AutDB: a platform to decode the genetic architecture of autism.

AutDB is a deeply annotated resource for exploring the impact of genetic variations associated with autism spectrum disorders (ASD). First released in 2007, AutDB has evolved into a multi-modular resource of diverse types of genetic and functional evidence related to ASD. Current modules include: Human Gene, which annotates all ASD-linked genes and their variants; Animal Model, which catalogs behavioral, anatomical and physiological data from rodent models of ASD; Protein Interaction (PIN), which builds interactomes from direct relationships of protein products of ASD genes; and Copy Number Variant (CNV), which catalogs deletions and duplications of chromosomal loci identified in ASD. A multilevel data-integration strategy is utilized to connect the ASD genes to the components of the other modules. All information in this resource is manually curated by expert scientists from primary scientific publications and is referenced to source articles. AutDB is actively maintained with a rigorous quarterly data release schedule. As of June 2017, AutDB contains detailed annotations for 910 genes, 2197 CNV loci, 1060 rodent models and 38 296 PINs. With its widespread use by the research community, AutDB serves as a reference resource for analysis of large datasets, accelerating ASD research and potentially leading to targeted drug treatments. AutDB is available at http://autism.mindspec.org/autdb/Welcome.do.

Co-expression profiling of autism genes in the mouse brain.

Autism spectrum disorder (ASD) is one of the most prevalent and highly heritable neurodevelopmental disorders in humans. There is significant evidence that the onset and severity of ASD is governed in part by complex genetic mechanisms affecting the normal development of the brain. To date, a number of genes have been associated with ASD. However, the temporal and spatial co-expression of these genes in the brain remain unclear. To address this issue, we examined the co-expression network of 26 autism genes from AutDB (http://mindspec.org/autdb.html), in the framework of 3,041 genes whose expression energies have the highest correlation between the coronal and sagittal images from the Allen Mouse Brain Atlas database (http://mouse.brain-map.org). These data were derived from in situ hybridization experiments conducted on male, 56-day old C57BL/6J mice co-registered to the Allen Reference Atlas, and were used to generate a normalized co-expression matrix indicating the cosine similarity between expression vectors of genes in this database. The network formed by the autism-associated genes showed a higher degree of co-expression connectivity than seen for the other genes in this dataset (Kolmogorov-Smirnov P = 5×10⁻²8). Using Monte Carlo simulations, we identified two cliques of co-expressed genes that were significantly enriched with autism genes (A Bonferroni corrected P<0.05). Genes in both these cliques were significantly over-expressed in the cerebellar cortex (P = 1×10⁻5) suggesting possible implication of this brain region in autism. In conclusion, our study provides a detailed profiling of co-expression patterns of autism genes in the mouse brain, and suggests specific brain regions and new candidate genes that could be involved in autism etiology.

AutDB: a gene reference resource for autism research.

Recent advances in studies of Autism Spectrum Disorders (ASD) has uncovered many new candidate genes and continues to do so at an accelerated pace. To address the genetic complexity of ASD, we have developed AutDB (http://www.mindspec.org/autdb.html), a publicly available web-portal for on-going collection, manual annotation and visualization of genes linked to the disorder. We present a disease-driven database model in AutDB where all genes connected to ASD are collected and classified according to their genetic variation: candidates identified from genetic association studies, rare single gene mutations and genes linked to syndromic autism. Gene entries are richly annotated for their relevance to autism, along with an in-depth view of their molecular functions. The content of AutDB originates entirely from the published scientific literature and is organized to optimize its use by the research community. The main focus of this resource is to provide an up-to-date, annotated list of ASD candidate genes in the form of reference dataset for interrogating molecular mechanisms underlying the disorder. Our model for consolidated knowledge representation in genetically complex disorders could be replicated to study other such disorders.

Prenatal maternal infection and risk for autism in offspring: A meta-analysis.

