.

RESULTS

Twenty-one articles were included (90 associations). Seventy-nine associations had nominally statistically significant findings (P < 0.05). Sixty-five associations had large or very large heterogeneity. Evidence for small-study effects and excess significance bias was found in seven and two associations, respectively. Nine associations: cerebrovascular disease (cohort studies), cerebrovascular disease (overall), cardiac disease (cohort studies), dyslipidaemia (all studies), risk of death (late-onset pre-eclampsia), fatal and non-fatal ischaemic heart disease, cardiovascular mortality (cohort studies), any diabetes or use of diabetic medication (unadjusted), and attention deficit/hyperactivity disorder (ADHD) (adjusted) were supported with robust evidence.

CONCLUSION

Many of the meta-analyses in this research field have caveats casting doubts on their validity. Current evidence suggests an increased risk for women to develop cardiovascular-related diseases, diabetes and dyslipidaemia after pre-eclampsia, while offspring exposed to pre-eclampsia are at higher risk for ADHD.

TWEETABLE ABSTRACT

Cardiovascular and cerebrovascular diseases were supported with convincing evidence for long-term health outcomes after pre-eclampsia.

Autism Spectrum Disorder Genetics and the Search for Pathological Mechanisms

Recent progress in the identification of genes and genomic regions contributing to autism spectrum disorder (ASD) has had a broad impact on our understanding of the nature of genetic risk for a range of psychiatric disorders, on our understanding of ASD biology, and on defining the key challenges now facing the field in efforts to translate gene discovery into an actionable understanding of pathology. While these advances have not yet had a transformative impact on clinical practice, there is nonetheless cause for real optimism: reliable lists of risk genes are large and growing rapidly; the identified encoded proteins have already begun to point to a relatively small number of areas of biology, where parallel advances in neuroscience and functional genomics are yielding profound insights; there is strong evidence pointing to mid-fetal prefrontal cortical development as one nexus of vulnerability for some of the largest-effect ASD risk genes; and there are multiple plausible paths forward toward rational therapeutics development that, while admittedly challenging, constitute fundamental departures from what was possible prior to the era of successful gene discovery.

Dysregulation of Neurite Outgrowth and Cell Migration in Autism and Other Neurodevelopmental Disorders

Despite decades of study, elucidation of the underlying etiology of complex developmental disorders such as autism spectrum disorder (ASD), schizophrenia (SCZ), intellectual disability (ID), and bipolar disorder (BPD) has been hampered by the inability to study human neurons, the heterogeneity of these disorders, and the relevance of animal model systems. Moreover, a majority of these developmental disorders have multifactorial or idiopathic (unknown) causes making them difficult to model using traditional methods of genetic alteration. Examination of the brains of individuals with ASD and other developmental disorders in both post-mortem and MRI studies shows defects that are suggestive of dysregulation of embryonic and early postnatal development. For ASD, more recent genetic studies have also suggested that risk genes largely converge upon the developing human cerebral cortex between weeks 8 and 24 in utero. Yet, an overwhelming majority of studies in autism rodent models have focused on postnatal development or adult synaptic transmission defects in autism related circuits. Thus, studies looking at early developmental processes such as proliferation, cell migration, and early differentiation, which are essential to build the brain, are largely lacking. Yet, interestingly, a few studies that did assess early neurodevelopment found that alterations in brain structure and function associated with neurodevelopmental disorders (NDDs) begin as early as the initial formation and patterning of the neural tube. By the early to mid-2000s, the derivation of human embryonic stem cells (hESCs) and later induced pluripotent stem cells (iPSCs) allowed us to study living human neural cells in culture for the first time. Specifically, iPSCs gave us the unprecedented ability to study cells derived from individuals with idiopathic disorders. Studies indicate that iPSC-derived neural cells, whether precursors or "matured" neurons, largely resemble cortical cells of embryonic humans from weeks 8 to 24. Thus, these cells are an excellent model to study early human neurodevelopment, particularly in the context of genetically complex diseases. Indeed, since 2011, numerous studies have assessed developmental phenotypes in neurons derived from individuals with both genetic and idiopathic forms of ASD and other NDDs. However, while iPSC-derived neurons are fetal in nature, they are post-mitotic and thus cannot be used to study developmental processes that occur before terminal differentiation. Moreover, it is important to note that during the 8-24-week window of human neurodevelopment, neural precursor cells are actively undergoing proliferation, migration, and early differentiation to form the basic cytoarchitecture of the brain. Thus, by studying NPCs specifically, we could gain insight into how early neurodevelopmental processes contribute to the pathogenesis of NDDs. Indeed, a few studies have explored NPC phenotypes in NDDs and have uncovered dysregulations in cell proliferation. Yet, few studies have explored migration and early differentiation phenotypes of NPCs in NDDs. In this chapter, we will discuss cell migration and neurite outgrowth and the role of these processes in neurodevelopment and NDDs. We will begin by reviewing the processes that are important in early neurodevelopment and early cortical development. We will then delve into the roles of neurite outgrowth and cell migration in the formation of the brain and how errors in these processes affect brain development. We also provide review of a few key molecules that are involved in the regulation of neurite outgrowth and migration while discussing how dysregulations in these molecules can lead to abnormalities in brain structure and function thereby highlighting their contribution to pathogenesis of NDDs. Then we will discuss whether neurite outgrowth, migration, and the molecules that regulate these processes are associated with ASD. Lastly, we will review the utility of iPSCs in modeling NDDs and discuss future goals for the study of NDDs using this technology.

