Meta-Analysis of Gene Expression in Autism Spectrum Disorder

Dysregulated plasma autoantibodies are associated with B cell dysfunction in young Arab children with autism spectrum disorder in Qatar

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impaired social interaction and communication, as well as the occurrence of stereotyped and repetitive behaviors. Previous studies have provided solid evidence of dysregulated immune system in ASD; however, limited studies have investigated autoantibody profiles in individuals with ASD. This study aims to screen plasma autoantibodies in a well-defined cohort of young children with ASD (n = 100) and their matched controls (n = 60) utilizing a high-throughput KoRectly Expressed (KREX) i-Ome protein-array technology. We identified differential protein expression of 16 autoantibodies in ASD, which were correlated with differential gene expression of these markers in independent ASD cohorts. Meanwhile, we identified a distinct list of 33 autoantibodies associated with ASD severity; several of which were correlated with maternal age and birth weight in ASD. In addition, we found dysregulated numbers of circulating B cells and activated HLADR+ B cells in ASD, which were correlated with altered levels of several autoantibodies. Further in-depth analysis of B cell subpopulations revealed an increased frequency of activated naïve B cells in ASD, as well as an association of resting naïve B cells and transitional B cells with ASD severity. Pathway enrichment analysis revealed disrupted MAPK signaling in ASD, suggesting a potential relevance of this pathway to altered autoantibodies and B cell dysfunction in ASD. Finally, we found that a combination of eight autoantibodies associated with ASD severity showed an area under the curve (ROC-AUC) of 0.937 (95% CI = 0.890, 0.983; p < 0.001), which demonstrated the diagnostic accuracy of the eight-marker signature in the severity classification of ASD cases. Overall, this study determined dysregulated autoantibody profiles and B cell dysfunction in children with ASD and identified an eight-autoantibody panel for ASD severity classification.

Beyond the Spectrum: Subtype-Specific Molecular Insights into Autism Spectrum Disorder Via Multimodal Data Integration

Some toddlers with autism spectrum disorder (ASD) have mild social symptoms and developmental improvement in skills, but for others, symptoms and abilities are moderately or even severely affected. Those with profound autism have the most severe social, language, and cognitive symptoms and are at the greatest risk of having a poor developmental outcome. The little that is known about the underlying biology of this important profound autism subtype, points clearly to embryonic dysregulation of proliferation, differentiation and neurogenesis. Because it is essential to gain foundational knowledge of the molecular biology associated with profound, moderate, and mild autism clinical subtypes, we used well-validated, data-driven patient subtyping methods to integrate clinical and molecular data at 1 to 3 years of age in a cohort of 363 ASD and controls representative of the general pediatric population in San Diego County. Clinical data were diagnostic, language, cognitive and adaptive ability scores. Molecular measures were 50 MSigDB Hallmark gene pathway activity scores derived from RNAseq gene expression. Subtyping identified four ASD, typical and mixed diagnostic clusters. 93% of subjects in one cluster were profound autism and 93% in a different cluster were control toddlers; a third cluster was 76% moderate ability ASD; and the last cluster was a mix of mild ASD and control toddlers. Among the four clusters, the profound autism subtype had the most severe social symptoms, language, cognitive, adaptive, social attention eye tracking, social fMRI activation, and age-related decline in abilities, while mild autism toddlers mixed within typical and delayed clusters had mild social symptoms, and neurotypical language, cognitive and adaptive scores that improved with age compared with profound and moderate autism toddlers in other clusters. In profound autism, 7 subtype- specific dysregulated gene pathways were found; they control embryonic proliferation, differentiation, neurogenesis, and DNA repair. To find subtype- common dysregulated pathways, we compared all ASD vs TD and found 17 ASD subtype- common dysregulated pathways. These common pathways showed a severity gradient with the greatest dysregulation in profound and least in mild. Collectively, results raise the new hypothesis that the continuum of ASD heterogeneity is moderated by subtype- common pathways and the distinctive nature of profound autism is driven by the differentially added profound subtype- specific embryonic pathways .

