Bio-collections in autism research

Contributing to an autism biobank: Diverse perspectives from autistic participants, family members and researchers

LAY ABSTRACT

A lot of autism research has focused on finding genes that might cause autism. To conduct these genetic studies, researchers have created 'biobanks' - collections of biological samples (such as blood, saliva, urine, stool and hair) and other health information (such as cognitive assessments and medical histories). Our study focused on the Australian Autism Biobank, which collected biological and health information from almost 1000 Australian autistic children and their families. We wanted to know what people thought about giving their information to the Biobank and why they chose to do so. We spoke to 71 people who gave to the Biobank, including 18 autistic adolescents and young adults, 46 of their parents and seven of their siblings. We also spoke to six researchers who worked on the Biobank project. We found that people were interested in giving their information to the Biobank so they could understand why some people were autistic. Some people felt knowing why could help them make choices about having children in the future. People also wanted to be involved in the Biobank because they believed it could be a resource that could help others in the future. They also trusted that scientists would keep their information safe and were keen to know how that information might be used in the future. Our findings show that people have lots of different views about autism biobanks. We suggest researchers should listen to these different views as they develop their work.

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.

Analysis of Gene-Environment Interactions Related to Developmental Disorders

Various genetic and environmental factors are associated with developmental disorders (DDs). It has been suggested that interaction between genetic and environmental factors (G × E) is involved in the etiology of DDs. There are two major approaches to analyze the interaction: genome-wide and candidate gene-based approaches. In this mini-review, we demonstrate how these approaches can be applied to reveal the G × E related to DDs focusing on zebrafish and mouse models. We also discuss novel approaches to analyze the G × E associated with DDs.

Human induced pluripotent stem cell line (QBRIi013-A) derivation from a 6-year-old female diagnosed with Autism spectrum disorder (ASD) and intellectual disability (ID)

Autism spectrum disorder (ASD) is a childhood-onset neurodevelopmental disorder characterized by social interaction, behavior, and communication challenges. Here, we generated an induced pluripotent stem cell (iPSC) line, QBRIi013-A using a non-integrating Sendai virus from a 6-year-old female diagnosed with ASD and intellectual disability. The QBRIi013-A cell line was fully characterized and exhibited a pluripotency capacity and trilineage differentiation potential. Furthermore, it showed normal karyotype and genetic identity to the patient's PBMCs. Consequently, this iPSC line provides a valuable cell model in understanding the molecular mechanism underlying the complexities of ASD pathogenesis.

Derivation of four iPSC lines from a male ASD patient carrying a deletion in the middle coding region of NRXN1α gene (NUIGi039-A and NUIGi039-B) and a male sibling control (NUIGi040-A and NUIGi040-B)

NRXN1 deletions are commonly found in autism spectrum disorder (ASD) and other neurodevelopmental/neuropsychiatric disorders. Derivation of induced pluripotent stem cells (iPSCs) from different diseases involving different deletion regions are essential, as NRXN1 may produce thousands of splicing variants. We report here the derivation of iPSCs from a sibling control and an ASD proband carrying de novo heterozygous deletions in the middle region of NRXN1, using a non-integrating Sendai viral kit. The genotype and karyotype of the iPSCs were validated by whole genome SNP array. All iPSC lines highly expressed pluripotency markers and could be differentiated into three germ layers.

The Autism-Related lncRNA MSNP1AS Regulates Moesin Protein to Influence the RhoA, Rac1, and PI3K/Akt Pathways and Regulate the Structure and Survival of Neurons

Autism spectrum disorder (ASD) is a complex disease involving multiple genes and multiple sites, and it is closely related to environmental factors. It has been gradually revealed that long noncoding RNAs (lncRNAs) may regulate the pathogenesis of ASD at the epigenetic level. In neuronal cells, the lncRNA moesin pseudogene 1 antisense (MSNP1AS) forms a double-stranded RNA with moesin (MSN) to suppress moesin protein expression. MSNP1AS overexpression can activate the RhoA pathway and inhibit the Rac1 and PI3K/Akt pathways; however, the regulation of Rac1 by MSNP1AS is not associated with MSN, and the effect on the RhoA pathway may also be associated with other factors. MSNP1AS can decrease the number and length of neurites, inhibit neuronal cell viability and migration, and promote apoptosis. Downregulation of MSN expression functions similarly to MSNP1AS, and its overexpression can block the above functions of MSNP1AS. In addition, in vivo experiments show that MSN improves social interactions and reduces repetitive behaviors in BTBR mice, decreases the activity of RhoA and restores the activity of PI3K/Akt pathway. Therefore, the abnormal expression of MSNP1AS in ASD patients might influence the structure and survival of neuronal cells through the regulation of moesin protein expression to facilitate the development and progression of ASD. These findings provide new evidence for studying the mechanisms of lncRNAs in ASD. LAY SUMMARY: Autism spectrum disorder (ASD) is a common neurodevelopmental disease and its neurodevelopmental mechanisms have not been elucidated. More and more studies have found that long noncoding RNAs (lncRNAs) can regulate the development of central nervous system in many ways and affect the pathogenic process of ASD. Moesin pseudogene 1 antisense (MSNP1AS) is an up-regulated lncRNA in ASD patients. In-depth functional experiments showed that MSNP1AS inhibited moesin protein expression and regulated the activation of multiple signaling pathways, thus decreasing the number and length of neurites, inhibiting neuronal cell viability and migration, and promoting apoptosis. Therefore, MSNP1AS is an important lncRNA related to ASD and can regulate the biological function of neurons.

