Integrated systems analysis reveals a molecular network underlying autism spectrum disorders

Parkinson's disease and schizophrenia interactomes contain temporally distinct gene clusters underlying comorbid mechanisms and unique disease processes

Genome-wide association studies suggest significant overlaps in Parkinson's disease (PD) and schizophrenia (SZ) risks, but the underlying mechanisms remain elusive. The protein-protein interaction network ('interactome') plays a crucial role in PD and SZ and can incorporate their spatiotemporal specificities. Therefore, to study the linked biology of PD and SZ, we compiled PD- and SZ-associated genes from the DisGeNET database, and constructed their interactomes using BioGRID and HPRD. We examined the interactomes using clustering and enrichment analyses, in conjunction with the transcriptomic data of 26 brain regions spanning foetal stages to adulthood available in the BrainSpan Atlas. PD and SZ interactomes formed four gene clusters with distinct temporal identities (Disease Gene Networks or 'DGNs'1-4). DGN1 had unique SZ interactome genes highly expressed across developmental stages, corresponding to a neurodevelopmental SZ subtype. DGN2, containing unique SZ interactome genes expressed from early infancy to adulthood, correlated with an inflammation-driven SZ subtype and adult SZ risk. DGN3 contained unique PD interactome genes expressed in late infancy, early and late childhood, and adulthood, and involved in mitochondrial pathways. DGN4, containing prenatally-expressed genes common to both the interactomes, involved in stem cell pluripotency and overlapping with the interactome of 22q11 deletion syndrome (comorbid psychosis and Parkinsonism), potentially regulates neurodevelopmental mechanisms in PD-SZ comorbidity. Our findings suggest that disrupted neurodevelopment (regulated by DGN4) could expose risk windows in PD and SZ, later elevating disease risk through inflammation (DGN2). Alternatively, variant clustering in DGNs may produce disease subtypes, e.g., PD-SZ comorbidity with DGN4, and early/late-onset SZ with DGN1/DGN2.

Focal adhesion is associated with lithium response in bipolar disorder: evidence from a network-based multi-omics analysis

Lithium (Li) is one of the most effective drugs for treating bipolar disorder (BD), however, there is presently no way to predict response to guide treatment. The aim of this study is to identify functional genes and pathways that distinguish BD Li responders (LR) from BD Li non-responders (NR). An initial Pharmacogenomics of Bipolar Disorder study (PGBD) GWAS of lithium response did not provide any significant results. As a result, we then employed network-based integrative analysis of transcriptomic and genomic data. In transcriptomic study of iPSC-derived neurons, 41 significantly differentially expressed (DE) genes were identified in LR vs NR regardless of lithium exposure. In the PGBD, post-GWAS gene prioritization using the GWA-boosting (GWAB) approach identified 1119 candidate genes. Following DE-derived network propagation, there was a highly significant overlap of genes between the top 500- and top 2000-proximal gene networks and the GWAB gene list (Phypergeometric = 1.28E-09 and 4.10E-18, respectively). Functional enrichment analyses of the top 500 proximal network genes identified focal adhesion and the extracellular matrix (ECM) as the most significant functions. Our findings suggest that the difference between LR and NR was a much greater effect than that of lithium. The direct impact of dysregulation of focal adhesion on axon guidance and neuronal circuits could underpin mechanisms of response to lithium, as well as underlying BD. It also highlights the power of integrative multi-omics analysis of transcriptomic and genomic profiling to gain molecular insights into lithium response in BD.

UBE3A: The Role in Autism Spectrum Disorders (ASDs) and a Potential Candidate for Biomarker Studies and Designing Therapeutic Strategies

Published reports from the CDC's Autism and Development Disabilities Monitoring Networks have shown that an average of 1 in every 44 (2.3%) 8-year-old children were estimated to have ASD in 2018. Many of the ASDs exhibiting varying degrees of autism-like phenotypes have chromosomal anomalies in the Chr15q11-q13 region. Numerous potential candidate genes linked with ASD reside in this chromosomal segment. However, several clinical, in vivo, and in vitro studies selected one gene more frequently than others randomly and unbiasedly. This gene codes for UBE3A or Ubiquitin protein ligase E3A [also known as E6AP ubiquitin-protein ligase (E6AP)], an enzyme involved in the cellular degradation of proteins. This gene has been listed as one of the several genes with a high potential of causing ASD in the Autism Database. The gain of function mutations, triplication, or duplication in the UBE3A gene is also associated with ASDs like Angelman Syndrome (AS) and Dup15q Syndrome. The genetic imprinting of UBE3A in the brain and a preference for neuronal maternal-specific expression are the key features of various ASDs. Since the UBE3A gene is involved in two main important diseases associated with autism-like symptoms, there has been widespread research going on in understanding the link between this gene and autism. Additionally, since no universal methodology or mechanism exists for identifying UBE3A-mediated ASD, it continues to be challenging for neurobiologists, neuroscientists, and clinicians to design therapies or diagnostic tools. In this review, we focus on the structure and functional aspects of the UBE3A protein, discuss the primary relevance of the 15q11-q13 region in the cause of ASDs, and highlight the link between UBE3A and ASD. We try to broaden the knowledge of our readers by elaborating on the possible mechanisms underlying UBE3A-mediated ASDs, emphasizing the usage of UBE3A as a prospective biomarker in the preclinical diagnosis of ASDs and discuss the positive outcomes, advanced developments, and the hurdles in the field of therapeutic strategies against UBE3A-mediated ASDs. This review is novel as it lays a very detailed and comprehensive platform for one of the most important genes associated with diseases showing autistic-like symptoms. Additionally, this review also attempts to lay optimistic feedback on the possible steps for the diagnosis, prevention, and therapy of these UBE3A-mediated ASDs in the upcoming years.

Spatiotemporal expression patterns of anxiety disorder-associated genes

Anxiety disorders (ADs) are the most common form of mental disorder that affects millions of individuals worldwide. Although physiological studies have revealed the neural circuits related to AD symptoms, how AD-associated genes are spatiotemporally expressed in the human brain still remains unclear. In this study, we integrated genome-wide association studies of four human AD subtypes-generalized anxiety disorder, social anxiety disorder, panic disorder, and obsessive-compulsive disorder-with spatial gene expression patterns. Our investigation uncovered a novel division among AD-associated genes, marked by significant and distinct expression enrichments in the cerebral nuclei, limbic, and midbrain regions. Each gene cluster was associated with specific anxiety-related behaviors, signaling pathways, region-specific gene networks, and cell types. Notably, we observed a significant negative correlation in the temporal expression patterns of these gene clusters during various developmental stages. Moreover, the specific brain regions enriched in each gene group aligned with neural circuits previously associated with negative decision-making and anxious temperament. These results suggest that the two distinct gene clusters may underlie separate neural systems involved in anxiety. As a result, our findings bridge the gap between genes and neural circuitry, shedding light on the mechanisms underlying AD-associated behaviors.

An in-depth comparison of linear and non-linear joint embedding methods for bulk and single-cell multi-omics

Multi-omic analyses are necessary to understand the complex biological processes taking place at the tissue and cell level, but also to make reliable predictions about, for example, disease outcome. Several linear methods exist that create a joint embedding using paired information per sample, but recently there has been a rise in the popularity of neural architectures that embed paired -omics into the same non-linear manifold. This work describes a head-to-head comparison of linear and non-linear joint embedding methods using both bulk and single-cell multi-modal datasets. We found that non-linear methods have a clear advantage with respect to linear ones for missing modality imputation. Performance comparisons in the downstream tasks of survival analysis for bulk tumor data and cell type classification for single-cell data lead to the following insights: First, concatenating the principal components of each modality is a competitive baseline and hard to beat if all modalities are available at test time. However, if we only have one modality available at test time, training a predictive model on the joint space of that modality can lead to performance improvements with respect to just using the unimodal principal components. Second, -omic profiles imputed by neural joint embedding methods are realistic enough to be used by a classifier trained on real data with limited performance drops. Taken together, our comparisons give hints to which joint embedding to use for which downstream task. Overall, product-of-experts performed well in most tasks and was reasonably fast, while early integration (concatenation) of modalities did quite poorly.