While prenatal maternal infection has received attention as a preventable and treatable risk factor for autism, findings have been inconsistent. This paper presents the results of a meta-analysis to determine whether the weight of the evidence supports such an association. Studies with a categorical diagnosis of autism as the outcome and an assessment of its association with prenatal maternal infection or fever (or the data necessary to compute this association) were included. A total of 36 studies met these criteria. Two independent reviewers extracted data on study design, methods of assessment, type of infectious agent, site of infection, trimester of exposure, definition of autism, and effect size. Analyses demonstrated a statistically significant association of maternal infection/fever with autism in offspring (OR = 1.32; 95% CI = 1.20-1.46). Adjustment for evident publication bias slightly weakened this association. There was little variation in effect sizes across agent or site of infection. Small differences across trimester of exposure were not statistically significant. There was some evidence that recall bias associated with status on the outcome variable leads to differential misclassification of exposure status. Nonetheless, the overall association is only modestly reduced when studies potentially contaminated by such bias are removed. Although causality has not been firmly established, these findings suggest maternal infection during pregnancy confers an increase in risk for autism in offspring. Given the prevalence of this risk factor, it is possible that the incidence of autism would be reduced by 12%-17% if maternal infections could be prevented or safely treated in a timely manner. LAY SUMMARY: This study is a meta-analysis of the association of maternal infection during pregnancy and subsequent autism in offspring. In combining the results from 36 studies of this association we find that a significant relationship is present. The association does not vary much across the types of infections or when they occur during pregnancy. We conclude that the incidence of autism could be substantially reduced if maternal infections could be prevented or safely treated in a timely manner.

A multifaceted approach for analyzing complex phenotypic data in rodent models of autism.

Autism (MIM 209850) is a multifactorial disorder with a broad clinical presentation. A number of high-confidence ASD risk genes are known; however, the contribution of non-genetic environmental factors towards ASD remains largely uncertain. Here, we present a bioinformatics resource of genetic and induced models of ASD developed using a shared annotation platform. Using this data, we depict the intricate trends in the research approaches to analyze rodent models of ASD. We identify the top 30 most frequently studied phenotypes extracted from rodent models of ASD based on 787 publications. As expected, many of these include animal model equivalents of the "core" phenotypes associated with ASD, such as impairments in social behavior and repetitive behavior, as well as several comorbid features of ASD including anxiety, seizures, and motor-control deficits. These phenotypes have also been studied in models based on a broad range of environmental inducers present in the database, of which gestational exposure to valproic acid (VPA) and maternal immune activation models comprising lipopolysaccharide (LPS) and poly I:C are the most studied. In our unique dataset of rescue models, we identify 24 pharmaceutical agents tested on established models derived from various ASD genes and CNV loci for their efficacy in mitigating symptoms relevant for ASD. As a case study, we analyze a large collection of Shank3 mouse models providing a high-resolution view of the in vivo role of this high-confidence ASD gene, which is the gateway towards understanding and dissecting the heterogeneous phenotypes seen in single-gene models of ASD. The trends described in this study could be useful for researchers to compare ASD models and to establish a complete profile for all relevant animal models in ASD research.

The zebrafish gene claudinj is essential for normal ear function and important for the formation of the otoliths.

We have identified a mutation in the zebrafish gene claudinj generated by retroviral integration. Mutant embryos display otoliths severely reduced in size, no response to tapping stimulus, and an inability to balance properly suggesting vestibular and hearing dysfunction. Antisense in situ hybridization to the cldnj gene showed expression first in the otic placode and later asymmetric expression in the otic vesicle. Morpholino inhibition of claudinj expression showed similar defects in otolith formation. Phylogenetic analysis of claudin sequences from multiple species demonstrates that claudinj was part of a gene expansion that began in the common ancestor of fish and humans, but additional fish specific gene duplications must have also occurred.

The Homeodomain Resource: 2003 update.