Derivation of two iPSC lines from a sporadic ASD patient (NUIGi033-A) and a paternal control (NUIGi034-A)

Hundreds of rare risk factors have been identified for ASD, however, the underlying causes for ~70% of sporadic cases are unknown. Sporadic ASD models are thus essential for validating phenotypic commonality and drug suitability to the majority of patients. Here, we derived induced pluripotent stem cells (iPSCs) from one sporadic ASD child and one paternal control, using non-integrating Sendai viral methods. The iPSCs strongly expressed pluripotency markers and could be differentiated into three germ layers. Their normal karyotype was validated by genome SNP array. The availability of sporadic ASD-derived iPSCs offers an opportunity for phenotypic comparison with genetic ASD models.

Derivation of familial iPSC lines from three ASD patients carrying NRXN1α+/- and two controls (NUIGi022-A, NUIGi022-B; NUIGi023-A, NUIGi023-B; NUIGi025-A, NUIGi025-B; NUIGi024-A, NUIGi024-B; NUIGi026-A, NUIGi026-B)

NRXN1 copy number variation is a rare genetic factor commonly shared among autism spectrum disorder (ASD), schizophrenia, intellectual disability, epilepsy and developmental delay. Human induced pluripotent stem cells (iPSCs) are essential for disease modeling and drug discovery, but familial cases are particularly rare. We report here the derivation of familial iPSC lines from two controls and three ASD patients carrying NRXN1α^+/-, using a non-integrating Sendai viral kit. The genotype and karyotype of the resulting iPSCs were validated by whole genome SNP array. All iPSC lines expressed comparable levels of pluripotency markers and could be differentiated into three germ layers.

Big data approaches to decomposing heterogeneity across the autism spectrum

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

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer

Background

Epidemiological and clinical evidence points to cancer as a comorbidity in people with autism spectrum disorders (ASD). A significant overlap of genes and biological processes between both diseases has also been reported.

Methods

Here, for the first time, we compared the gene expression profiles of ASD frontal cortex tissues and 22 cancer types obtained by differential expression meta-analysis and report gene, pathway, and drug set-based overlaps between them.

Results

Four cancer types (brain, thyroid, kidney, and pancreatic cancers) presented a significant overlap in gene expression deregulations in the same direction as ASD whereas two cancer types (lung and prostate cancers) showed differential expression profiles significantly deregulated in the opposite direction from ASD. Functional enrichment and LINCS L1000 based drug set enrichment analyses revealed the implication of several biological processes and pathways that were affected jointly in both diseases, including impairments of the immune system, and impairments in oxidative phosphorylation and ATP synthesis among others. Our data also suggest that brain and kidney cancer have patterns of transcriptomic dysregulation in the PI3K/AKT/MTOR axis that are similar to those found in ASD.

Conclusions

Comparisons of ASD and cancer differential gene expression meta-analysis results suggest that brain, kidney, thyroid, and pancreatic cancers are candidates for direct comorbid associations with ASD. On the other hand, lung and prostate cancers are candidates for inverse comorbid associations with ASD. Joint perturbations in a set of specific biological processes underlie these associations which include several pathways previously implicated in both cancer and ASD encompassing immune system alterations, impairments of energy metabolism, cell cycle, and signaling through PI3K and G protein-coupled receptors among others. These findings could help to explain epidemiological observations pointing towards direct and inverse comorbid associations between ASD and specific cancer types and depict a complex scenario regarding the molecular patterns of association between ASD and cancer.

Electronic supplementary material

The online version of this article (10.1186/s13229-019-0262-8) contains supplementary material, which is available to authorized users.