The Potential Usefulness of the Expanded Carrier Screening to Identify Hereditary Genetic Diseases: A Case Report from Real-World Data

Expanded carrier screening (ECS) means a comprehensive genetic analysis to evaluate an individual's carrier status. ECS is becoming more frequently used, thanks to the availability of techniques such as next generation sequencing (NGS) and array comparative genomic hybridization (aCGH), allowing for extensive genome-scale analyses. Here, we report the case of a couple who underwent ECS for a case of autism spectrum disorder in the male partner family. aCGH and whole-exome sequencing (WES) were performed in the couple. aCGH analysis identified in the female partner two deletions involving genes associated to behavioral and neurodevelopment disorders. No clinically relevant alterations were identified in the husband. Interestingly, WES analysis identified in the male partner a pathogenic variant in the LPL gene that is emerging as a novel candidate gene for autism. This case shows that ECS may be useful in clinical contexts, especially when both the partners are analyzed before conception, thus allowing the estimation of their risk to transmit an inherited condition. On the other side, there are several concerns related to possible incidental findings and difficult-to-interpret results. Once these limits are defined by the establishment of specific guidelines, ECS may have a greater diffusion.

Glutaminase 1 deficiency confined in forebrain neurons causes autism spectrum disorder-like behaviors

An abnormal glutamate signaling pathway has been proposed in the mechanisms of autism spectrum disorder (ASD). However, less is known about the involvement of alterations of glutaminase 1 (GLS1) in the pathophysiology of ASD. We show that the transcript level of GLS1 is significantly decreased in the postmortem frontal cortex and peripheral blood of ASD subjects. Mice lacking Gls1 in CamKIIα-positive neurons display a series of ASD-like behaviors, synaptic excitatory and inhibitory (E/I) imbalance, higher spine density, and glutamate receptor expression in the prefrontal cortex, as well as a compromised expression pattern of genes involved in synapse pruning and less engulfed synaptic puncta in microglia. A low dose of lipopolysaccharide treatment restores microglial synapse pruning, corrects synaptic neurotransmission, and rescues behavioral deficits in these mice. In summary, these findings provide mechanistic insights into Gls1 loss in ASD symptoms and identify Gls1 as a target for the treatment of ASD.

Autism Spectrum Disorder: A Neuro-Immunometabolic Hypothesis of the Developmental Origins

Fetal neuroinflammation and prenatal stress (PS) may contribute to lifelong neurological disabilities. Astrocytes and microglia, among the brain's non-neuronal "glia" cell populations, play a pivotal role in neurodevelopment and predisposition to and initiation of disease throughout lifespan. One of the most common neurodevelopmental disorders manifesting between 1-4 years of age is the autism spectrum disorder (ASD). A pathological glial-neuronal interplay is thought to increase the risk for clinical manifestation of ASD in at-risk children, but the mechanisms remain poorly understood, and integrative, multi-scale models are needed. We propose a model that integrates the data across the scales of physiological organization, from genome to phenotype, and provides a foundation to explain the disparate findings on the genomic level. We hypothesize that via gene-environment interactions, fetal neuroinflammation and PS may reprogram glial immunometabolic phenotypes that impact neurodevelopment and neurobehavior. Drawing on genomic data from the recently published series of ovine and rodent glial transcriptome analyses with fetuses exposed to neuroinflammation or PS, we conducted an analysis on the Simons Foundation Autism Research Initiative (SFARI) Gene database. We confirmed 21 gene hits. Using unsupervised statistical network analysis, we then identified six clusters of probable protein-protein interactions mapping onto the immunometabolic and stress response networks and epigenetic memory. These findings support our hypothesis. We discuss the implications for ASD etiology, early detection, and novel therapeutic approaches. We conclude with delineation of the next steps to verify our model on the individual gene level in an assumption-free manner. The proposed model is of interest for the multidisciplinary community of stakeholders engaged in ASD research, the development of novel pharmacological and non-pharmacological treatments, early prevention, and detection as well as for policy makers.

Comparing synaptic proteomes across five mouse models for autism reveals converging molecular similarities including deficits in oxidative phosphorylation and Rho GTPase signaling

Specific and effective treatments for autism spectrum disorder (ASD) are lacking due to a poor understanding of disease mechanisms. Here we test the idea that similarities between diverse ASD mouse models are caused by deficits in common molecular pathways at neuronal synapses. To do this, we leverage the availability of multiple genetic models of ASD that exhibit shared synaptic and behavioral deficits and use quantitative mass spectrometry with isobaric tandem mass tagging (TMT) to compare their hippocampal synaptic proteomes. Comparative analyses of mouse models for Fragile X syndrome (Fmr1 knockout), cortical dysplasia focal epilepsy syndrome (Cntnap2 knockout), PTEN hamartoma tumor syndrome (Pten haploinsufficiency), ANKS1B syndrome (Anks1b haploinsufficiency), and idiopathic autism (BTBR+) revealed several common altered cellular and molecular pathways at the synapse, including changes in oxidative phosphorylation, and Rho family small GTPase signaling. Functional validation of one of these aberrant pathways, Rac1 signaling, confirms that the ANKS1B model displays altered Rac1 activity counter to that observed in other models, as predicted by the bioinformatic analyses. Overall similarity analyses reveal clusters of synaptic profiles, which may form the basis for molecular subtypes that explain genetic heterogeneity in ASD despite a common clinical diagnosis. Our results suggest that ASD-linked susceptibility genes ultimately converge on common signaling pathways regulating synaptic function and propose that these points of convergence are key to understanding the pathogenesis of this disorder.

SAPAP Scaffold Proteins: From Synaptic Function to Neuropsychiatric Disorders

Excitatory (glutamatergic) synaptic transmission underlies many aspects of brain activity and the genesis of normal human behavior. The postsynaptic scaffolding proteins SAP90/PSD-95-associated proteins (SAPAPs), which are abundant components of the postsynaptic density (PSD) at excitatory synapses, play critical roles in synaptic structure, formation, development, plasticity, and signaling. The convergence of human genetic data with recent in vitro and in vivo animal model data indicates that mutations in the genes encoding SAPAP1-4 are associated with neurological and psychiatric disorders, and that dysfunction of SAPAP scaffolding proteins may contribute to the pathogenesis of various neuropsychiatric disorders, such as schizophrenia, autism spectrum disorders, obsessive compulsive disorders, Alzheimer's disease, and bipolar disorder. Here, we review recent major genetic, epigenetic, molecular, behavioral, electrophysiological, and circuitry studies that have advanced our knowledge by clarifying the roles of SAPAP proteins at the synapses, providing new insights into the mechanistic links to neurodevelopmental and neuropsychiatric disorders.

Systems analysis of de novo mutations in congenital heart diseases identified a protein network in the hypoplastic left heart syndrome

Despite a strong genetic component, only a few genes have been identified in congenital heart diseases (CHDs). We introduced systems analyses to uncover the hidden organization on biological networks of mutations in CHDs and leveraged network analysis to integrate the protein interactome, patient exomes, and single-cell transcriptomes of the developing heart. We identified a CHD network regulating heart development and observed that a sub-network also regulates fetal brain development, thereby providing mechanistic insights into the clinical comorbidities between CHDs and neurodevelopmental conditions. At a small scale, we experimentally verified uncharacterized cardiac functions of several proteins. At a global scale, our study revealed developmental dynamics of the network and observed its association with the hypoplastic left heart syndrome (HLHS), which was further supported by the dysregulation of the network in HLHS endothelial cells. Overall, our work identified previously uncharacterized CHD factors and provided a generalizable framework applicable to studying many other complex diseases. A record of this paper's Transparent Peer Review process is included in the supplemental information.