Multi-omics analyses demonstrate the modulating role of gut microbiota on the associations of unbalanced dietary intake with gastrointestinal symptoms in children with autism spectrum disorder

Our previous work revealed that unbalanced dietary intake was an important independent factor associated with constipation and gastrointestinal (GI) symptoms in children with autism spectrum disorder (ASD). Growing evidence has shown the alterations in the gut microbiota and gut microbiota-derived metabolites in ASD. However, how the altered microbiota might affect the associations between unbalanced diets and GI symptoms in ASD remains unknown. We analyzed microbiome and metabolomics data in 90 ASD and 90 typically developing (TD) children based on 16S rRNA and untargeted metabolomics, together with dietary intake and GI symptoms assessment. We found that there existed 11 altered gut microbiota (FDR-corrected P-value <0.05) and 397 altered metabolites (P-value <0.05) in children with ASD compared with TD children. Among the 11 altered microbiota, the Turicibacter, Coprococcus 1, and Lachnospiraceae FCS020 group were positively correlated with constipation (FDR-corrected P-value <0.25). The Eggerthellaceae was positively correlated with total GI symptoms (FDR-corrected P-value <0.25). More importantly, three increased microbiota including Turicibacter, Coprococcus 1, and Eggerthellaceae positively modulated the associations of unbalanced dietary intake with constipation and total GI symptoms, and the decreased Clostridium sp. BR31 negatively modulated their associations in ASD children (P-value <0.05). Together, the altered microbiota strengthens the relationship between unbalanced dietary intake and GI symptoms. Among the altered metabolites, ten metabolites derived from microbiota (Turicibacter, Coprococcus 1, Eggerthellaceae, and Clostridium sp. BR31) were screened out, enriched in eight metabolic pathways, and were identified to correlate with constipation and total GI symptoms in ASD children (FDR-corrected P-value <0.25). These metabolomics findings further support the modulating role of gut microbiota on the associations of unbalanced dietary intake with GI symptoms. Collectively, our research provides insights into the relationship between diet, the gut microbiota, and GI symptoms in children with ASD.

Diversity and Classification of Genetic Variations in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition with symptoms that affect the whole personality and all aspects of life. Although there is a high degree of heterogeneity in both its etiology and its characteristic behavioral patterns, the disorder is well-captured along the autistic triad. Currently, ASD status can be confirmed following an assessment of behavioral features, but there is a growing emphasis on conceptualizing autism as a spectrum, which allows for establishing a diagnosis based on the level of support need, free of discrete categories. Since ASD has a high genetic predominance, the number of genetic variations identified in the background of the condition is increasing exponentially as genetic testing methods are rapidly evolving. However, due to the huge amount of data to be analyzed, grouping the different DNA variations is still challenging. Therefore, in the present review, a multidimensional classification scheme was developed to accommodate most of the currently known genetic variants associated with autism. Genetic variations have been grouped according to six criteria (extent, time of onset, information content, frequency, number of genes involved, inheritance pattern), which are themselves not discrete categories, but form a coherent continuum in line with the autism spectrum approach.

A meta-analysis of differentially expressed and regulatory genes with their functional enrichment analysis for brain transcriptome data in autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent challenges in social interactions and repetitive behavioral patterns. It is a significant problem emerging worldwide, as one in 100 children is affected by this disorder globally. In this study, a meta-analysis was performed for the identification of differentially expressed genes (DEGs) along with the expression analysis of regulatory genes. Functional enrichment analysis was an integral part of current findings to notify the significant pathways of this complex disorder. The study was conducted with two RNA-Seq datasets, viz., GSE64018 and GSE62098, for ASD patients and control samples from the GEO database. The identification of up-regulatory and down-regulatory genes was performed by the interaction analysis of transcription factors (TF) and DEGs. As an outcome of the meta-analysis, 2543 DEGs were identified as common across both of the datasets in which 1402 DEGs exhibited upregulation and 1130 genes have shown downregulation. In network analysis, upregulatory genes have shown strong interaction while downregulatory genes exhibit weak or null interaction. Further, in the enrichment analysis of screened upregulatory DEGs, three major significant pathways were identified namely the ATP synthesis pathway, FAS signaling pathway, and the Huntington's disease pathway. The common expression of CYC 1 gene in all the identified pathways has indicated that it is an important key regulator gene for the majorly associated pathways. The study concludes that all the potential DEGs were found to show their related high expression in neurobiological regulations specifically with ASD.Communicated by Ramaswamy S. Sarma.