The Homeodomain Resource is a searchable, curated collection of information for the homeodomain protein family. The resource is organized in a compact form and provides user-friendly interfaces for both querying the component databases and assembling customized datasets. The current release (version 5.0, October 2002) contains 1056 full-length homeodomain-containing sequences, 37 experimentally-derived structures, 81 homeodomain interactions, 84 homeodomain DNA-binding sites and 114 homeodomain proteins implicated in human genetic disorders. A new feature of this new release is the inclusion of experimentally-derived protein-protein interaction data for homeodomain family members. All entries are cross-linked for easy retrieval of the original records from source databases. The Homeodomain Resource is freely available through the World Wide Web at http://research.nhgri.nih.gov/homeodomain/.

A systematic variant annotation approach for ranking genes associated with autism spectrum disorders.

BACKGROUND: The search for genetic factors underlying autism spectrum disorders (ASD) has led to the identification of hundreds of genes containing thousands of variants that differ in mode of inheritance, effect size, frequency, and function. A major challenge involves assessing the collective evidence in an unbiased, systematic manner for their functional relevance. METHODS: Here, we describe a scoring algorithm for prioritization of candidate genes based on the cumulative strength of evidence for each ASD-associated variant cataloged in AutDB (also known as SFARI Gene). We retrieved data from 889 publications to generate a dataset of 2187 rare and 711 common variants distributed across 461 genes implicated in ASD. Each individual variant was manually annotated with multiple attributes extracted from the original report, followed by score assignment using a set of standardized parameters yielding a single score for each gene. RESULTS: There was a wide variation in scores; SHANK3, CHD8, and ADNP had distinctly higher scores than all other genes in the dataset. Our gene scores were significantly correlated with other recently published rankings of ASD genes (RSpearman = 0.40-0.63; p< 0.0001), providing support for our scoring algorithm. CONCLUSIONS: This new resource, which is freely available, for the first time aggregates on one-platform variants identified from various study types (simplex, multiplex, multigenerational, and consanguineous families), from both common and rare variants, and also incorporates their putative functional consequences to arrive at a genetically and biologically driven ranking scheme. This work represents a major step in moving from simply cataloging autism variants to using data-driven approaches to gain insight into their significance.

Gaucher mutation N188S is associated with myoclonic epilepsy.

The recent article by Montfort et al. [2004] reported a functional analysis of 13 glucocerebrosidase alleles, including mutation N188S, which they considered to be a "very mild mutation" or "modifier variant." Our clinical experience with patients carrying this mutation and preliminary protein modeling data lead us to dispute this conclusion.

Molecular modeling of mutations in the DNA-binding domain of the oncoprotein Qin.

The retroviral oncogene qin, homologue of mammalian brain factor 1 (FOXG1 B), belongs to the family of winged helix transcription factors. Oncogenic transformation by Qin requires sequence-specific DNA binding. Missense mutations in the forkhead domain of Qin modulate its oncogenic transforming ability in chicken embryonic fibroblasts. We used homology model building (threading) techniques to generate atomic structures of wild-type c-Qin and c-Qin mutants, using the solution structure of the forkhead domain of the adipocyte transcription factor as a template (M. J. van Dongen et al., J. Mol. Biol., 296: 351-359, 2000). Energy calculations indicate that the Qin forkhead structure is stabilized primarily by hydrophobic interactions between residues at the helical interface. None of the missense mutations analyzed here were responsible for maintaining the most critical pairwise interactions holding the forkhead domain together. The mutated proteins form the overall structure of the forkhead domain, but the mutations do interfere with DNA binding.

Structural analysis of disease-causing mutations in the P-subfamily of forkhead transcription factors.

Mutations in a number of forkhead transcription factors are associated with the development of inherited diseases in humans. Two closely related genes, FOXP2 and FOXP3, are implicated in two completely different human disorders. A point mutation in the forkhead domain of FOXP2 (R553H) is responsible for a severe speech and language disorder, while a series of missense mutations distributed over the forkhead domain of FOXP3 cause a fatal disorder called IPEX, characterized by immune dysregulation, polyendocrinopathy, and enteropathy. Homology model building techniques were used to generate atomic structures of FOXP2 and FOXP3, using the solution structures of the forkhead domain of the adipocyte-transcription factor FREAC-11 and AFX as templates. The impact of these disease-causing missense mutations on the three-dimensional structure, stability, and surface electrostatic charge distribution of the forkhead domains is examined here. The missense mutations R553H in FOXP2 and R397W in FOXP3 dramatically alter the electrostatic potentials of the molecular surface of their respective forkhead domains.