Evidence against the "normalization" prediction of the early brain overgrowth hypothesis of autism

BACKGROUND

The frequently cited Early Overgrowth Hypothesis of autism spectrum disorder (ASD) postulates that there is overgrowth of the brain in the first 2 years of life, which is followed by a period of arrested growth leading to normalized brain volume in late childhood and beyond. While there is consistent evidence for early brain overgrowth, there is mixed evidence for normalization of brain volume by middle childhood. The outcome of this debate is important to understanding the etiology and neurodevelopmental trajectories of ASD.

METHODS

Brain volume was examined in two very large single-site samples of children, adolescents, and adults. The primary sample comprised 456 6-25-year-olds (ASD n = 240, typically developing controls (TDC) n = 216), including a large number of females (n = 102) and spanning a wide IQ range (47-158). The replication sample included 175 males. High-resolution T1-weighted anatomical MRI images were examined for group differences in total brain, cerebellar, ventricular, gray, and white matter volumes.

RESULTS

The ASD group had significantly larger total brain, cerebellar, gray matter, white matter, and lateral ventricular volumes in both samples, indicating that brain volume remains enlarged through young adulthood, rather than normalizing. There were no significant age or sex interactions with diagnosis in these measures. However, a significant diagnosis-by-IQ interaction was detected in the larger sample, such that increased brain volume was related to higher IQ in the TDCs, but not in the ASD group. Regions-of-significance analysis indicated that total brain volume was larger in ASD than TDC for individuals with IQ less than 115, providing a potential explanation for prior inconsistent brain size results. No relationships were found between brain volume and measures of autism symptom severity within the ASD group.

LIMITATIONS

Our cross-sectional sample may not reflect individual changes over time in brain volume and cannot quantify potential changes in volume prior to age 6.

CONCLUSIONS

These findings challenge the "normalization" prediction of the brain overgrowth hypothesis by demonstrating that brain enlargement persists across childhood into early adulthood. The findings raise questions about the clinical implications of brain enlargement, since we find that it neither confers cognitive benefits nor predicts increased symptom severity in ASD.

Modeling neuronal consequences of autism-associated gene regulatory variants with human induced pluripotent stem cells

Genetic factors contribute to the development of autism spectrum disorder (ASD), and although non-protein-coding regions of the genome are being increasingly implicated in ASD, the functional consequences of these variants remain largely uncharacterized. Induced pluripotent stem cells (iPSCs) enable the production of personalized neurons that are genetically matched to people with ASD and can therefore be used to directly test the effects of genomic variation on neuronal gene expression, synapse function, and connectivity. The combined use of human pluripotent stem cells with genome editing to introduce or correct specific variants has proved to be a powerful approach for exploring the functional consequences of ASD-associated variants in protein-coding genes and, more recently, long non-coding RNAs (lncRNAs). Here, we review recent studies that implicate lncRNAs, other non-coding mutations, and regulatory variants in ASD susceptibility. We also discuss experimental design considerations for using iPSCs and genome editing to study the role of the non-protein-coding genome in ASD.