DEMA: a distance-bounded energy-field minimization algorithm to model and layout biomolecular networks with quantitative features

SUMMARY

In biology, graph layout algorithms can reveal comprehensive biological contexts by visually positioning graph nodes in their relevant neighborhoods. A layout software algorithm/engine commonly takes a set of nodes and edges and produces layout coordinates of nodes according to edge constraints. However, current layout engines normally do not consider node, edge or node-set properties during layout and only curate these properties after the layout is created. Here, we propose a new layout algorithm, distance-bounded energy-field minimization algorithm (DEMA), to natively consider various biological factors, i.e., the strength of gene-to-gene association, the gene's relative contribution weight and the functional groups of genes, to enhance the interpretation of complex network graphs. In DEMA, we introduce a parameterized energy model where nodes are repelled by the network topology and attracted by a few biological factors, i.e., interaction coefficient, effect coefficient and fold change of gene expression. We generalize these factors as gene weights, protein-protein interaction weights, gene-to-gene correlations and the gene set annotations-four parameterized functional properties used in DEMA. Moreover, DEMA considers further attraction/repulsion/grouping coefficient to enable different preferences in generating network views. Applying DEMA, we performed two case studies using genetic data in autism spectrum disorder and Alzheimer's disease, respectively, for gene candidate discovery. Furthermore, we implement our algorithm as a plugin to Cytoscape, an open-source software platform for visualizing networks; hence, it is convenient. Our software and demo can be freely accessed at http://discovery.informatics.uab.edu/dema.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Downregulation of TGIF2 is possibly correlated with neuronal apoptosis and autism-like symptoms in mice

BACKGROUND

TGFB-induced factor homeobox 2 (TGIF2) has been reported to exert essential functions in brain development. This study aimed to elucidate the correlation of TGIF2 with autism, a neurodevelopmental condition which presents with severe communication problems.

METHODS

An autism-related gene expression dataset GSE36315 was used to analyze aberrantly expressed genes in autistic brain tissues. Maternal mice were treated with valproate (VPA), and their offspring were selected as model mice with autism. The functions of TGIF2 in autism-like symptoms in mice were examined by behavioral tests and histological examination of their hippocampal tissues. Mouse hippocampal neurons were extracted for in vitro studies. A gene set enrichment analysis was performed to analyze the signaling pathways involved, and the upstream factors influencing TGIF2 expression were explored in the ENCODE database and validated by ChIP-qPCR assays.

RESULTS

TGIF2 was poorly expressed in autistic patients in the GSE36315 dataset as well as in the temporal cortex tissues of autistic mice. Adenovirus-mediated overexpression of TGIF2 suppressed autism-like symptoms and neuronal apoptosis in autistic mice. TGIF2 activated the Wnt/β-catenin signaling pathway. TGIF2 could be regulated by monomethylation of histone H3 Lys4 (H3K4me1). The histone demethylase LSD1 was highly expressed in the tissues of autistic mice and bound to TGIF2 promoter, which was possibly responsible for TGIF2 downregulation.

CONCLUSION

This research suggests that the downregulation of TGIF2, possibly regulated by LSD1/H3K4me1, is correlated with neuronal apoptosis and development of autism in mice through the inactivation of the Wnt/β-catenin pathway.

The dark side of synaptic proteins in tumours

Research in the past decade has uncovered the essential role of the nervous system in the tumour microenvironment. The recent advances in cancer neuroscience, especially the discovery of neuron-tumour synaptic/perisynaptic structures, have revealed the dark side of synaptic proteins in the progression of brain tumours. Here, we provide an overview of the synaptic proteins expressed by tumour cells and analyse their molecular functions and organisation by comparing them with neuronal synaptic proteins. We focus on the studies of neuroligin-3, the glutamate receptors AMPAR and NMDAR and the synaptic scaffold protein DLGAP1, for their newly discovered regulatory role in the proliferation and progression of tumours. Progress in cancer neuroscience has brought novel insights into the treatment of cancers. In the last part of this review, we discuss the therapeutical strategies targeting synaptic proteins and the current challenges and possible toolkits regarding their clinical application in cancer treatment. Our understanding of cancer neuroscience is still in its infancy; deeper investigation of how tumour cells co-opt synaptic signaling will help fulfil the therapeutical potential of the synaptic proteins as promising anti-tumour targets.

Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases

Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the "big data" revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.

Association and epistatic analysis of white matter related genes across the continuum schizophrenia and autism spectrum disorders: The joint effect of NRG1-ErbB genes

BACKGROUND

Schizophrenia-spectrum disorders (SSD) and Autism spectrum disorders (ASD) are neurodevelopmental disorders that share clinical, cognitive, and genetic characteristics, as well as particular white matter (WM) abnormalities. In this study, we aimed to investigate the role of a set of oligodendrocyte/myelin-related (OMR) genes and their epistatic effect on the risk for SSD and ASD.

METHODS

We examined 108 SNPs in a set of 22 OMR genes in 1749 subjects divided into three independent samples (187 SSD trios, 915 SSD cases/control, and 91 ASD trios). Genetic association and gene-gene interaction analyses were conducted with PLINK and MB-MDR, and permutation procedures were implemented in both.

RESULTS

Some OMR genes showed an association trend with SSD, while after correction, the ones that remained significantly associated were MBP, ERBB3, and AKT1. Significant gene-gene interactions were found between (i) NRG1*MBP (perm p-value = 0.002) in the SSD trios sample, (ii) ERBB3*AKT1 (perm p-value = 0.001) in the SSD case-control sample, and (iii) ERBB3*QKI (perm p-value = 0.0006) in the ASD trios sample.

DISCUSSION

Our results suggest the implication of OMR genes in the risk for both SSD and ASD and highlight the role of NRG1 and ERBB genes. These findings are in line with the previous evidence and may suggest pathophysiological mechanisms related to NRG1/ERBBs signalling in these disorders.

Modestly increasing systemic interleukin-6 perinatally disturbs secondary germinal zone neurogenesis and gliogenesis and produces sociability deficits

Epidemiologic studies have demonstrated that infections during pregnancy increase the risk of offspring developing Schizophrenia, Autism, Depression and Bipolar Disorder and have implicated interleukin-6 (IL-6) as a causal agent. However, other cytokines have been associated with the developmental origins of psychiatric disorders; therefore, it remains to be established whether elevating IL-6 is sufficient to alter the trajectory of neural development. Furthermore, most rodent studies have manipulated the maternal immune system at mid-gestation, which affects the stem cells and progenitors in both the primary and secondary germinal matrices. Therefore, a question that remains to be addressed is whether elevating IL-6 when the secondary germinal matrices are most active will affect brain development. Here, we have increased IL-6 from postnatal days 3-6 when the secondary germinal matrices are rapidly expanding. Using Nestin-Cre^ERT2 fate mapping we show that this transient increase in IL-6 decreased neurogenesis in the dentate gyrus of the dorsal hippocampus, reduced astrogliogenesis in the amygdala and decreased oligodendrogenesis in the body and splenium of the corpus callosum all by ∼ 50%. Moreover, the IL-6 treatment elicited behavioral changes classically associated with neurodevelopmental disorders. As adults, IL-6 injected male mice lost social preference in the social approach test, spent ∼ 30% less time socially engaging with sexually receptive females and produced ∼ 50% fewer ultrasonic vocalizations during mating. They also engaged ∼ 50% more time in self-grooming behavior and had an increase in inhibitory avoidance. Altogether, these data provide new insights into the biological mechanisms linking perinatal immune activation to complex neurodevelopmental brain disorders.

Brain transcriptomic profiling reveals common alterations across neurodegenerative and psychiatric disorders

Neurodegenerative and neuropsychiatric disorders (ND-NPs) are multifactorial, polygenic and complex behavioral phenotypes caused by brain abnormalities. Large-scale collaborative efforts have tried to identify the genetic architecture of these conditions. However, the specific and shared underlying molecular pathobiology of brain illnesses is not clear. Here, we examine transcriptome-wide characterization of eight conditions, using a total of 2,633 post-mortem brain samples from patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), Progressive Supranuclear Palsy (PSP), Pathological Aging (PA), Autism Spectrum Disorder (ASD), Schizophrenia (Scz), Major Depressive Disorder (MDD), and Bipolar Disorder (BP)–in comparison with 2,078 brain samples from matched control subjects.