STX1A and Asperger syndrome: a replication study.

BACKGROUND: Autism spectrum conditions (ASC) are a group of conditions characterized by difficulties in communication and social interaction, alongside unusually narrow interests and repetitive, stereotyped behaviour. Genetic association and expression studies have suggested an important role for the GABAergic circuits in ASC. Syntaxin 1A (STX1A) encodes a protein involved in regulation of serotonergic and GABAergic systems and its expression is altered in autism. METHODS: In this study, the association between three single nucleotide polymorphisms (SNPs) (rs4717806, rs941298 and rs6951030) in STX1A gene and Asperger syndrome (AS) were tested in 650 controls and 479 individuals with AS, all of Caucasian ancestry. RESULTS: rs4717806 (P = 0.00334) and rs941298 (P = 0.01741) showed a significant association with AS, replicating previous results. Both SNPs putatively alter transcription factor binding sites both directly and through other variants in high linkage disequilibrium. CONCLUSIONS: The current study confirms the role of STX1A as an important candidate gene in ASC. The exact molecular mechanisms through which STX1A contributes to the etiology remain to be elucidated.

Prioritization of Copy Number Variation Loci Associated with Autism from AutDB-An Integrative Multi-Study Genetic Database.

Copy number variants (CNVs) are thought to play an important role in the predisposition to autism spectrum disorder (ASD). However, their relatively low frequency and widespread genomic distribution complicates their accurate characterization and utilization for clinical genetics purposes. Here we present a comprehensive analysis of multi-study, genome-wide CNV data from AutDB (http://mindspec.org/autdb.html), a genetic database that accommodates detailed annotations of published scientific reports of CNVs identified in ASD individuals. Overall, we evaluated 4,926 CNVs in 2,373 ASD subjects from 48 scientific reports, encompassing ∼2.12×10(9) bp of genomic data. Remarkable variation was seen in CNV size, with duplications being significantly larger than deletions, (P  =  3×10(-105); Wilcoxon rank sum test). Examination of the CNV burden across the genome revealed 11 loci with a significant excess of CNVs among ASD subjects (P<7×10(-7)). Altogether, these loci covered 15,610 kb of the genome and contained 166 genes. Remarkable variation was seen both in locus size (20 - 4950 kb), and gene content, with seven multigenic (≥3 genes) and four monogenic loci. CNV data from control populations was used to further refine the boundaries of these ASD susceptibility loci. Interestingly, our analysis indicates that 15q11.2-13.3, a genomic region prone to chromosomal rearrangements of various sizes, contains three distinct ASD susceptibility CNV loci that vary in their genomic boundaries, CNV types, inheritance patterns, and overlap with CNVs from control populations. In summary, our analysis of AutDB CNV data provides valuable insights into the genomic characteristics of ASD susceptibility CNV loci and could therefore be utilized in various clinical settings and facilitate future genetic research of this disorder.

SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs).

New technologies enabling genome-wide interrogation have led to a large and rapidly growing number of autism spectrum disorder (ASD) candidate genes. Although encouraging, the volume and complexity of these data make it challenging for scientists, particularly non-geneticists, to comprehensively evaluate available evidence for individual genes. Described here is the Gene Scoring module within SFARI Gene 2.0 (https://gene.sfari.org/autdb/GS_Home.do), a platform developed to enable systematic community driven assessment of genetic evidence for individual genes with regard to ASD.

Animal model integration to AutDB, a genetic database for autism.