Sex Differences Along the Autism Continuum: A Twin Study of Brain Structure

Females might possess protective mechanisms regarding autism spectrum disorder (ASD) and require a higher detrimental load, including structural brain alterations, before developing clinically relevant levels of autistic traits. This study examines sex differences in structural brain morphology in autism and autistic traits using a within-twin pair approach. Twin design inherently controls for shared confounders and enables the study of gene-independent neuroanatomical variation. N = 148 twins (62 females) from 49 monozygotic and 25 dizygotic same-sex pairs were included. Participants were distributed along the whole continuum of autism including twin pairs discordant and concordant for clinical ASD. Regional brain volume, surface area, and cortical thickness were computed. Within-twin pair increases in autistic traits were related to decreases in cortical volume and surface area of temporal and frontal regions specifically in female twin pairs, in particular regions involved in social communication, while only two regions were associated with autistic traits in males. The same pattern was detected in the monozygotic twin pairs only. Thus, non-shared environmental factors seem to impact female more than male cerebral architecture associated with autistic traits. Our results are in line with the hypothesis of a female protective effect in autism and highlights the need to study ASD in females separately from males.

Neurodevelopmental Outcomes of Prenatal Preeclampsia Exposure

Preeclampsia is a dangerous hypertensive disorder of pregnancy with known links to negative child health outcomes. Here, we review epidemiological and basic neuroscience work from the past several decades linking prenatal preeclampsia to altered neurodevelopment. This work demonstrates increased rates of neuropsychiatric disorders [e.g., increased autism spectrum disorder, attention deficit hyperactivity disorder (ADHD)] in children of preeclamptic pregnancies, as well as increased rates of cognitive impairments [e.g., decreased intelligence quotient (IQ), academic performance] and neurological disease (e.g., stroke and epilepsy). We also review findings from multiple animal models of preeclampsia. Manipulation of key clinical preeclampsia processes in these models (e.g., placental hypoxia, immune dysfunction, angiogenesis, oxidative stress) causes various disruptions in offspring, including ones in white matter/glia, glucocorticoid receptors, neuroimmune outcomes, cerebrovascular structure, and cognition/behavior. This animal work implicates potentially high-yield targets that may be leveraged in the future for clinical application.

Sex/gender differences in neurology and psychiatry: Autism

Autism is a heterogenous set of early-onset neurodevelopmental conditions that are more prevalent in males than in females. Due to the high phenotypic, neurobiological, developmental, and etiological heterogeneity in the autism spectrum, recent research programs are increasingly exploring whether sex- and gender-related factors could be helpful markers to clarify the heterogeneity in autism and work toward a personalized approach to intervention and support. In this chapter, we summarize recent clinical and neuroscientific research addressing sex/gender influences in autism and explore how sex/gender-based investigations shed light on similar or different underlying neurodevelopmental mechanisms of autism by sex/gender. We review evidence that may help to explain some of the underlying sex-related biological mechanisms associated with autism, including genetics and the effects of sex steroid hormones in the prenatal environment. We conclude that current research points toward coexisting quantitative and, perhaps more evidently, qualitative sex/gender-modulation effects in autism across multiple neurobiological aspects. However, converging findings of specific neurobiological presentations and sex/gender-informed mechanisms cutting across the many subgroups within the autism spectrum are still lacking. Future research should use big data approaches and new stratification methods to decompose sex/gender-related heterogeneity in autism and work toward personalized, sex/gender-informed intervention and support for autistic people.

Big data approaches to decomposing heterogeneity across the autism spectrum

Autism is a diagnostic label based on behavior. While the diagnostic criteria attempt to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame research examining multi-level heterogeneity in autism. Theoretical concepts such as 'spectrum' or 'autisms' reflect non-mutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case-control models are suboptimal for tackling heterogeneity. Big data are an important ingredient for furthering our understanding of heterogeneity in autism. In addition to being 'feature-rich', big data should be both 'broad' (i.e., large sample size) and 'deep' (i.e., multiple levels of data collected on the same individuals). These characteristics increase the likelihood that the study results are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model's utility can be measured by its ability to explain clinically or mechanistically important phenomena, and also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing changes within individuals. While progress can be made with 'supervised' models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. A better understanding of how to model heterogeneity between autistic people will facilitate progress towards precision medicine for symptoms that cause suffering, and person-centered support.