Similar transcriptome alterations were observed between NDs and NPs with the top correlations obtained between Scz-BP, ASD-PD, AD-PD, and Scz-ASD. Region-specific comparisons also revealed shared transcriptome alterations in frontal and temporal lobes across NPs and NDs. Co-expression network analysis identified coordinated dysregulations of cell-type-specific modules across NDs and NPs. This study provides a transcriptomic framework to understand the molecular alterations of NPs and NDs through their shared- and specific gene expression in the brain.

Network biology bridges the gaps between quantitative genetics and multi-omics to map complex diseases

With advances in high-throughput sequencing technologies, quantitative genetics approaches have provided insights into genetic basis of many complex diseases. Emerging in-depth multi-omics profiling technologies have created exciting opportunities for systematically investigating intricate interaction networks with different layers of biological molecules underlying disease etiology. Herein, we summarized two main categories of biological networks: evidence-based and statistically inferred. These different types of molecular networks complement each other at both bulk and single-cell levels. We also review three main strategies to incorporate quantitative genetics results with multi-omics data by network analysis: (a) network propagation, (b) functional module-based methods, (c) comparative/dynamic networks. These strategies not only aid in elucidating molecular mechanisms of complex diseases but can guide the search for therapeutic targets.

Single-cell transcriptome identifies molecular subtype of autism spectrum disorder impacted by de novo loss-of-function variants regulating glial cells

BACKGROUND

In recent years, several hundred autism spectrum disorder (ASD) implicated genes have been discovered impacting a wide range of molecular pathways. However, the molecular underpinning of ASD, particularly from the point of view of 'brain to behaviour' pathogenic mechanisms, remains largely unknown.

METHODS

We undertook a study to investigate patterns of spatiotemporal and cell type expression of ASD-implicated genes by integrating large-scale brain single-cell transcriptomes (> million cells) and de novo loss-of-function (LOF) ASD variants (impacting 852 genes from 40,122 cases).

RESULTS

We identified multiple single-cell clusters from three distinct developmental human brain regions (anterior cingulate cortex, middle temporal gyrus and primary visual cortex) that evidenced high evolutionary constraint through enrichment for brain critical exons and high pLI genes. These clusters also showed significant enrichment with ASD loss-of-function variant genes (p < 5.23 × 10^-11) that are transcriptionally highly active in prenatal brain regions (visual cortex and dorsolateral prefrontal cortex). Mapping ASD de novo LOF variant genes into large-scale human and mouse brain single-cell transcriptome analysis demonstrate enrichment of such genes into neuronal subtypes and are also enriched for subtype of non-neuronal glial cell types (astrocyte, p < 6.40 × 10^-11, oligodendrocyte, p < 1.31 × 10^-09).

CONCLUSION

Among the ASD genes enriched with pathogenic de novo LOF variants (i.e. KANK1, PLXNB1), a subgroup has restricted transcriptional regulation in non-neuronal cell types that are evolutionarily conserved. This association strongly suggests the involvement of subtype of non-neuronal glial cells in the pathogenesis of ASD and the need to explore other biological pathways for this disorder.

Sterol and lipid analyses identifies hypolipidemia and apolipoprotein disorders in autism associated with adaptive functioning deficits

An improved understanding of sterol and lipid abnormalities in individuals with autism spectrum disorder (ASD) could lead to personalized treatment approaches. Toward this end, in blood, we identified reduced synthesis of cholesterol in families with ≥2 children with ASD participating with the Autism Genetic Resource Exchange (AGRE), as well as reduced amounts of high-density lipoprotein cholesterol (HDL), apolipoprotein A1 (ApoA1) and apolipoprotein B (ApoB), with 19.9% of the subjects presenting with apolipoprotein patterns similar to hypolipidemic clinical syndromes and 30% with either or both ApoA1 and ApoB less than the fifth centile. Subjects with levels less than the fifth centile of HDL or ApoA1 or ApoA1 + ApoB had lower adaptive functioning than other individuals with ASD, and hypocholesterolemic subjects had apolipoprotein deficits significantly divergent from either typically developing individuals participating in National Institutes of Health or the National Health and Nutrition Examination Survey III.

Drug repositioning based on network-specific core genes identifies potential drugs for the treatment of autism spectrum disorder in children

Identification of exact causative genes is important for in silico drug repositioning based on drug-gene-disease relationships. However, the complex polygenic etiology of the autism spectrum disorder (ASD) is a challenge in the identification of etiological genes. The network-based core gene identification method can effectively use the interactions between genes and accurately identify the pathogenic genes of ASD. We developed a novel network-based drug repositioning framework that contains three steps: network-specific core gene (NCG) identification, potential therapeutic drug repositioning, and candidate drug validation. First, through the analysis of transcriptome data for 178 brain tissues, gene network analysis identified 365 NCGs in 18 coexpression modules that were significantly correlated with ASD. Second, we evaluated two proposed drug repositioning methods. In one novel approach (dtGSEA), we used the NCGs to probe drug-gene interaction data and identified 35 candidate drugs. In another approach, we compared NCG expression patterns with drug-induced transcriptome data from the Connectivity Map database and found 46 candidate drugs. Third, we validated the candidate drugs using an in-house mental diseases and compounds knowledge graph (MCKG) that contained 7509 compounds, 505 mental diseases, and 123,890 edges. We found a total of 42 candidate drugs that were associated with mental illness, among which 10 drugs (baclofen, sulpiride, estradiol, entinostat, everolimus, fluvoxamine, curcumin, calcitriol, metronidazole, and zinc) were postulated to be associated with ASD. This study proposes a powerful network-based drug repositioning framework and also provides candidate drugs as well as potential drug targets for the subsequent development of ASD therapeutic drugs.

An increased burden of rare exonic variants in NRXN1 microdeletion carriers is likely to enhance the penetrance for autism spectrum disorder

Autism spectrum disorder (ASD) is characterized by a complex polygenic background, but with the unique feature of a subset of cases (~15%‐30%) presenting a rare large‐effect variant. However, clinical interpretation in these cases is often complicated by incomplete penetrance, variable expressivity and different neurodevelopmental trajectories. NRXN1 intragenic deletions represent the prototype of such ASD‐associated susceptibility variants. From chromosomal microarrays analysis of 104 ASD individuals, we identified an inherited NRXN1 deletion in a trio family. We carried out whole‐exome sequencing and deep sequencing of mitochondrial DNA (mtDNA) in this family, to evaluate the burden of rare variants which may contribute to the phenotypic outcome in NRXN1 deletion carriers. We identified an increased burden of exonic rare variants in the ASD child compared to the unaffected NRXN1 deletion‐transmitting mother, which remains significant if we restrict the analysis to potentially deleterious rare variants only (P = 6.07 × 10⁻⁵). We also detected significant interaction enrichment among genes with damaging variants in the proband, suggesting that additional rare variants in interacting genes collectively contribute to cross the liability threshold for ASD. Finally, the proband's mtDNA presented five low‐level heteroplasmic mtDNA variants that were absent in the mother, and two maternally inherited variants with increased heteroplasmic load. This study underlines the importance of a comprehensive assessment of the genomic background in carriers of large‐effect variants, as penetrance modulation by additional interacting rare variants to might represent a widespread mechanism in neurodevelopmental disorders.

Integrated analysis of human transcriptome data for Rett syndrome finds a network of involved genes

OBJECTIVES

Rett syndrome (RTT) is a rare disorder causing severe intellectual and physical disability. The cause is a mutation in the gene coding for the methyl-CpG binding protein 2 (MECP2), a multifunctional regulator protein. Purpose of the study was integration and investigation of multiple gene expression profiles in human cells with impaired MECP2 gene to obtain a robust, data-driven insight in molecular disease mechanisms.

METHODS

Information about changed gene expression was extracted from five previously published studies, integrated and the resulting differentially expressed genes were analysed using overrepresentation analysis of biological pathways and gene ontology, and network analysis.

RESULTS

We identified a set of genes, which are significantly changed not in all but several transcriptomics datasets and were not mentioned in the context of RTT before. We found that these genes are involved in several processes and molecular pathways known to be affected in RTT. Integrating transcription factors we identified a possible link how MECP2 regulates cytoskeleton organisation via MEF2C and CAPG.