BACKGROUND: In the post-genomic era, multi-faceted research on complex disorders such as autism has generated diverse types of molecular information related to its pathogenesis. The rapid accumulation of putative candidate genes/loci for Autism Spectrum Disorders (ASD) and ASD-related animal models poses a major challenge for systematic analysis of their content. We previously created the Autism Database (AutDB) to provide a publicly available web portal for ongoing collection, manual annotation, and visualization of genes linked to ASD. Here, we describe the design, development, and integration of a new module within AutDB for ongoing collection and comprehensive cataloguing of ASD-related animal models. DESCRIPTION: As with the original AutDB, all data is extracted from published, peer-reviewed scientific literature. Animal models are annotated with a new standardized vocabulary of phenotypic terms developed by our researchers which is designed to reflect the diverse clinical manifestations of ASD. The new Animal Model module is seamlessly integrated to AutDB for dissemination of diverse information related to ASD. Animal model entries within the new module are linked to corresponding candidate genes in the original "Human Gene" module of the resource, thereby allowing for cross-modal navigation between gene models and human gene studies. Although the current release of the Animal Model module is restricted to mouse models, it was designed with an expandable framework which can easily incorporate additional species and non-genetic etiological models of autism in the future. CONCLUSIONS: Importantly, this modular ASD database provides a platform from which data mining, bioinformatics, and/or computational biology strategies may be adopted to develop predictive disease models that may offer further insights into the molecular underpinnings of this disorder. It also serves as a general model for disease-driven databases curating phenotypic characteristics of corresponding animal models.

A brain region-specific predictive gene map for autism derived by profiling a reference gene set.

Molecular underpinnings of complex psychiatric disorders such as autism spectrum disorders (ASD) remain largely unresolved. Increasingly, structural variations in discrete chromosomal loci are implicated in ASD, expanding the search space for its disease etiology. We exploited the high genetic heterogeneity of ASD to derive a predictive map of candidate genes by an integrated bioinformatics approach. Using a reference set of 84 Rare and Syndromic candidate ASD genes (AutRef84), we built a composite reference profile based on both functional and expression analyses. First, we created a functional profile of AutRef84 by performing Gene Ontology (GO) enrichment analysis which encompassed three main areas: 1) neurogenesis/projection, 2) cell adhesion, and 3) ion channel activity. Second, we constructed an expression profile of AutRef84 by conducting DAVID analysis which found enrichment in brain regions critical for sensory information processing (olfactory bulb, occipital lobe), executive function (prefrontal cortex), and hormone secretion (pituitary). Disease specificity of this dual AutRef84 profile was demonstrated by comparative analysis with control, diabetes, and non-specific gene sets. We then screened the human genome with the dual AutRef84 profile to derive a set of 460 potential ASD candidate genes. Importantly, the power of our predictive gene map was demonstrated by capturing 18 existing ASD-associated genes which were not part of the AutRef84 input dataset. The remaining 442 genes are entirely novel putative ASD risk genes. Together, we used a composite ASD reference profile to generate a predictive map of novel ASD candidate genes which should be prioritized for future research.

The DNA-binding region of RAG 1 is not a homeodomain.

Functional annotation is used to catalog information that would be of value in experimental design and analysis but annotations in public databases are often incorrect. Here, one such case is discussed.

Structural and functional analyses of disease-causing missense mutations in the forkhead domain of FOXC1.

Five missense mutations (P79L, P79T, I91S, I91T and R127H) within the forkhead DNA-binding domain of the FOXC1 transcription factor, identified in patients with Axenfeld-Rieger (AR) malformations, were studied to identify the effects of these mutations on FOXC1 structure and function. Molecular modeling and threading analyses predict that the I91S and T mutations may generate local disruptions to the structure of the forkhead domain while the R127H mutation alters the electrostatic charge of the DNA binding surface of the forkhead domain. The P79L and T mutations are not predicted to grossly perturb the structure of the forkhead domain. Biological analyses indicate that all of these missense mutations cause a range of FOXC1 perturbations, including nuclear localization defects, reduced or abolished DNA binding capacity, and a reduction in the transactivation capacity of FOXC1. These experiments extend our previous hypothesis that reduced transactivation of appropriate target genes by FOXC1, underlie AR malformations mapping to human chromosome 6p25. Importantly, these results can also be applied to predict the consequences of the molecular effects of mutations of other FOX genes that have analogous missense mutations, including FOXP2, FOXE3 and FOXC2.