A predictive ensemble classifier for the gene expression diagnosis of ASD at ages 1 to 4 years

Autism Spectrum Disorder (ASD) diagnosis remains behavior-based and the median age of diagnosis is ~52 months, nearly 5 years after its first-trimester origin. Accurate and clinically-translatable early-age diagnostics do not exist due to ASD genetic and clinical heterogeneity. Here we collected clinical, diagnostic, and leukocyte RNA data from 240 ASD and typically developing (TD) toddlers (175 toddlers for training and 65 for test). To identify gene expression ASD diagnostic classifiers, we developed 42,840 models composed of 3570 gene expression feature selection sets and 12 classification methods. We found that 742 models had AUC-ROC ≥ 0.8 on both Training and Test sets. Weighted Bayesian model averaging of these 742 models yielded an ensemble classifier model with accurate performance in Training and Test gene expression datasets with ASD diagnostic classification AUC-ROC scores of 85-89% and AUC-PR scores of 84-92%. ASD toddlers with ensemble scores above and below the overall ASD ensemble mean of 0.723 (on a scale of 0 to 1) had similar diagnostic and psychometric scores, but those below this ASD ensemble mean had more prenatal risk events than TD toddlers. Ensemble model feature genes were involved in cell cycle, inflammation/immune response, transcriptional gene regulation, cytokine response, and PI3K-AKT, RAS and Wnt signaling pathways. We additionally collected targeted DNA sequencing smMIPs data on a subset of ASD risk genes from 217 of the 240 ASD and TD toddlers. This DNA sequencing found about the same percentage of SFARI Level 1 and 2 ASD risk gene mutations in TD (12 of 105) as in ASD (13 of 112) toddlers, and classification based only on the presence of mutation in these risk genes performed at a chance level of 49%. By contrast, the leukocyte ensemble gene expression classifier correctly diagnostically classified 88% of TD and ASD toddlers with ASD risk gene mutations. Our ensemble ASD gene expression classifier is diagnostically predictive and replicable across different toddler ages, races, and ethnicities; out-performs a risk gene mutation classifier; and has potential for clinical translation.

Nuclear access of DNlg3 c-terminal fragment and its function in regulating innate immune response genes

Neuroligins (NLGNs) are one of the autism susceptibility genes, however, the mechanism that how dysfunction of NLGNs leads to Autism remains unclear. More and more studies have shown that the transcriptome alteration may be one of the important factors to generate Autism. Therefore, we are very concerned about whether Neuroligins would affect transcriptional regulation, which may at last lead to Autism. As a single-transmembrane receptor, proteolytic cleavage is one of the most important posttranslational modifications of NLGN proteins. In this study, we demonstrated the existence of DNlg3 C-terminal fragment. Studies in the S2 cells and HEK293T cells showed the evidence for nuclear access of the DNlg3 C-terminal fragment. Then we identified the possible targets of DNlg3 C-terminal fragment after its nuclear access by RNA-seq. The bioinformatics analysis indicated the transcriptome alteration between dnlg3 null flies and wild type flies focused on genes for the innate immune responses. These results were consistent with the infection hypotheses for autism. Our study revealed the nuclear access ability of DNlg3 c-terminal fragment and its possible function in transcriptional regulation of the innate immune response genes. This work provides the new links between synaptic adhesion molecule NLGNs and immune activation, which may help us to get a deeper understanding on the relationship between NLGNs and Autism.

Recent developments and future directions in meta-analysis of differential gene expression in livestock RNA-Seq

Decreases in the costs of high-throughput sequencing technologies have led to continually increasing numbers of livestock RNA-Seq studies in the last decade. Although the number of studies has increased dramatically, most livestock RNA-Seq experiments are limited by cost to a small number of biological replicates. Meta-analysis procedures can be used to integrate and jointly analyze data from multiple independent studies. Meta-analyses increase the sample size, which in turn increase both statistical power and robustness of the results. In this work, we discuss cutting edge approaches to combining results from multiple independent RNA-Seq studies to improve livestock transcriptomics research. We review currently published RNA-Seq meta-analyses in livestock, describe many of the key issues specific to RNA-Seq meta-analysis in livestock species, and discuss future perspectives.

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.

A cross-species spatiotemporal proteomic analysis identifies UBE3A-dependent signaling pathways and targets

