Animal model integration to AutDB, a genetic database for autism

A generalized classification and coding system of Human Disease Animal Model Resource data with a case study to show improving database retrieval efficiency

BACKGROUND

Currently there is no unified data classification and coding standard for the existing human disease animal model resource data worldwide. Different data classification and coding systems produce different retrieval methods. Some of these methods are inefficient and difficult to use. This research investigated the rules for the classification and coding of such data based on the Replication Methodology of Animal Models for Human Disease, the Classification and Coding Rules for Health Information Data Set (WS/T 306-2009), the Science and Technology Resource Identification (GB/T 32843-2016), the Scientific Data Management Measures (000014349/2018-00052), and The Generic Description Specification for Natural Science and Technology Resources. This research aimed to develop a classification and coding system for data obtained from human disease animal model resource based on the Internet environment to provide a standardized and unified foundation for the collection, saving, retrieval, and sharing of data from this resource.

RESULTS

A complete data classification and coding table compiled in the form of letters and numbers was produced, with a classification infrastructure that expanded layer by layer according to the three dimensions (namely, system diseases, animal species, and modeling methods) and essential attributes. When necessary, it adopted the hierarchy of major, intermediate, and minor categories for certain layer and also one-to-one matched the code and classification result.

CONCLUSION

Through this study, a sharing and joint construction mechanism for data from this resource can be developed by all research institutes in this field. As a case study, this research also offered technical support for constructing the database for the National Human Disease Animal Model Resource Center. The technological innovation of this paper is to derive a research oriented retrieval method, which provides technical support for the research on the current COVID-19 epidemic and on possible future epidemics.

Multi-sensory (auditory and somatosensory) pre-pulse inhibition in mice

We investigated the perception of two mechanoreceptive modalities alone and in combination: main effects and interaction between auditory and somatosensory stimulation in mice. Fifteen C57BL/6J mice between the ages of 1 and 6 months were tested three times each. Experimental design roughly followed published procedures using pre-pulse inhibition (PPI) of the acoustic startle response, except pre-pulses included vibration of the test chamber as well as soft sounds. Auditory pre-pulses were 80 dB broadband noises of 4, 9, 25, or 45 ms duration. Vibrations were of the same duration but of different frequencies (500, 460, 360, and 220 Hz). Pre-pulse inhibition increased with duration of the auditory pre-pulses, as expected. There was significant PPI to some but not all vibrotactile pre-pulses. Multimodal PPI was approximately additive (no significant auditory-by-somatosensory interaction). PPI increased more with age to somatosensory than to auditory pre-pulses. Future studies of multi-modal psychophysics in various mouse mutants could lend support to more mechanistic studies of neural specificity and possibly autism, tinnitus, and PTSD.

Rodent genetic models of neurodevelopmental disorders and epilepsy

Neurodevelopmental disorders (NDDs) are characterised by cognitive, social and motor deficits and are highly comorbid with intractable epilepsies. Through advances in genetic sequencing technologies a vast number of genes have been implicated in NDDs. State-of-the-art gene-editing techniques have led to the generation of hundreds of mouse models of NDDs. As an example, rodent models of Rett and Dravet syndromes as well as the syndromes caused by mutations in CDKL5 and Syngap1 display cognitive deficits in conjunction with seizure phenotypes. These models allow researchers to understand the underlying mechanisms as well as develop novel treatment strategies that can potentially be translated to the clinic. Furthermore, it may be possible to gain insights into the contribution of epilepsy to the progression of cognitive, social and motor phenotypes in NDDs.

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.

Erroneous inference based on a lack of preference within one group: Autism, mice, and the social approach task

The Social Approach Task is commonly used to identify sociability deficits when modeling liability factors for autism spectrum disorder (ASD) in mice. It was developed to expand upon existing assays to examine distinct aspects of social behavior in rodents and has become a standard component of mouse ASD-relevant phenotyping pipelines. However, there is variability in the statistical analysis and interpretation of results from this task. A common analytical approach is to conduct within-group comparisons only, and then interpret a difference in significance levels as if it were a group difference, without any direct comparison. As an efficient shorthand, we named this approach EWOCs: Erroneous Within-group Only Comparisons. Here, we examined the prevalence of EWOCs and used simulations to test whether this approach could produce misleading inferences. Our review of Social Approach studies of high-confidence ASD genes revealed 45% of papers sampled used only this analytical approach. Through simulations, we then demonstrate how a lack of significant difference within one group often does not correspond to a significant difference between groups, and show this erroneous interpretation increases the rate of false positives up to 25%. Finally, we define a simple solution: use an index, like a social preference score, with direct statistical comparisons between groups to identify significant differences. We also provide power calculations to guide sample size in future studies. Overall, elimination of EWOCs and adoption of direct comparisons should result in more accurate, reliable, and reproducible data interpretations from the Social Approach Task across ASD liability models. Autism Res 2019, 12: 1171-1183. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: The Social Approach Task is widely used to assess social behavior in mice and is frequently used in studies modeling autism. However, reviewing published studies showed nearly half do not use correct comparisons to interpret these data. Using simulated and original data, we argue the correct statistical approach is a direct comparison of scores between groups. This simple solution should reduce false positives and improve consistency of results across studies.

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.