CONCLUSIONS

Integrative analysis of omics data and prior knowledge databases is a powerful approach to identify links between mutation and phenotype especially in rare disease research where little data is available.

A Machine Learning Approach to Predicting Autism Risk Genes: Validation of Known Genes and Discovery of New Candidates

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a strong genetic basis. The role of de novo mutations in ASD has been well established, but the set of genes implicated to date is still far from complete. The current study employs a machine learning-based approach to predict ASD risk genes using features from spatiotemporal gene expression patterns in human brain, gene-level constraint metrics, and other gene variation features. The genes identified through our prediction model were enriched for independent sets of ASD risk genes, and tended to be down-expressed in ASD brains, especially in frontal and parietal cortex. The highest-ranked genes not only included those with strong prior evidence for involvement in ASD (for example, NBEA, HERC1, and TCF20), but also indicated potentially novel candidates, such as, MYCBP2 and CAND1, which are involved in protein ubiquitination. We also showed that our method outperformed state-of-the-art scoring systems for ranking curated ASD candidate genes. Gene ontology enrichment analysis of our predicted risk genes revealed biological processes clearly relevant to ASD, including neuronal signaling, neurogenesis, and chromatin remodeling, but also highlighted other potential mechanisms that might underlie ASD, such as regulation of RNA alternative splicing and ubiquitination pathway related to protein degradation. Our study demonstrates that human brain spatiotemporal gene expression patterns and gene-level constraint metrics can help predict ASD risk genes. Our gene ranking system provides a useful resource for prioritizing ASD candidate genes.

Homozygous deletions implicate non-coding epigenetic marks in Autism spectrum disorder

More than 98% of the human genome is made up of non-coding DNA, but techniques to ascertain its contribution to human disease have lagged far behind our understanding of protein coding variations. Autism spectrum disorder (ASD) has been mostly associated with coding variations via de novo single nucleotide variants (SNVs), recessive/homozygous SNVs, or de novo copy number variants (CNVs); however, most ASD cases continue to lack a genetic diagnosis. We analyzed 187 consanguineous ASD families for biallelic CNVs. Recessive deletions were significantly enriched in affected individuals relative to their unaffected siblings (17% versus 4%, p < 0.001). Only a small subset of biallelic deletions were predicted to result in coding exon disruption. In contrast, biallelic deletions in individuals with ASD were enriched for overlap with regulatory regions, with 23/28 CNVs disrupting histone peaks in ENCODE (p < 0.009). Overlap with regulatory regions was further demonstrated by comparisons to the 127-epigenome dataset released by the Roadmap Epigenomics project, with enrichment for enhancers found in primary brain tissue and neuronal progenitor cells. Our results suggest a novel noncoding mechanism of ASD, describe a powerful method to identify important noncoding regions in the human genome, and emphasize the potential significance of gene activation and regulation in cognitive and social function.

A multidimensional precision medicine approach identifies an autism subtype characterized by dyslipidemia

The promise of precision medicine lies in data diversity. More than the sheer size of biomedical data, it is the layering of multiple data modalities, offering complementary perspectives, that is thought to enable the identification of patient subgroups with shared pathophysiology. In the present study, we use autism to test this notion. By combining healthcare claims, electronic health records, familial whole-exome sequences and neurodevelopmental gene expression patterns, we identified a subgroup of patients with dyslipidemia-associated autism.

VLDL-specific increases of fatty acids in autism spectrum disorder correlate with social interaction

BACKGROUND

Abnormalities of lipid metabolism contributing to the autism spectrum disorder (ASD) pathogenesis have been suggested, but the mechanisms are not fully understood. We aimed to characterize the lipid metabolism in ASD and to explore a biomarker for clinical evaluation.

METHODS

An age-matched case-control study was designed. Lipidomics was conducted using the plasma samples from 30 children with ASD compared to 30 typical developmental control (TD) children. Large-scale lipoprotein analyses were also conducted using the serum samples from 152 children with ASD compared to 122 TD children. Data comparing ASD to TD subjects were evaluated using univariate (Mann-Whitney test) and multivariate analyses (conditional logistic regression analysis) for main analyses using cofounders (diagnosis, sex, age, height, weight, and BMI), Spearman rank correlation coefficient, and discriminant analyses.

FINDINGS

Forty-eight significant metabolites involved in lipid biosynthesis and metabolism, oxidative stress, and synaptic function were identified in the plasma of ASD children by lipidomics. Among these, increased fatty acids (FAs), such as omega-3 (n-3) and omega-6 (n-6), showed correlations with clinical social interaction score and ASD diagnosis. Specific reductions of very-low-density lipoprotein (VLDL) and apoprotein B (APOB) in serum of ASD children also were found by large-scale lipoprotein analysis. VLDL-specific reduction in ASD was correlated with APOB, indicating VLDL-specific dyslipidaemia associated with APOB in ASD children.

INTERPRETATION

Our results demonstrated that the increases in FAs correlated positively with social interaction are due to VLDL-specific degradation, providing novel insights into the lipid metabolism underlying ASD pathophysiology.

FUNDING

This study was supported mainly by MEXT, Japan.

Quantitative genome-wide association study of six phenotypic subdomains identifies novel genome-wide significant variants in autism spectrum disorder

Autism spectrum disorders (ASD) are highly heritable and are characterized by deficits in social communication and restricted and repetitive behaviors. Twin studies on phenotypic subdomains suggest a differing underlying genetic etiology. Studying genetic variation explaining phenotypic variance will help to identify specific underlying pathomechanisms. We investigated the effect of common variation on ASD subdomains in two cohorts including >2500 individuals. Based on the Autism Diagnostic Interview-Revised (ADI-R), we identified and confirmed six subdomains with a SNP-based genetic heritability h²_SNP = 0.2-0.4. The subdomains nonverbal communication (NVC), social interaction (SI), and peer interaction (PI) shared genetic risk factors, while the subdomains of repetitive sensory-motor behavior (RB) and restricted interests (RI) were genetically independent of each other. The polygenic risk score (PRS) for ASD as categorical diagnosis explained 2.3-3.3% of the variance of SI, joint attention (JA), and PI, 4.5% for RI, 1.2% of RB, but only 0.7% of NVC. We report eight genome-wide significant hits-partially replicating previous findings-and 292 known and novel candidate genes. The underlying biological mechanisms were related to neuronal transmission and development. At the SNP and gene level, all subdomains showed overlap, with the exception of RB. However, no overlap was observed at the functional level. In summary, the ADI-R algorithm-derived subdomains related to social communication show a shared genetic etiology in contrast to restricted and repetitive behaviors. The ASD-specific PRS overlapped only partially, suggesting an additional role of specific common variation in shaping the phenotypic expression of ASD subdomains.

Ex vivo Quantitative Proteomic Analysis of Serotonin Transporter Interactome: Network Impact of the SERT Ala56 Coding Variant

Altered serotonin (5-HT) signaling is associated with multiple brain disorders, including major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and autism spectrum disorder (ASD). The presynaptic, high-affinity 5-HT transporter (SERT) tightly regulates 5-HT clearance after release from serotonergic neurons in the brain and enteric nervous systems, among other sites. Accumulating evidence suggests that SERT is dynamically regulated in distinct activity states as a result of environmental and intracellular stimuli, with regulation perturbed by disease-associated coding variants. Our lab identified a rare, hypermorphic SERT coding substitution, Gly56Ala, in subjects with ASD, finding that the Ala56 variant stabilizes a high-affinity outward-facing conformation (SERT^∗) that leads to elevated 5-HT uptake in vitro and in vivo. Hyperactive SERT Ala56 appears to preclude further activity enhancements by p38α mitogen-activated protein kinase (MAPK) and can be normalized by pharmacological p38α MAPK inhibition, consistent with SERT Ala56 mimicking, constitutively, a high-activity conformation entered into transiently by p38α MAPK activation. We hypothesize that changes in SERT-interacting proteins (SIPs) support the shift of SERT into the SERT^∗ state which may be captured by comparing the composition of SERT Ala56 protein complexes with those of wildtype (WT) SERT, defining specific interactions through comparisons of protein complexes recovered using preparations from SERT^–/– (knockout; KO) mice. Using quantitative proteomic-based approaches, we identify a total of 459 SIPs, that demonstrate both SERT specificity and sensitivity to the Gly56Ala substitution, with a striking bias being a loss of SIP interactions with SERT Ala56 compared to WT SERT. Among this group are previously validated SIPs, such as flotillin-1 (FLOT1) and protein phosphatase 2A (PP2A), whose functions are believed to contribute to SERT microdomain localization and regulation. Interestingly, our studies nominate a number of novel SIPs implicated in ASD, including fragile X mental retardation 1 protein (FMR1) and SH3 and multiple ankyrin repeat domains protein 3 (SHANK3), of potential relevance to long-standing evidence of serotonergic contributions to ASD. Further investigation of these SIPs, and the broader networks they engage, may afford a greater understanding of ASD as well as other brain and peripheral disorders associated with perturbed 5-HT signaling.