Spontaneous Activity Patterns Are Altered in the Developing Visual Cortex of the Fmr1 Knockout Mouse

Fragile X syndrome (FXS) is the most prevalent inherited cause of autism and is accompanied by behavioral and sensory deficits. Errors in the wiring of the brain during early development likely contribute to these deficits, but the underlying mechanisms are unclear. Spontaneous activity patterns, which are required for fine-tuning neuronal networks before the senses become active, are perturbed in rodent models of FXS. Here, we investigated spontaneous network activity patterns in the developing visual cortex of the Fmr1 knockout mouse using in vivo calcium imaging during the second postnatal week, before eye opening. We found that while the frequency, mean amplitude and duration of spontaneous network events were unchanged in the knockout mouse, pair-wise correlations between neurons were increased compared to wild type littermate controls. Further analysis revealed that interneuronal correlations were not generally increased, rather that low-synchronization events occurred relatively less frequently than high-synchronization events. Low-, but not high-, synchronization events have been associated with retinal inputs previously. Since we found that spontaneous retinal waves were normal in the knockout, our results suggest that peripherally driven activity is underrepresented in the Fmr1 KO visual cortex. Therefore, we propose that central gating of retinal inputs may be affected in FXS and that peripherally and centrally driven activity patterns are already unbalanced before eye opening in this disorder.

FunVar: A systematic pipeline to unravel the convergence patterns of genetic variants in ASD, a paradigmatic complex disease

In recent years, the technological advances for capturing genetic variation in large populations led to the identification of large numbers of putative or disease-causing variants. However, their mechanistic understanding is lagging far behind and has posed new challenges regarding their relevance for disease phenotypes, particularly for common complex disorders. In this study, we propose a systematic pipeline to infer biological meaning from genetic variants, namely rare Copy Number Variants (CNVs). The pipeline consists of three modules that seek to (1) improve genetic data quality by excluding low confidence CNVs, (2) identify disrupted biological processes, and (3) aggregate similar enriched biological processes terms using semantic similarity. The proposed pipeline was applied to CNVs from individuals diagnosed with Autism Spectrum Disorder (ASD). We found that rare CNVs disrupting brain expressed genes dysregulated a wide range of biological processes, such as nervous system development and protein polyubiquitination. The disrupted biological processes identified in ASD patients were in accordance with previous findings. This coherence with literature indicates the feasibility of the proposed pipeline in interpreting the biological role of genetic variants in complex disease development. The suggested pipeline is easily adjustable at each step and its independence from any specific dataset and software makes it an effective tool in analyzing existing genetic resources. The FunVar pipeline is available at https://github.com/lasigeBioTM/FunVar and includes pre and post processing steps to effectively interpret biological mechanisms of putative disease causing genetic variants.

Next-Generation Sequencing in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a common disorder that causes substantial distress. Heritability studies consistently show a strong genetic contribution, raising the hope that identifying ASD-associated genetic variants will offer insights into neurobiology and ultimately therapeutics. Next-generation sequencing (NGS) enabled the identification of disruptive variants throughout protein-coding regions of the genome. Alongside large cohorts and novel statistical methods, these NGS methods revolutionized ASD gene discovery. NGS methods have also contributed substantially to functional genetic data, such as gene expression, used to understand the neurobiological consequences of disrupting these ASD-associated genes. These functional data are also critical for annotating the noncoding genome as whole-genome sequencing (WGS) begins to provide initial insights outside of protein-coding regions. NGS methods still have a major role to play, as do similarly transformative advances in stem cell and gene-editing methods, in translating genetic discoveries into a first generation of ASD therapeutics.

The Autism-Associated Gene Scn2a Contributes to Dendritic Excitability and Synaptic Function in the Prefrontal Cortex

Autism spectrum disorder (ASD) is strongly associated with de novo gene mutations. One of the most commonly affected genes is SCN2A. ASD-associated SCN2A mutations impair the encoded protein Na_V1.2, a sodium channel important for action potential initiation and propagation in developing excitatory cortical neurons. The link between an axonal sodium channel and ASD, a disorder typically attributed to synaptic or transcriptional dysfunction, is unclear. Here we show that Na_V1.2 is unexpectedly critical for dendritic excitability and synaptic function in mature pyramidal neurons in addition to regulating early developmental axonal excitability. Na_V1.2 loss reduced action potential backpropagation into dendrites, impairing synaptic plasticity and synaptic strength, even when Na_V1.2 expression was disrupted in a cell-autonomous fashion late in development. These results reveal a novel dendritic function for Na_V1.2, providing insight into cellular mechanisms probably underlying circuit and behavioral dysfunction in ASD.