Behavioral and neuroanatomical approaches in models of neurodevelopmental disorders: opportunities for translation

PURPOSE OF REVIEW

This review highlights the invaluable contribution of in-vivo rodent models in dissecting the underlying neurobiology for numerous neurodevelopmental disorders. Currently, models are routinely generated with precision genomics and characterized for research on neurodevelopmental disorders. In order to impact translation, outcome measures that are translationally relevant are essential. This review emphasizes the importance of accurate neurobehavioral and anatomical analyses.

RECENT FINDINGS

Numerous well validated assays for testing alterations across behavioral domains with sensitivity and throughput have become important tools for studying the effects of genetic mutations on neurodevelopment. Recent work has highlighted relationships and links between behavioral outcomes and various anatomical metrics from neuroimaging via magnetic resonance. These readouts are biological markers and outcome measures for translational research and will be have important roles for genetic or pharmacologic intervention strategies.

SUMMARY

Combinatorial approaches that leverage translationally relevant behavior and neuroanatomy can be used to develop a platform for assessment of cutting edge preclinical models. Reliable, robust behavioral phenotypes in preclinical model systems, with clustering of brain disease will lead to well informed, precise biochemical mechanistic hypotheses. Ultimately, these steadfast workhorse techniques will accelerate the progress of developing and testing targeted treatments for multiple neurodevelopmental disorders.

Modeling autism in non-human primates: Opportunities and challenges

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social communication deficits and restricted, repetitive patterns of behavior. For more than a decade, genetically-modified, risk factor-induced, as well as naturally occurring rodent models for ASD have been used as the most predominant tools to dissect the molecular and circuitry mechanisms underlying ASD. However, the apparent evolutionary differences in terms of social behavior and brain anatomy between rodents and humans have become an issue of debate regarding the translational value of rodent models for studying ASD. More recently, genome manipulation of non human primates using lentivirus-based gene expression, TALEN and CRISPR/Cas9 mediated gene editing techniques, has been reported. Genetically modified non-human primate models for ASD have been produced and characterized. While the feasibility, value, and exciting opportunities provided by the non-human primate models have been clearly demonstrated, many challenges still remain. Here, we review current progress, discuss the remaining challenges, and highlight the key issues in the development of non-human primate models for ASD research and drug development. Autism Res 2018, 11: 686-694. © 2018 International Society for Autism Research, Wiley Periodicals, Inc.

LAY SUMMARY

Over the last two decades, genetically modified rat and mouse models have been used as the most predominant tools to study mechanisms underlying autism spectrum disorder (ASD). However, the apparent evolutionary differences between rodents and humans limit the translational value of rodent models for studying ASD. Recently, several non-human primate models for ASD have been established and characterized. Here, we review current progress, discuss the challenges, and highlight the key issues in the development of non-human primate models for ASD research and drug development.

Cell-type-specific neuroanatomy of cliques of autism-related genes in the mouse brain

Two cliques of genes identified computationally for their high co-expression in the mouse brain according to the Allen Brain Atlas, and for their enrichment in genes related to autism spectrum disorder (ASD), have recently been shown to be highly co-expressed in the cerebellar cortex, compared to what could be expected by chance. Moreover, the expression of these cliques of genes is not homogeneous across the cerebellar cortex, and it has been noted that their expression pattern seems to highlight the granular layer. However, this observation was only made by eye, and recent advances in computational neuroanatomy allow to rank cell types in the mouse brain (characterized by their transcriptome profiles) according to the similarity between their spatial density profiles and the spatial expression profiles of the cliques. We establish by Monte Carlo simulation that with probability at least 99%, the expression profiles of the two cliques are more similar to the density profile of granule cells than 99% of the expression of cliques containing the same number of genes (Purkinje cells also score above 99% in one of the cliques). Thresholding the expression profiles shows that the signal is more intense in the granular layer. Finally, we work out pairs of cell types whose combined expression profiles are more similar to the expression profiles of the cliques than any single cell type. These pairs predominantly consist of one cortical pyramidal cell and one cerebellar cell (which can be either a granule cell or a Purkinje cell).

Overview of mouse models of autism spectrum disorders

This overview describes many well characterized mouse models of autism spectrum disorders (ASDs). Mouse models considered here were selected because they are examples of genetically engineered models where human genetic evidence supports a causative relationship between the targeted mutation and the behavioral phenotype. As the ASD diagnosis is based primarily on behavioral evaluations in humans in the domains of social interaction, communication, and restricted interests, the murine phenotypes analogous to human autistic behaviors are highlighted for the different models and behaviors. Although genetically engineered mouse models with good construct and face validity are valuable for identifying and defining underlying pathophysiological mechanisms and for developing potential therapeutic interventions for the human condition, the translational value of various rodent behavioral assays remains a subject of debate. Significant challenges associated with modeling ASDs in rodents because of the clinical and molecular heterogeneity that characterize this disorder are also considered.

The need for a comprehensive molecular characterization of autism spectrum disorders

Autism spectrum disorders (ASD) are a heterogeneous group of disorders which have complex behavioural phenotypes. Although ASD is a highly heritable neuropsychiatric disorder, genetic research alone has not provided a profound understanding of the underlying causes. Recent developments using biochemical tools such as transcriptomics, proteomics and cellular models, will pave the way to gain new insights into the underlying pathological pathways. This review addresses the state-of-the-art in the search for molecular biomarkers for ASD. In particular, the most important findings in the biochemical field are highlighted and the need for establishing streamlined interaction between behavioural studies, genetics and proteomics is stressed. Eventually, these approaches will lead to suitable translational ASD models and, therefore, a better disease understanding which may facilitate novel drug discovery efforts in this challenging field.

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.

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.