Developmental IL-6 Exposure Favors Production of PDGF-Responsive Multipotential Progenitors at the Expense of Neural Stem Cells and Other Progenitors

Interleukin-6 (IL-6) is increased in maternal serum and amniotic fluid of children subsequently diagnosed with autism spectrum disorders. However, it is not clear how increased IL-6 alters brain development. Here, we show that IL-6 increases the prevalence of a specific platelet-derived growth factor (PDGF)-responsive multipotent progenitor, with opposite effects on neural stem cells and on subsets of bipotential glial progenitors. Acutely, increasing circulating IL-6 levels 2-fold above baseline in neonatal mice specifically stimulated the proliferation of a PDGF-responsive multipotential progenitor accompanied by increased phosphorylated STAT3, increased Fbxo15 expression, and decreased Dnmt1 and Tlx expression. Fate mapping studies using a Nestin-CreERT2 driver revealed decreased astrogliogenesis in the frontal cortex. IL-6-treated mice were hyposmic; however, olfactory bulb neuronogenesis was unaffected. Altogether, these studies provide important insights into how inflammation alters neural stem cells and progenitors and provide new insights into the molecular and cellular underpinnings of neurodevelopmental disorders associated with maternal infections.

Network Structure Analysis Identifying Key Genes of Autism and Its Mechanism

Identifying the key genes of autism is of great significance for understanding its pathogenesis and improving the clinical level of medicine. In this paper, we use the structural parameters (average degree) of gene correlation networks to identify genes related to autism and study its pathogenesis. Based on the gene expression profiles of 82 autistic patients (the experimental group, E) and 64 healthy persons (the control group, C) in NCBI database, spearman correlation networks are established, and their average degrees under different thresholds are analyzed. It is found that average degrees of C and E are basically separable at the full thresholds. This indicates that there is a clear difference between the network structures of C and E, and it also suggests that this difference is related to the mechanism of disease. By annotating and enrichment analysis of the first 20 genes (MD-Gs) with significant difference in the average degree, we find that they are significantly related to gland development, cardiovascular development, and embryogenesis of nervous system, which support the results in Alter et al.'s original research. In addition, FIGF and CSF3 may play an important role in the mechanism of autism.

Transcriptomic Analysis Reveals Abnormal Expression of Prion Disease Gene Pathway in Brains from Patients with Autism Spectrum Disorders

The role of infections in the pathogenesis of autism spectrum disorder (ASD) is still controversial. In this study, we aimed to evaluate markers of infections and immune activation in ASD by performing a meta-analysis of publicly available whole-genome transcriptomic datasets of brain samples from autistic patients and otherwise normal people. Among the differentially expressed genes, no significant enrichment was observed for infectious diseases previously associated with ASD, including herpes simplex virus-1 (HSV-1), cytomegalovirus and Epstein–Barr virus in brain samples, nor was it found in peripheral blood from ASD patients. Interestingly, a significant number of genes belonging to the “prion diseases” pathway were found to be modulated in our ASD brain meta-analysis. Overall, our data do not support an association between infection and ASD. However, the data do provide support for the involvement of pathways related to other neurodegenerative diseases and give input to uncover novel pathogenetic mechanisms underlying ASD.

Dlgap1 negatively regulates browning of white fat cells through effects on cell proliferation and apoptosis

Background

Obesity is a metabolic imbalance characterized by excessive deposition of white fat. The browning of white fat can effectively treat obesity and related diseases. Although Dlgap1 (Discs, Large (Drosophila) Homolog-Associated Protein 1) is suspected to have an effect on this process, no empirical evidence is available.

Methods

To understand the role of Dlgap1, we cultured white and brown fat cells, then performed overexpression and knockout experiments.

Results

We found that Dlgap1 overexpression in brown adipocytes inhibits brown-fat-related gene expression, promotes white-fat-related genes, while also increasing brown-adipocyte proliferation and apoptosis. However, the gene overexpression has no effect on brown adipocyte maturation. Knocking out Dlgap1 in white fat cells promotes the expression and inhibition of brown-fat-related and white-fat-related genes, respectively. Additionally, the knockout inhibits white fat cell proliferation and apoptosis, while also promoting their maturation.

Conclusions

Dlgap1 negatively regulates the browning of white adipocytes by influencing cell proliferation and apoptosis.

The proteome and its dynamics: A missing piece for integrative multi-omics in schizophrenia

The complex and heterogeneous pathophysiology of schizophrenia can be deconstructed by integration of large-scale datasets encompassing genes through behavioral phenotypes. Genome-wide datasets are now available for genetic, epigenetic and transcriptomic variations in schizophrenia, which are then analyzed by newly devised systems biology algorithms. A missing piece, however, is the inclusion of information on the proteome and its dynamics in schizophrenia. Proteomics has lagged behind omics of the genome, transcriptome and epigenome since analytic platforms were relatively less robust for proteins. There has been remarkable progress, however, in the instrumentation of liquid chromatography (LC) and mass spectrometry (MS) (LCMS), experimental paradigms and bioinformatics of the proteome. Here, we present a summary of methodological innovations of recent years in MS based proteomics and the power of new generation proteomics, review proteomics studies that have been conducted in schizophrenia to date, and propose how such data can be analyzed and integrated with other omics results. The function of a protein is determined by multiple molecular properties, i.e., subcellular localization, posttranslational modification (PTMs) and protein-protein interactions (PPIs). Incorporation of these properties poses additional challenges in proteomics and their integration with other omics; yet is a critical next step to close the loop of multi-omics integration. In sum, the recent advent of high-throughput proteome characterization technologies and novel mathematical approaches enable us to incorporate functional properties of the proteome to offer a comprehensive multi-omics based understanding of schizophrenia pathophysiology.

Three Brothers With Autism Carry a Stop-Gain Mutation in the HPA-Axis Gene NR3C2

Whole exome sequencing and copy-number variant analysis was performed on a family with three brothers diagnosed with autism. Each of the siblings shares an alteration in the nuclear receptor subfamily 3 group C member 2 (NR3C2) gene that is predicted to result in a stop-gain mutation (p.Q919X) in the mineralocorticoid receptor (MR) protein. This variant was maternally inherited and provides further evidence for a connection between the NR3C2 and autism. Interestingly, the NR3C2 gene encodes the MR protein, a steroid hormone-regulated transcription factor that acts in the hypothalamic-pituitary-adrenal axis and has been connected to stress and anxiety, both of which are features often seen in individuals with autism. Autism Res 2020, 13: 523-531. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Given the complexity of the genetics underlying autism, each gene contributes to risk in a relatively small number of individuals, typically less than 1% of all autism cases. Whole exome sequencing of three brothers with autism identified a rare variant in the nuclear receptor subfamily 3 group C member 2 gene that is predicted to strongly interfere with its normal function. This gene encodes the mineralocorticoid receptor protein, which plays a role in how the body responds to stress and anxiety, features that are often elevated in people diagnosed with autism. This study adds further support to the relevance of this gene as a risk factor for autism.