Gene expression imputation across multiple brain regions provides insights into schizophrenia risk

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

Rediscovering the value of families for psychiatric genetics research

As it is likely that both common and rare genetic variation are important for complex disease risk, studies that examine the full range of the allelic frequency distribution should be utilized to dissect the genetic influences on mental illness. The rate limiting factor for inferring an association between a variant and a phenotype is inevitably the total number of copies of the minor allele captured in the studied sample. For rare variation, with minor allele frequencies of 0.5% or less, very large samples of unrelated individuals are necessary to unambiguously associate a locus with an illness. Unfortunately, such large samples are often cost prohibitive. However, by using alternative analytic strategies and studying related individuals, particularly those from large multiplex families, it is possible to reduce the required sample size while maintaining statistical power. We contend that using whole genome sequence (WGS) in extended pedigrees provides a cost-effective strategy for psychiatric gene mapping that complements common variant approaches and WGS in unrelated individuals. This was our impetus for forming the "Pedigree-Based Whole Genome Sequencing of Affective and Psychotic Disorders" consortium. In this review, we provide a rationale for the use of WGS with pedigrees in modern psychiatric genetics research. We begin with a focused review of the current literature, followed by a short history of family-based research in psychiatry. Next, we describe several advantages of pedigrees for WGS research, including power estimates, methods for studying the environment, and endophenotypes. We conclude with a brief description of our consortium and its goals.

Cross-species models of attention-deficit/hyperactivity disorder and autism spectrum disorder: lessons from CNTNAP2, ADGRL3, and PARK2

Animal and cellular models are essential tools for all areas of biological research including neuroscience. Model systems can also be used to investigate the pathophysiology of psychiatric disorders such as attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). In this review, we provide a summary of animal and cellular models for three genes linked to ADHD and ASD in human patients - CNTNAP2, ADGRL3, and PARK2. We also highlight the strengths and weaknesses of each model system. By bringing together behavioral and neurobiological data, we demonstrate how a cross-species approach can provide integrated insights into gene function and the pathogenesis of ADHD and ASD. The knowledge gained from transgenic models will be essential to discover and validate new treatment targets for these disorders.

The Role of De Novo Noncoding Regulatory Mutations in Neurodevelopmental Disorders

Advances in sequencing technology have significantly expanded our understanding of the genetics of autism and neurodevelopmental disorders (NDDs). Continued technological improvements and cost reductions have now shifted the focus to investigations into the functional noncoding portions of the genome. There is a patient trend toward an excess of de novo and potentially disruptive mutations among conserved noncoding sequences implicated in the regulation of genes. The signals become stronger when restricted to genes already implicated in NDDs, but de novo mutation in such elements is estimated to account for <5% of patients. Larger sample sizes, improved variant detection, functional testing, and better approaches to classify noncoding variation will be required to identify specific pathogenic variants underlying disease.

Neurodevelopmental disease genes implicated by de novo mutation and copy number variation morbidity

We combined de novo mutation (DNM) data from 10,927 individuals with developmental delay and autism to identify 253 candidate neurodevelopmental disease genes with an excess of missense and/or likely gene-disruptive (LGD) mutations. Of these genes, 124 reach exome-wide significance (P < 5 × 10^-7) for DNM. Intersecting these results with copy number variation (CNV) morbidity data shows an enrichment for genomic disorder regions (30/253, likelihood ratio (LR) +1.85, P = 0.0017). We identify genes with an excess of missense DNMs overlapping deletion syndromes (for example, KIF1A and the 2q37 deletion) as well as duplication syndromes, such as recurrent MAPK3 missense mutations within the chromosome 16p11.2 duplication, recurrent CHD4 missense DNMs in the 12p13 duplication region, and recurrent WDFY4 missense DNMs in the 10q11.23 duplication region. Network analyses of genes showing an excess of DNMs highlights functional networks, including cell-specific enrichments in the D1⁺ and D2⁺ spiny neurons of the striatum.

Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder

Most genetic risk for psychiatric disease lies in regulatory regions, implicating pathogenic dysregulation of gene expression and splicing. However, comprehensive assessments of transcriptomic organization in diseased brains are limited. In this work, we integrated genotypes and RNA sequencing in brain samples from 1695 individuals with autism spectrum disorder (ASD), schizophrenia, and bipolar disorder, as well as controls. More than 25% of the transcriptome exhibits differential splicing or expression, with isoform-level changes capturing the largest disease effects and genetic enrichments. Coexpression networks isolate disease-specific neuronal alterations, as well as microglial, astrocyte, and interferon-response modules defining previously unidentified neural-immune mechanisms. We integrated genetic and genomic data to perform a transcriptome-wide association study, prioritizing disease loci likely mediated by cis effects on brain expression. This transcriptome-wide characterization of the molecular pathology across three major psychiatric disorders provides a comprehensive resource for mechanistic insight and therapeutic development.

Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology

Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learning methods are widely used to identify these markers, but their performance is highly dependent upon the size and quality of available data. In this study, we demonstrated that machine learning classifiers trained on gene functional similarities, using Gene Ontology (GO), can improve the identification of genes involved in complex diseases. For this purpose, we developed a supervised machine learning methodology to predict complex disease genes. The proposed pipeline was assessed using Autism Spectrum Disorder (ASD) candidate genes. A quantitative measure of gene functional similarities was obtained by employing different semantic similarity measures. To infer the hidden functional similarities between ASD genes, various types of machine learning classifiers were built on quantitative semantic similarity matrices of ASD and non-ASD genes. The classifiers trained and tested on ASD and non-ASD gene functional similarities outperformed previously reported ASD classifiers. For example, a Random Forest (RF) classifier achieved an AUC of 0. 80 for predicting new ASD genes, which was higher than the reported classifier (0.73). Additionally, this classifier was able to predict 73 novel ASD candidate genes that were enriched for core ASD phenotypes, such as autism and obsessive-compulsive behavior. In addition, predicted genes were also enriched for ASD co-occurring conditions, including Attention Deficit Hyperactivity Disorder (ADHD). We also developed a KNIME workflow with the proposed methodology which allows users to configure and execute it without requiring machine learning and programming skills. Machine learning is an effective and reliable technique to decipher ASD mechanism by identifying novel disease genes, but this study further demonstrated that their performance can be improved by incorporating a quantitative measure of gene functional similarities. Source code and the workflow of the proposed methodology are available at https://github.com/Muh-Asif/ASD-genes-prediction.

Maternal immune activation, central nervous system development and behavioral phenotypes

Maternal immune activation (MIA) refers to a maternal immune system triggered by infectious or infectious-like stimuli. A cascade of cytokines and immunologic alterations are transmitted to the fetus, resulting in adverse phenotypes most notably in the central nervous system. Epidemiologic studies implicate maternal infections in a variety of neuropsychiatric disorders, most commonly autism spectrum disorders and schizophrenia. In animal models, MIA causes neurochemical and anatomic changes in the brain that correspond to those found in humans with the disorders. As our understanding of the interactions between environment, genetics, and immune system grows, the role of alternative, noninfectious risk factors, such as prenatal stress, obesity, and the gut microbiome also becomes clearer. This review considers how infectious and noninfectious etiologies activate the maternal immune system. Their impact on fetal programming and neuropsychiatric disorders in offspring is examined in the context of human and animal studies.

The Wnt Signaling Pathway and Related Therapeutic Drugs in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a series of neurodevelopmental disorder with a large genetic component. However, the pathogenic genes and molecular mechanisms of ASD have not been clearly defined. Recent technological advancements, such as next-generation sequencing, have led to the identification of certain loci that is responsible for the pathophysiology of ASD. Three functional pathways, such as chromatin remodeling, Wnt signaling and mitochondrial dysfunction are potentially involved in ASD. In this review, we will focus on recent studies of the involvement of Wnt signaling pathway components in ASD pathophysiology and related drugs used in ASD treatment.

Investigation of common, low-frequency and rare genome-wide variation in anorexia nervosa

Anorexia nervosa (AN) is a complex neuropsychiatric disorder presenting with dangerously low body weight, and a deep and persistent fear of gaining weight. To date, only one genome-wide significant locus associated with AN has been identified. We performed an exome-chip based genome-wide association studies (GWAS) in 2158 cases from nine populations of European origin and 15 485 ancestrally matched controls. Unlike previous studies, this GWAS also probed association in low-frequency and rare variants. Sixteen independent variants were taken forward for in silico and de novo replication (11 common and 5 rare). No findings reached genome-wide significance. Two notable common variants were identified: rs10791286, an intronic variant in OPCML (P=9.89 × 10-6), and rs7700147, an intergenic variant (P=2.93 × 10-5). No low-frequency variant associations were identified at genome-wide significance, although the study was well-powered to detect low-frequency variants with large effect sizes, suggesting that there may be no AN loci in this genomic search space with large effect sizes.