Cross-Cultural Validation of the Polish Version of the ADI-R, Including New Algorithms for Toddlers and Young Preschoolers

Autism Diagnostic Interview-Revised (ADI-R) is one of the most widely used standardized diagnostic instruments for autism spectrum disorder (ASD). This article presents findings from the validation of the Polish version of the Autism Diagnostic Interview-Revised (ADI-R-PL), including new algorithms for toddlers and preschoolers. The validation group consisted of 125 participants: 65 with Autism Spectrum Disorder (ASD group) and 60 in the control group, including individuals with non-ASD disorders and typical development. The normalization group consisted of 178 participants, including 118 with ASD. The ADI-R-PL was found to have good psychometric properties. Confirmatory factor analysis supported both a bifactor structure and three-factor model. The study has generated preliminary information about the psychometric properties of the new algorithms for toddlers and young preschoolers. To the best of our knowledge, this paper is the first to propose new cutoffs in three ADI-R domains for a non-English-speaking population.

From bedside to bench and back: Translating ASD models

Autism spectrum disorders (ASD) represent a heterogeneous group of disorders defined by deficits in social interaction/communication and restricted interests, behaviors, or activities. Models of ASD, developed based on clinical data and observations, are used in basic science, the "bench," to better understand the pathophysiology of ASD and provide therapeutic options for patients in the clinic, the "bedside." Translational medicine creates a bridge between the bench and bedside that allows for clinical and basic science discoveries to challenge one another to improve the opportunities to bring novel therapies to patients. From the clinical side, biomarker work is expanding our understanding of possible mechanisms of ASD through measures of behavior, genetics, imaging modalities, and serum markers. These biomarkers could help to subclassify patients with ASD in order to better target treatments to a more homogeneous groups of patients most likely to respond to a candidate therapy. In turn, basic science has been responding to developments in clinical evaluation by improving bench models to mechanistically and phenotypically recapitulate the ASD phenotypes observed in clinic. While genetic models are identifying novel therapeutics targets at the bench, the clinical efforts are making progress by defining better outcome measures that are most representative of meaningful patient responses. In this review, we discuss some of these challenges in translational research in ASD and strategies for the bench and bedside to bridge the gap to achieve better benefits to patients.

Study protocol for the Australian autism biobank: an international resource to advance autism discovery research

BACKGROUND

The phenotypic and genetic heterogeneity of autism spectrum disorder (ASD) presents considerable challenges in understanding etiological pathways, selecting effective therapies, providing genetic counselling, and predicting clinical outcomes. With advances in genetic and biological research alongside rapid-pace technological innovations, there is an increasing imperative to access large, representative, and diverse cohorts to advance knowledge of ASD. To date, there has not been any single collective effort towards a similar resource in Australia, which has its own unique ethnic and cultural diversity. The Australian Autism Biobank was initiated by the Cooperative Research Centre for Living with Autism (Autism CRC) to establish a large-scale repository of biological samples and detailed clinical information about children diagnosed with ASD to facilitate future discovery research.

METHODS

The primary group of participants were children with a confirmed diagnosis of ASD, aged between 2 and 17 years, recruited through four sites in Australia. No exclusion criteria regarding language level, cognitive ability, or comorbid conditions were applied to ensure a representative cohort was recruited. Both biological parents and siblings were invited to participate, along with children without a diagnosis of ASD, and children who had been queried for an ASD diagnosis but did not meet diagnostic criteria. All children completed cognitive assessments, with probands and parents completing additional assessments measuring ASD symptomatology. Parents completed questionnaires about their child's medical history and early development. Physical measurements and biological samples (blood, stool, urine, and hair) were collected from children, and physical measurements and blood samples were collected from parents. Samples were sent to a central processing site and placed into long-term storage.

DISCUSSION

The establishment of this biobank is a valuable international resource incorporating detailed clinical and biological information that will help accelerate the pace of ASD discovery research. Recruitment into this study has also supported the feasibility of large-scale biological sample collection in children diagnosed with ASD with comprehensive phenotyping across a wide range of ages, intellectual abilities, and levels of adaptive functioning. This biological and clinical resource will be open to data access requests from national and international researchers to support future discovery research that will benefit the autistic community.

Characterizing the Interplay Between Autism Spectrum Disorder and Comorbid Medical Conditions: An Integrative Review

Co-occurring medical disorders and associated physiological abnormalities in individuals with autism spectrum disorder (ASD) may provide insight into causal pathways or underlying biological mechanisms. Here, we review medical conditions that have been repeatedly highlighted as sharing the strongest associations with ASD-epilepsy, sleep, as well as gastrointestinal and immune functioning. We describe within each condition their prevalence, associations with behavior, and evidence for successful treatment. We additionally discuss research aiming to uncover potential aetiological mechanisms. We then consider the potential interaction between each group of conditions and ASD and, based on the available evidence, propose a model that integrates these medical comorbidities in relation to potential shared aetiological mechanisms. Future research should aim to systematically examine the interactions between these physiological systems, rather than considering these in isolation, using robust and sensitive biomarkers across an individual's development. A consideration of the overlap between medical conditions and ASD may aid in defining biological subtypes within ASD and in the development of specific targeted interventions.