Foetal oestrogens and autism

Elevated latent prenatal steroidogenic activity has been found in the amniotic fluid of autistic boys, based on measuring prenatal androgens and other steroid hormones. To date, it is unclear if other prenatal steroids also contribute to autism likelihood. Prenatal oestrogens need to be investigated, as they play a key role in synaptogenesis and corticogenesis during prenatal development, in both males and females. Here we test whether levels of prenatal oestriol, oestradiol, oestrone and oestrone sulphate in amniotic fluid are associated with autism, in the same Danish Historic Birth Cohort, in which prenatal androgens were measured, using univariate logistic regression (n = 98 cases, n = 177 controls). We also make a like-to-like comparison between the prenatal oestrogens and androgens. Oestradiol, oestrone, oestriol and progesterone each related to autism in univariate analyses after correction with false discovery rate. A comparison of standardised odds ratios showed that oestradiol, oestrone and progesterone had the largest effects on autism likelihood. These results for the first time show that prenatal oestrogens contribute to autism likelihood, extending the finding of elevated prenatal steroidogenic activity in autism. This likely affects sexual differentiation, brain development and function.

A comparative study of the genetic components of three subcategories of autism spectrum disorder

The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) controversially combined previously distinct subcategories of autism spectrum disorder (ASD) into a single diagnostic category. However, genetic convergences and divergences between different ASD subcategories are unclear. By retrieving 1725 exonic de novo mutations (DNMs) from 1628 subjects with autistic disorder (AD), 1873 from 1564 subjects with pervasive developmental disorder not otherwise specified (PDD-NOS), 276 from 247 subjects with Asperger's syndrome (AS), and 2077 from 2299 controls, we found that rates of putative functional DNMs (loss-of-function, predicted deleterious missense, and frameshift) in all three subcategories were significantly higher than those in control. We then investigated the convergences and divergences of the three ASD subcategories based on four genetic aspects: whether any two ASD subcategories (1) shared significantly more genes with functional DNMs, (2) exhibited similar spatio-temporal expression patterns, (3) shared significantly more candidate genes, and (4) shared some ASD-associated functional pathways. It is revealed that AD and PDD-NOS were broadly convergent in terms of all four genetic aspects, suggesting these two ASD subcategories may be genetically combined. AS was divergent to AD and PDD-NOS for aspects of functional DNMs and expression patterns, whereas AS and AD/PDD-NOS were convergent for aspects of candidate genes and functional pathways. Our results indicated that the three ASD subcategories present more genetic convergences than divergences, favouring DSM-5's new classification. This study suggests that specifically defined genotypes and their corresponding phenotypes should be integrated analyzed for precise diagnosis of complex disorders, such as ASD.

An integrative analysis of non-coding regulatory DNA variations associated with autism spectrum disorder

A number of genetic studies have identified rare protein-coding DNA variations associated with autism spectrum disorder (ASD), a neurodevelopmental disorder with significant genetic etiology and heterogeneity. In contrast, the contributions of functional, regulatory genetic variations that occur in the extensive non-protein-coding regions of the genome remain poorly understood. Here we developed a genome-wide analysis to identify the rare single nucleotide variants (SNVs) that occur in non-coding regions and determined the regulatory function and evolutionary conservation of these variants. Using publicly available datasets and computational predictions, we identified SNVs within putative regulatory regions in promoters, transcription factor binding sites, and microRNA genes and their target sites. Overall, we found that the regulatory variants in ASD cases were enriched in ASD-risk genes and genes involved in fetal neurodevelopment. As with previously reported coding mutations, we found an enrichment of the regulatory variants associated with dysregulation of neurodevelopmental and synaptic signaling pathways. Among these were several rare inherited SNVs found in the mature sequence of microRNAs predicted to affect the regulation of ASD-risk genes. We show a paternally inherited miR-873-5p variant with altered binding affinity for several risk-genes including NRXN2 and CNTNAP2 putatively overlay maternally inherited loss-of-function coding variations in NRXN1 and CNTNAP2 to likely increase the genetic liability in an idiopathic ASD case. Our analysis pipeline provides a new resource for identifying loss-of-function regulatory DNA variations that may contribute to the genetic etiology of complex disorders.

Novel Gene-Based Analysis of ASD GWAS: Insight Into the Biological Role of Associated Genes

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by its significant social impact and high heritability. The latest meta-analysis of ASD GWAS (genome-wide association studies) has revealed the association of several SNPs that were replicated in additional sets of independent samples. However, summary statistics from GWAS can be used to perform a gene-based analysis (GBA). GBA allows to combine all genetic information across the gene to create a single statistic (p-value for each gene). Thus, PASCAL (Pathway scoring algorithm), a novel GBA tool, has been applied to the summary statistics from the latest meta-analysis of ASD. GBA approach (testing the gene as a unit) provides an advantage to perform an accurate insight into the biological ASD mechanisms. Therefore, a gene-network analysis and an enrichment analysis for KEGG and GO terms were carried out. GENE2FUNC was used to create gene expression heatmaps and to carry out differential expression analysis (DEA) across GTEx v7 tissues and Brainspan data. dbMDEGA was employed to perform a DEG analysis between ASD and brain control samples for the associated genes and interactors.

Results: PASCAL has identified the following loci associated with ASD: XRN2, NKX2-4, PLK1S1, KCNN2, NKX2-2, CRHR1-IT1, C8orf74 and LOC644172. While some of these genes were previously reported by MAGMA (XRN2, PLK1S1, and KCNN2), PASCAL has been useful to highlight additional genes. The biological characterization of the ASD-associated genes and their interactors have demonstrated the association of several GO and KEGG terms. Moreover, DEA analysis has revealed several up- and down-regulated clusters. In addition, many of the ASD-associated genes and their interactors have shown association with ASD expression datasets.

Conclusions: This study identifies several associations at a gene level in ASD. Most of them were previously reported by MAGMA. This fact proves that PASCAL is an efficient GBA tool to extract additional information from previous GWAS. In addition, this study has characterized for the first time the biological role of the ASD-associated genes across brain regions, neurodevelopmental stages, and ASD gene-expression datasets.

Haploinsufficiency in the ANKS1B gene encoding AIDA-1 leads to a neurodevelopmental syndrome

Neurodevelopmental disorders, including autism spectrum disorder, have complex polygenic etiologies. Single-gene mutations in patients can help define genetic factors and molecular mechanisms underlying neurodevelopmental disorders. Here we describe individuals with monogenic heterozygous microdeletions in ANKS1B, a predicted risk gene for autism and neuropsychiatric diseases. Affected individuals present with a spectrum of neurodevelopmental phenotypes, including autism, attention-deficit hyperactivity disorder, and speech and motor deficits. Neurons generated from patient-derived induced pluripotent stem cells demonstrate loss of the ANKS1B-encoded protein AIDA-1, a brain-specific protein highly enriched at neuronal synapses. A transgenic mouse model of Anks1b haploinsufficiency recapitulates a range of patient phenotypes, including social deficits, hyperactivity, and sensorimotor dysfunction. Identification of the AIDA-1 interactome using quantitative proteomics reveals protein networks involved in synaptic function and the etiology of neurodevelopmental disorders. Our findings formalize a link between the synaptic protein AIDA-1 and a rare, previously undefined genetic disease we term ANKS1B haploinsufficiency syndrome.

Network-Based Integrative Analysis of Genomics, Epigenomics and Transcriptomics in Autism Spectrum Disorders

Current studies suggest that autism spectrum disorders (ASDs) may be caused by many genetic factors. In fact, collectively considering multiple studies aimed at characterizing the basic pathophysiology of ASDs, a large number of genes has been proposed. Addressing the problem of molecular data interpretation using gene networks helps to explain genetic heterogeneity in terms of shared pathways. Besides, the integrative analysis of multiple omics has emerged as an approach to provide a more comprehensive view of a disease. In this work, we carry out a network-based meta-analysis of the genes reported as associated with ASDs by studies that involved genomics, epigenomics, and transcriptomics. Collectively, our analysis provides a prioritization of the large number of genes proposed to be associated with ASDs, based on genes' relevance within the intracellular circuits, the strength of the supporting evidence of association with ASDs, and the number of different molecular alterations affecting genes. We discuss the presence of the prioritized genes in the SFARI (Simons Foundation Autism Research Initiative) database and in gene networks associated with ASDs by other investigations. Lastly, we provide the full results of our analyses to encourage further studies on common targets amenable to therapy.