Gene regulatory mechanisms underlying sex differences in brain development and psychiatric disease

The sexual differentiation of the mammalian nervous system requires the precise coordination of the temporal and spatial regulation of gene expression in diverse cell types. Sex hormones act at multiple developmental time points to specify sex-typical differentiation during embryonic and early development and to coordinate subsequent responses to gonadal hormones later in life by establishing sex-typical patterns of epigenetic modifications across the genome. Thus, mutations associated with neuropsychiatric conditions may result in sexually dimorphic symptoms by acting on different neural substrates or chromatin landscapes in males and females. Finally, as stress hormone signaling may directly alter the molecular machinery that interacts with sex hormone receptors to regulate gene expression, the contribution of chronic stress to the pathogenesis or presentation of mental illness may be additionally different between the sexes. Here, we review the mechanisms that contribute to sexual differentiation in the mammalian nervous system and consider some of the implications of these processes for sex differences in neuropsychiatric conditions.

Frontal lobe metabolic alterations in autism spectrum disorder: a 1H-magnetic resonance spectroscopy study

PURPOSE

Recently, neuroimaging studies were performed using ¹H-magnetic resonance spectroscopy (¹H-MRS), revealing a quantitative alteration of neurochemicals (such as neurotransmitters and metabolites) in several brain regions of patients with autism spectrum disorder (ASD). The involvement of the frontal lobe in the neurobiology of ASD has long been documented in the literature. Therefore, the aim of this study was to analyze the alterations of N-acetylaspartate/creatine (NAA/Cr) and choline/Cr (Cho/Cr) ratios in the frontal lobe subcortical white matter (WM) in ASD patients, in order to reveal any alteration of metabolites that might be the expression of specific clinical features of the disorder.

PATIENTS AND METHODS

An ¹H-MRS study of the frontal lobe subcortical WM was performed in 75 children with ASD and in 50 age-matched controls to evaluate the functional activity of this brain region.

RESULTS

NAA/Cr and Cho/Cr ratios were significantly altered in ASD, compared to control subjects. Moreover, in the ASD group, NAA/Cr was significantly lower in patients with a cognitive impairment.

CONCLUSION

Results from this study confirm the existence of brain metabolites' alterations in frontal lobe WM in children with ASD, supporting the relevance of this brain region in the clinical expressions of this disorder, including its role in the cognitive impairment. Further ¹H-MRS investigations will allow to comprehensively explain the relationship between metabolic alteration in a specific brain region and specific clinical features of ASD.

Whole genome sequencing in psychiatric disorders: the WGSPD consortium

As technology advances, whole genome sequencing (WGS) is likely to supersede other genotyping technologies. The rate of this change depends on its relative cost and utility. Variants identified uniquely through WGS may reveal novel biological pathways underlying complex disorders and provide high-resolution insight into when, where, and in which cell type these pathways are affected. Alternatively, cheaper and less computationally intensive approaches may yield equivalent insights. Understanding the role of rare variants in the noncoding gene-regulating genome, through pilot WGS projects, will be critical to determine which of these two extremes best represents reality. With large cohorts, well-defined risk loci, and a compelling need to understand the underlying biology, psychiatric disorders have a role to play in this preliminary WGS assessment. The WGSPD consortium will integrate data for 18,000 individuals with psychiatric disorders, beginning with autism spectrum disorder, schizophrenia, bipolar disorder, and major depressive disorder, along with over 150,000 controls.