Angelman syndrome (AS) is a severe neurodevelopmental disorder caused by the loss of neuronal E3 ligase UBE3A. Restoring UBE3A levels is a potential disease-modifying therapy for AS and has recently entered clinical trials. There is paucity of data regarding the molecular changes downstream of UBE3A hampering elucidation of disease therapeutics and biomarkers. Notably, UBE3A plays an important role in the nucleus but its targets have yet to be elucidated. Using proteomics, we assessed changes during postnatal cortical development in an AS mouse model. Pathway analysis revealed dysregulation of proteasomal and tRNA synthetase pathways at all postnatal brain developmental stages, while synaptic proteins were altered in adults. We confirmed pathway alterations in an adult AS rat model across multiple brain regions and highlighted region-specific differences. UBE3A reinstatement in AS model mice resulted in near complete and partial rescue of the proteome alterations in adolescence and adults, respectively, supporting the notion that restoration of UBE3A expression provides a promising therapeutic option. We show that the nuclear enriched transketolase (TKT), one of the most abundantly altered proteins, is a novel direct UBE3A substrate and is elevated in the neuronal nucleus of rat brains and human iPSC-derived neurons. Taken together, our study provides a comprehensive map of UBE3A-driven proteome remodeling in AS across development and species, and corroborates an early UBE3A reinstatement as a viable therapeutic option. To support future disease and biomarker research, we present an accessible large-scale multi-species proteomic resource for the AS community ( https://www.angelman-proteome-project.org/ ).

Alternative splicing and gene co-expression network-based analysis of dizygotic twins with autism-spectrum disorder and their parents

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with high heritability, however, understanding the complexity of the underlying genetic basis has proven to be a challenging task. We hypothesized that dissecting the aberrations in alternative splicing (AS) and their effects on expression networks might provide insight. Therefore, we performed AS and co-expression analyses of total RNA isolated from Peripheral Blood Mononuclear Cells (PBMCs) of two pairs of dizygotic (DZ) twins with non-syndromic autism and their parents. We identified 183 differential AS events in 146 genes, seven of them being Simons Foundation Autism Research Initiative (SFARI) Category 1-3 genes, three of which had previously been reported to be alternatively spliced in ASD post-mortem brains. Gene co-expression analysis identified 7 modules with 513 genes, 5 of which were SFARI Category 1 or Category 2 genes. Among differentially AS genes within the modules, ZNF322 and NR4A1 could be potentially interesting targets for further investigations.

Brain-immune interactions in neuropsychiatric disorders: Lessons from transcriptome studies for molecular targeting

Understanding the pathophysiological mechanisms of neuropsychiatric disorders has been a challenging quest for neurobiologists. Recent years have witnessed enormous technological advances in the field of neuroimmunology, blurring boundaries between the central nervous system and the periphery. Consequently, the discipline has expanded to cover interactions between the nervous and immune systems in health and diseases. The complex interplay between the peripheral and central immune pathways in neuropsychiatric disorders has recently been documented in various studies, but the genetic determinants remain elusive. Recent transcriptome studies have identified dysregulated genes involved in peripheral immune cell activation, blood-brain barrier integrity, glial cell activation, and synaptic plasticity in major depressive disorder, bipolar disorder, autism spectrum disorder, and schizophrenia. Herein, the key transcriptomic techniques applied in investigating differentially expressed genes and pathways responsible for altered brain-immune interactions in neuropsychiatric disorders are discussed. The application of transcriptomics that can aid in identifying molecular targets in various neuropsychiatric disorders is highlighted.

Differential and spatial expression meta-analysis of genes identified in genome-wide association studies of depression

Major depressive disorder (MDD) is the most prevalent psychiatric disorder worldwide and affects individuals of all ages. It causes significant psychosocial impairments and is a major cause of disability. A recent consortium study identified 102 genetic variants and 269 genes associated with depression. To provide targets for future depression research, we prioritized these recently identified genes using expression data. We examined the differential expression of these genes in three studies that profiled gene expression of MDD cases and controls across multiple brain regions. In addition, we integrated anatomical expression information to determine which brain regions and transcriptomic cell types highly express the candidate genes. We highlight 12 of the 269 genes with the most consistent differential expression: MANEA, UBE2M, CKB, ITPR3, SPRY2, SAMD5, TMEM106B, ZC3H7B, LST1, ASXL3, ZNF184 and HSPA1A. The majority of these top genes were found to have sex-specific differential expression. We place greater emphasis on ZNF184 as it is the top gene in a more conservative analysis of the 269. Specifically, the differential expression of ZNF184 was strongest in subcortical regions in males and females. Anatomically, our results suggest the importance of the dorsal lateral geniculate nucleus, cholinergic, monoaminergic and enteric neurons. These findings provide a guide for targeted experiments to advance our understanding of the genetic underpinnings of depression.

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.

Comprehensive Analysis of RNA-Seq Gene Expression Profiling of Brain Transcriptomes Reveals Novel Genes, Regulators, and Pathways in Autism Spectrum Disorder

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with deficits in social communication ability and repetitive behavior. The pathophysiological events involved in the brain of this complex disease are still unclear.