CYFIP1 overexpression increases fear response in mice but does not affect social or repetitive behavioral phenotypes

Background

CYFIP1, a protein that interacts with FMRP and regulates protein synthesis and actin dynamics, is overexpressed in Dup15q syndrome as well as autism spectrum disorder (ASD). While CYFIP1 heterozygosity has been rigorously studied due to its loss in 15q11.2 deletion, Prader-Willi and Angelman syndrome, the effects of CYFIP1 overexpression, as is observed in patients with CYFIP1 duplication, are less well understood.

Methods

We developed and validated a mouse model of human CYFIP1 overexpression (CYFIP1 OE) using qPCR and western blot analysis. We performed a large battery of behavior testing on these mice, including ultrasonic vocalizations, three-chamber social assay, home-cage behavior, Y-maze, elevated plus maze, open field test, Morris water maze, fear conditioning, prepulse inhibition, and the hot plate assay. We also performed RNA sequencing and analysis on the basolateral amygdala.

Results

Extensive behavioral testing in CYFIP1 OE mice reveals no changes in the core behaviors related to ASD: social interactions and repetitive behaviors. However, we did observe mild learning deficits and an exaggerated fear response. Using RNA sequencing of the basolateral amygdala, a region associated with fear response, we observed changes in pathways related to cytoskeletal regulation, oligodendrocytes, and myelination. We also identified GABA-A subunit composition changes in basolateral amygdala neurons, which are essential components of the neural fear conditioning circuit.

Conclusion

Overall, this research identifies the behavioral and molecular consequences of CYFIP1 overexpression and how they contribute to the variable phenotype seen in Dup15q syndrome and in ASD patients with excess CYFIP1.

The ANKS1B gene and its associated phenotypes: focus on CNS drug response

The ANKS1B gene was a top finding in genome-wide association studies (GWAS) of antipsychotic drug response. Subsequent GWAS findings for ANKS1B include cognitive ability, educational attainment, body mass index, response to corticosteroids and drug dependence. We review current human association evidence for ANKS1B, in addition to functional studies that include two published mouse knockouts. The several GWAS findings in humans indicate that phenotypically relevant variation is segregating at the ANKS1B locus. ANKS1B shows strong plausibility for involvement in CNS drug response because it encodes a postsynaptic effector protein that mediates long-term changes to neuronal biology. Forthcoming data from large biobanks should further delineate the role of ANKS1B in CNS drug response.

Defining the Genetic, Genomic, Cellular, and Diagnostic Architectures of Psychiatric Disorders

Studies of the genetics of psychiatric disorders have become one of the most exciting and fast-moving areas in human genetics. A decade ago, there were few reproducible findings, and now there are hundreds. In this review, we focus on the findings that have illuminated the genetic architecture of psychiatric disorders and the challenges of using these findings to inform our understanding of pathophysiology. The evidence is now overwhelming that psychiatric disorders are "polygenic"-that many genetic loci contribute to risk. With the exception of a subset of those with ASD, few individuals with a psychiatric disorder have a single, deterministic genetic cause; rather, developing a psychiatric disorder is influenced by hundreds of different genetic variants, consistent with a polygenic model. As progressively larger studies have uncovered more about their genetic architecture, the need to elucidate additional architectures has become clear. Even if we were to have complete knowledge of the genetic architecture of a psychiatric disorder, full understanding requires deep knowledge of the functional genomic architecture-the implicated loci impact regulatory processes that influence gene expression and the functional coordination of genes that control biological processes. Following from this is cellular architecture: of all brain regions, cell types, and developmental stages, where and when are the functional architectures operative? Given that the genetic architectures of different psychiatric disorders often strongly overlap, we are challenged to re-evaluate and refine the diagnostic architectures of psychiatric disorders using fundamental genetic and neurobiological data.

In vivo proximity proteomics of nascent synapses reveals a novel regulator of cytoskeleton-mediated synaptic maturation

Excitatory synapse formation during development involves the complex orchestration of both structural and functional alterations at the postsynapse. However, the molecular mechanisms that underlie excitatory synaptogenesis are only partially resolved, in part because the internal machinery of developing synapses is largely unknown. To address this, we apply a chemicogenetic approach, in vivo biotin identification (iBioID), to discover aspects of the proteome of nascent synapses. This approach uncovered sixty proteins, including a previously uncharacterized protein, CARMIL3, which interacts in vivo with the synaptic cytoskeletal regulator proteins SrGAP3 (or WRP) and actin capping protein. Using new CRISPR-based approaches, we validate that endogenous CARMIL3 is localized to developing synapses where it facilitates the recruitment of capping protein and is required for spine structural maturation and AMPAR recruitment associated with synapse unsilencing. Together these proteomic and functional studies reveal a previously unknown mechanism important for excitatory synapse development in the developing perinatal brain.

Integrated Analysis of Brain Transcriptome Reveals Convergent Molecular Pathways in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a set of complex neurodevelopmental disorders with etiology that remains elusive. Although there is a mounting body of investigation in different brain regions related to ASD, our knowledge about the common and distinct perturb condition between them is at the threshold of accumulation. In this study, based on protein-protein interactions, post-mortem transcriptome analysis was performed with corpus callosum (CC) and prefrontal cortex (PFC) samples from ASD individuals and controls. Co-expression network analysis revealed that a total of seven (four for CC set, three for PFC set) core dysfunctional modules strongly enriched for known ASD-risk genes. Three quarters of them in CC set (M4, M6, M29) significantly enriched for genes annotated by genetically associated variants in our previous whole genome sequencing data. We further determined transcriptional and post-transcriptional regulation subnetwork for each ASD-correlated module, including 47 pivot transcription factors, 130 pivot miRNAs, and 7 pivot lncRNAs. Moreover, there were significantly more interactions between CC-M4, -M6, and PFC-M2, mainly involved in synaptic functions and neuronal development. Our integrated multifactor analysis of ASD brain transcriptome profile illustrated underlying common and distinct molecular mechanisms and the module crosstalk between CC and PFC, helping to shed light on the molecular neuropathological underlying ASD.

Neuroglia in the autistic brain: evidence from a preclinical model

Background

Neuroglial cells that provide homeostatic support and form defence of the nervous system contribute to all neurological disorders. We analyzed three major types of neuroglia, astrocytes, oligodendrocytes, and microglia in the brains of an animal model of autism spectrum disorder, in which rats were exposed prenatally to antiepileptic and mood stabilizer drug valproic acid; this model being of acknowledged clinical relevance.

Methods

We tested the autistic-like behaviors of valproic acid-prenatally exposed male rats by performing isolation-induced ultrasonic vocalizations, the three-chamber test, and the hole board test. To account for human infancy, adolescence, and adulthood, such tasks were performed at postnatal day 13, postnatal day 35, and postnatal day 90, respectively. After sacrifice, we examined gene and protein expression of specific markers of neuroglia in hippocampus, prefrontal cortex, and cerebellum, these brain regions being associated with autism spectrum disorder pathogenesis.

Results

Infant offspring of VPA-exposed dams emitted less ultrasonic vocalizations when isolated from their mothers and siblings and, in adolescence and adulthood, they showed altered sociability in the three chamber test and increased stereotypic behavior in the hole board test. Molecular analyses indicate that prenatal valproic acid exposure affects all types of neuroglia, mainly causing transcriptional modifications. The most prominent changes occur in prefrontal cortex and in the hippocampus of autistic-like animals; these changes are particularly evident during infancy and adolescence, while they appear to be mitigated in adulthood.

Conclusions

Neuroglial pathological phenotype in autism spectrum disorder rat model appears to be rather mild with little signs of widespread and chronic neuroinflammation.