Recurrent de novo mutations in neurodevelopmental disorders: properties and clinical implications

Next-generation sequencing (NGS) is now more accessible to clinicians and researchers. As a result, our understanding of the genetics of neurodevelopmental disorders (NDDs) has rapidly advanced over the past few years. NGS has led to the discovery of new NDD genes with an excess of recurrent de novo mutations (DNMs) when compared to controls. Development of large-scale databases of normal and disease variation has given rise to metrics exploring the relative tolerance of individual genes to human mutation. Genetic etiology and diagnosis rates have improved, which have led to the discovery of new pathways and tissue types relevant to NDDs. In this review, we highlight several key findings based on the discovery of recurrent DNMs ranging from copy number variants to point mutations. We explore biases and patterns of DNM enrichment and the role of mosaicism and secondary mutations in variable expressivity. We discuss the benefit of whole-genome sequencing (WGS) over whole-exome sequencing (WES) to understand more complex, multifactorial cases of NDD and explain how this improved understanding aids diagnosis and management of these disorders. Comprehensive assessment of the DNM landscape across the genome using WGS and other technologies will lead to the development of novel functional and bioinformatics approaches to interpret DNMs and drive new insights into NDD biology.

Early development of infants with neurofibromatosis type 1: a case series

Background

Prospective studies of infants at familial risk for autism spectrum disorder (ASD) have yielded insights into the earliest signs of the disorder but represent heterogeneous samples of unclear aetiology. Complementing this approach by studying cohorts of infants with monogenic syndromes associated with high rates of ASD offers the opportunity to elucidate the factors that lead to ASD.

Methods

We present the first report from a prospective study of ten 10-month-old infants with neurofibromatosis type 1 (NF1), a monogenic disorder with high prevalence of ASD or ASD symptomatology. We compared data from infants with NF1 to a large cohort of infants at familial risk for ASD, separated by outcome at age 3 of ASD (n = 34), atypical development (n = 44), or typical development (n = 89), and low-risk controls (n = 75). Domains assessed at 10 months by parent report and examiner observation include cognitive and adaptive function, sensory processing, social engagement, and temperament.

Results

Infants with NF1 showed striking impairments in motor functioning relative to low-risk infants; this pattern was seen in infants with later ASD from the familial cohort (HR-ASD). Both infants with NF1 and the HR-ASD group showed communication delays relative to low-risk infants.

Conclusions

Ten-month-old infants with NF1 show a range of developmental difficulties that were particularly striking in motor and communication domains. As with HR-ASD infants, social skills at this age were not notably impaired. This is some of the first information on early neurodevelopment in NF1. Strong inferences are limited by the sample size, but the findings suggest implications for early comparative developmental science and highlight motor functioning as an important domain to inform the development of relevant animal models. The findings have clinical implications in indicating an important focus for early surveillance and remediation in this early diagnosed genetic disorder.

ATP-dependent chromatin remodeling during mammalian development

Precise gene expression ensures proper stem and progenitor cell differentiation, lineage commitment and organogenesis during mammalian development. ATP-dependent chromatin-remodeling complexes utilize the energy from ATP hydrolysis to reorganize chromatin and, hence, regulate gene expression. These complexes contain diverse subunits that together provide a multitude of functions, from early embryogenesis through cell differentiation and development into various adult tissues. Here, we review the functions of chromatin remodelers and their different subunits during mammalian development. We discuss the mechanisms by which chromatin remodelers function and highlight their specificities during mammalian cell differentiation and organogenesis.

Opposing Effects on NaV1.2 Function Underlie Differences Between SCN2A Variants Observed in Individuals With Autism Spectrum Disorder or Infantile Seizures

BACKGROUND

Variants in the SCN2A gene that disrupt the encoded neuronal sodium channel NaV1.2 are important risk factors for autism spectrum disorder (ASD), developmental delay, and infantile seizures. Variants observed in infantile seizures are predominantly missense, leading to a gain of function and increased neuronal excitability. How variants associated with ASD affect NaV1.2 function and neuronal excitability are unclear.

METHODS

We examined the properties of 11 ASD-associated SCN2A variants in heterologous expression systems using whole-cell voltage-clamp electrophysiology and immunohistochemistry. Resultant data were incorporated into computational models of developing and mature cortical pyramidal cells that express NaV1.2.

RESULTS

In contrast to gain of function variants that contribute to seizure, we found that all ASD-associated variants dampened or eliminated channel function. Incorporating these electrophysiological results into a compartmental model of developing excitatory neurons demonstrated that all ASD variants, regardless of their mechanism of action, resulted in deficits in neuronal excitability. Corresponding analysis of mature neurons predicted minimal change in neuronal excitability.

CONCLUSIONS

This functional characterization thus identifies SCN2A mutation and NaV1.2 dysfunction as the most frequently observed ASD risk factor detectable by exome sequencing and suggests that associated changes in neuronal excitability, particularly in developing neurons, may contribute to ASD etiology.