METHODS

In this study, we aimed to profile the gene expression signatures of brain cortex of ASD patients, by using two publicly available RNA-seq studies, in order to discover new ASD-related genes.

RESULTS

We detected 1567 differentially expressed genes (DEGs) by meta-analysis, where 1194 were upregulated and 373 were downregulated genes. Several ASD-related genes previously reported were also identified. Our meta-analysis identified 235 new DEGs that were not detected using the individual RNA-seq studies used. Some of those genes, including seven DEGs (PAK1, DNAH17, DOCK8, DAPP1, PCDHAC2, and ERBIN, SLC7A7), have been confirmed in previous reports to be associated with ASD. Gene Ontology (GO) and pathways analysis showed several molecular pathways enriched by the DEGs, namely, osteoclast differentiation, TNF signaling pathway, complement and coagulation cascade. Topological analysis of protein-protein interaction of the ASD brain cortex revealed proteomics hub gene signatures: MYC, TP53, HDAC1, CDK2, BAG3, CDKN1A, GABARAPL1, EZH2, VIM, and TRAF1. We also identified the transcriptional factors (TFs) regulating DEGs, namely, FOXC1, GATA2, YY1, FOXL1, USF2, NFIC, NFKB1, E2F1, TFAP2A, HINFP.

CONCLUSION

Novel core genes and molecular signatures involved with ASD were identified by our meta-analysis.

Association between MTHFR C677T/A1298C and susceptibility to autism spectrum disorders: a meta-analysis

BACKGROUND

Autism spectrum disorder (ASD) is becoming increasingly prevalent of late. Methylenetetrahydrofolate reductase (MTHFR) has a significant role in folate metabolism. Owing to the inconsistencies and inconclusiveness on the association between MTHFR single nucleotide polymorphism (SNP) and ASD susceptibilities, a meta-analysis was conducted to settle the inconsistencies.

METHODS

For this meta-analysis, a total of 15 manuscripts published up to January 26, 2020, were selected from PubMed, Google Scholar, Medline, WangFang, and CNKI databases using search terms "MTHFR" OR "methylenetetrahydrofolate reductase" AND "ASD" OR "Autism Spectrum Disorders" OR "Autism" AND "polymorphism" OR "susceptibility" OR "C677T" OR "A1298C".

RESULTS

The findings of the meta-analysis indicated that MTHFR C677T polymorphism is remarkably associated with ASD in the five genetic models, viz., allelic, dominant, recessive, heterozygote, and homozygote. However, the MTHFR A1298C polymorphism was not found to be significantly related to ASD in the five genetic models. Subgroup analyses revealed significant associations of ASD with the MTHFR (C677T and A1298C) polymorphism. Sensitivity analysis showed that this meta-analysis was stable and reliable. No publication bias was identified in the associations between MTHFRC677T polymorphisms and ASD in the five genetic models, except for the one with regard to the associations between MTHFRA1298C polymorphisms and ASD in the five genetic models.

CONCLUSION

This meta-analysis showed that MTHFR C677T polymorphism is a susceptibility factor for ASD, and MTHFR A1298C polymorphism is not associated with ASD susceptibility.

Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements

An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.

A survey of gene expression meta-analysis: methods and applications

The increasing use of high-throughput gene expression quantification technologies over the last two decades and the fact that most of the published studies are stored in public databases has triggered an explosion of studies available through public repositories. All this information offers an invaluable resource for reuse to generate new knowledge and scientific findings. In this context, great interest has been focused on meta-analysis methods to integrate and jointly analyze different gene expression datasets. In this work, we describe the main steps in the gene expression meta-analysis, from data preparation to the state-of-the art statistical methods. We also analyze the main types of applications and problems that can be approached in gene expression meta-analysis studies and provide a comparative overview of the available software and bioinformatics tools. Moreover, a practical guide for choosing the most appropriate method in each case is also provided.

New Horizons for Molecular Genetics Diagnostic and Research in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a highly heritable, heterogeneous, and complex pervasive neurodevelopmental disorder (PND) characterized by distinctive abnormalities of human cognitive functions, social interaction, and speech development.Nowadays, several genetic changes including chromosome abnormalities, genetic variations, transcriptional epigenetics, and noncoding RNA have been identified in ASD. However, the association between these genetic modifications and ASDs has not been confirmed yet.The aim of this review is to summarize the key findings in ASD from genetic viewpoint that have been identified from the last few decades of genetic and molecular research.

RNA sequencing of identical twins discordant for autism reveals blood-based signatures implicating immune and transcriptional dysregulation

Background

A gap exists in our mechanistic understanding of how genetic and environmental risk factors converge at the molecular level to result in the emergence of autism symptoms. We compared blood-based gene expression signatures in identical twins concordant and discordant for autism spectrum condition (ASC) to differentiate genetic and environmentally driven transcription differences, and establish convergent evidence for biological mechanisms involved in ASC.

Methods

Genome-wide gene expression data were generated using RNA-seq on whole blood samples taken from 16 pairs of monozygotic (MZ) twins and seven twin pair members (39 individuals in total), who had been assessed for ASC and autism traits at age 12. Differential expression (DE) analyses were performed between (a) affected and unaffected subjects (N = 36) and (b) within discordant ASC MZ twin pairs (total N = 11) to identify environmental-driven DE. Gene set enrichment and pathway testing was performed on DE gene lists. Finally, an integrative analysis using DNA methylation data aimed to identify genes with consistent evidence for altered regulation in cis.

Results

In the discordant twin analysis, three genes showed evidence for DE at FDR < 10%: IGHG4, EVI2A and SNORD15B. In the case-control analysis, four DE genes were identified at FDR < 10% including IGHG4, PRR13P5, DEPDC1B, and ZNF501. We find enrichment for DE of genes curated in the SFARI human gene database. Pathways showing evidence of enrichment included those related to immune cell signalling and immune response, transcriptional control and cell cycle/proliferation. Integrative methylomic and transcriptomic analysis identified a number of genes showing suggestive evidence for cis dysregulation.

Limitations

Identical twins stably discordant for ASC are rare, and as such the sample size was limited and constrained to the use of peripheral blood tissue for transcriptomic and methylomic profiling. Given these primary limitations, we focused on transcript-level analysis.

Conclusions

Using a cohort of ASC discordant and concordant MZ twins, we add to the growing body of transcriptomic-based evidence for an immune-based component in the molecular aetiology of ASC. Whilst the sample size was limited, the study demonstrates the utility of the discordant MZ twin design combined with multi-omics integration for maximising the potential to identify disease-associated molecular signals.

Improving the classification of neuropsychiatric conditions using gene ontology terms as features

Although neuropsychiatric disorders have an established genetic background, their molecular foundations remain elusive. This has prompted many investigators to search for explanatory biomarkers that can predict clinical outcomes. One approach uses machine learning to classify patients based on blood mRNA expression. However, these endeavors typically fail to achieve the high level of performance, stability, and generalizability required for clinical translation. Moreover, these classifiers can lack interpretability because not all genes have relevance to researchers. For this study, we hypothesized that annotation-based classifiers can improve classification performance, stability, generalizability, and interpretability. To this end, we evaluated the models of four classification algorithms on six neuropsychiatric data sets using four annotation databases. Our results suggest that the Gene Ontology Biological Process database can transform gene expression into an annotation-based feature space that is accurate and stable. We also show how annotation features can improve the interpretability of classifiers: as annotations are used to assign biological importance to genes, the biological importance of annotation-based features are the features themselves. In evaluating the annotation features, we find that top ranked annotations tend contain top ranked genes, suggesting that the most predictive annotations are a superset of the most predictive genes. Based on this, and the fact that annotations are used routinely to assign biological importance to genetic data, we recommend transforming gene-level expression into annotation-level expression prior to the classification of neuropsychiatric conditions.

Solving for X: Evidence for sex-specific autism biomarkers across multiple transcriptomic studies

Autism spectrum disorder (ASD) is a markedly heterogeneous condition with a varied phenotypic presentation. Its high concordance among siblings, as well as its clear association with specific genetic disorders, both point to a strong genetic etiology. However, the molecular basis of ASD is still poorly understood, although recent studies point to the existence of sex-specific ASD pathophysiologies and biomarkers. Despite this, little is known about how exactly sex influences the gene expression signatures of ASD probands. In an effort to identify sex-dependent biomarkers and characterize their function, we present an analysis of a single paired-end postmortem brain RNA-Seq data set and a meta-analysis of six blood-based microarray data sets. Here, we identify several genes with sex-dependent dysregulation, and many more with sex-independent dysregulation. Moreover, through pathway analysis, we find that these sex-independent biomarkers have substantially different biological roles than the sex-dependent biomarkers, and that some of these pathways are ubiquitously dysregulated in both postmortem brain and blood. We conclude by synthesizing the discovered biomarker profiles with the extant literature, by highlighting the advantage of studying sex-specific dysregulation directly, and by making a call for new transcriptomic data that comprise large female cohorts.

Molecular windows into the human brain for psychiatric disorders

Delineating the pathophysiology of psychiatric disorders has been extremely challenging but technological advances in recent decades have facilitated a deeper interrogation of molecular processes in the human brain. Initial candidate gene expression studies of the postmortem brain have evolved into genome wide profiling of the transcriptome and the epigenome, a critical regulator of gene expression. Here, we review the potential and challenges of direct molecular characterization of the postmortem human brain, and provide a brief overview of recent transcriptional and epigenetic studies with respect to neuropsychiatric disorders. Such information can now be leveraged and integrated with the growing number of genome-wide association databases to provide a functional context of trait-associated genetic variants linked to psychiatric illnesses and related phenotypes. While it is clear that the field is still developing and challenges remain to be surmounted, these recent advances nevertheless hold tremendous promise for delineating the neurobiological underpinnings of mental diseases and accelerating the development of novel medication strategies.

Clinical and Molecular Characteristics of Mitochondrial Dysfunction in Autism Spectrum Disorder

Autism spectrum disorder (ASD) affects ~ 2% of children in the United States. The etiology of ASD likely involves environmental factors triggering physiological abnormalities in genetically sensitive individuals. One of these major physiological abnormalities is mitochondrial dysfunction, which may affect a significant subset of children with ASD. Here we systematically review the literature on human studies of mitochondrial dysfunction related to ASD. Clinical aspects of mitochondrial dysfunction in ASD include unusual neurodevelopmental regression, especially if triggered by an inflammatory event, gastrointestinal symptoms, seizures, motor delays, fatigue and lethargy. Traditional biomarkers of mitochondrial disease are widely reported to be abnormal in ASD, but appear non-specific. Newer biomarkers include buccal cell enzymology, biomarkers of fatty acid metabolism, non-mitochondrial enzyme function, apoptosis markers and mitochondrial antibodies. Many genetic abnormalities are associated with mitochondrial dysfunction in ASD, including chromosomal abnormalities, mitochondrial DNA mutations and large-scale deletions, and mutations in both mitochondrial and non-mitochondrial nuclear genes. Mitochondrial dysfunction has been described in immune and buccal cells, fibroblasts, muscle and gastrointestinal tissue and the brains of individuals with ASD. Several environmental factors, including toxicants, microbiome metabolites and an oxidized microenvironment are shown to modulate mitochondrial function in ASD tissues. Investigations of treatments for mitochondrial dysfunction in ASD are promising but preliminary. The etiology of mitochondrial dysfunction and how to define it in ASD is currently unclear. However, preliminary evidence suggests that the mitochondria may be a fruitful target for treatment and prevention of ASD. Further research is needed to better understand the role of mitochondrial dysfunction in the pathophysiology of ASD.

Genome-Wide Expression Profiles for Ischemic Stroke: A Meta-Analysis

BACKGROUND

Genome-wide expression studies (GWES), using microarray platforms, have allowed a deeper understanding of the molecular factors involved in the pathophysiology of ischemic stroke (IS), one of the main global causes of mortality and disability.

METHODS

In the current work, we carried out a meta-analysis of available GWES for IS. Bioinformatics and computational biology analyses were applied to identify enriched functional categories and convergence with other genomic datasets for IS.

RESULTS

Three primary datasets were included and in the meta-analyses for GWES and IS, 41 differentially expressed (DE) genes were identified using a random effects model. Thirteen of these genes were downregulated and 28 were upregulated. An analysis of functional categories found a significant enrichment for the Gene Ontology Term "Inflammatory Response" and for binding sites for the PAX2 transcription factor.

CONCLUSIONS

The list of DE genes identified in this meta-analysis of GWES for IS is useful for future genetic and molecular studies, which would allow the identification of novel mechanisms involved in the pathophysiology of IS. Several of the DE genes found in this meta-analysis have known functional roles related to mechanisms involved in the pathophysiology of IS. It is recognized the role of the inflammatory response in the pathophysiology of IS.

The role of reduced expression of fragile X mental retardation protein in neurons and increased expression in astrocytes in idiopathic and syndromic autism (duplications 15q11.2-q13)

Fragile X syndrome (FXS), caused by lack of fragile X mental retardation protein (FMRP), is associated with a high prevalence of autism. The deficit of FMRP reported in idiopathic autism suggests a mechanistic overlap between FXS and autism. The overall goal of this study is to detect neuropathological commonalities of FMRP deficits in the brains of people with idiopathic autism and with syndromic autism caused by dup15q11.2-q13 (dup15). This study tests the hypothesis based on our preliminary data that both idiopathic and syndromic autism are associated with brain region-specific deficits of neuronal FMRP and structural changes of the affected neurons. This immunocytochemical study revealed neuronal FMRP deficits and shrinkage of deficient neurons in the cerebral cortex, subcortical structures, and cerebellum in subjects with idiopathic and dup(15)/autism. Neuronal FMRP deficit coexists with surprising infiltration of the brains of autistic children and adults with FMRP-positive astrocytes known to be typical only for the fetal and short postnatal periods. In the examined autistic subjects, these astrocytes selectively infiltrate the border between white and gray matter in the cerebral and cerebellar cortex, the molecular layer of the cortex, part of the amygdala and thalamus, central cerebellar white matter, and dentate nucleus. Astrocyte pathology results in an additional local loss of FMRP in neurons and their shrinkage. Neuronal deficit of FMRP and shrinkage of affected neurons in structures free of FMRP-positive astrocytes and regions infiltrated with FMRP-expressing astrocytes appear to reflect mechanistic, neuropathological, and functional commonalities of FMRP abnormalities in FXS and autism spectrum disorder. Autism Res 2018, 11: 1316-1331. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Immunocytochemistry reveals a deficit of fragile X mental retardation protein (FMRP) in neurons of cortical and subcortical brain structures but increased FMRP expression in astrocytes infiltrating gray and white matter. The detected shrinkage of FMRP-deficient neurons may provide a mechanistic explanation of reported neuronal structural and functional changes in autism. This study contributes to growing evidence of mechanistic commonalities between fragile X syndrome and autism spectrum disorder.

dbMDEGA: a database for meta-analysis of differentially expressed genes in autism spectrum disorder

Background

Autism spectrum disorders (ASD) are hereditary, heterogeneous and biologically complex neurodevelopmental disorders. Individual studies on gene expression in ASD cannot provide clear consensus conclusions. Therefore, a systematic review to synthesize the current findings from brain tissues and a search tool to share the meta-analysis results are urgently needed.

Methods

Here, we conducted a meta-analysis of brain gene expression profiles in the current reported human ASD expression datasets (with 84 frozen male cortex samples, 17 female cortex samples, 32 cerebellum samples and 4 formalin fixed samples) and knock-out mouse ASD model expression datasets (with 80 collective brain samples). Then, we applied R language software and developed an interactive shared and updated database (dbMDEGA) displaying the results of meta-analysis of data from ASD studies regarding differentially expressed genes (DEGs) in the brain.

Results

This database, dbMDEGA (https://dbmdega.shinyapps.io/dbMDEGA/), is a publicly available web-portal for manual annotation and visualization of DEGs in the brain from data from ASD studies. This database uniquely presents meta-analysis values and homologous forest plots of DEGs in brain tissues. Gene entries are annotated with meta-values, statistical values and forest plots of DEGs in brain samples. This database aims to provide searchable meta-analysis results based on the current reported brain gene expression datasets of ASD to help detect candidate genes underlying this disorder.

Conclusion

This new analytical tool may provide valuable assistance in the discovery of DEGs and the elucidation of the molecular pathogenicity of ASD. This database model may be replicated to study other disorders.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1915-2) contains supplementary material, which is available to authorized users.

Hypermasculinised facial morphology in boys and girls with Autism Spectrum Disorder and its association with symptomatology

Elevated prenatal testosterone exposure has been associated with Autism Spectrum Disorder (ASD) and facial masculinity. By employing three-dimensional (3D) photogrammetry, the current study investigated whether prepubescent boys and girls with ASD present increased facial masculinity compared to typically-developing controls. There were two phases to this research. 3D facial images were obtained from a normative sample of 48 boys and 53 girls (3.01-12.44 years old) to determine typical facial masculinity/femininity. The sexually dimorphic features were used to create a continuous 'gender score', indexing degree of facial masculinity. Gender scores based on 3D facial images were then compared for 54 autistic and 54 control boys (3.01-12.52 years old), and also for 20 autistic and 60 control girls (4.24-11.78 years). For each sex, increased facial masculinity was observed in the ASD group relative to control group. Further analyses revealed that increased facial masculinity in the ASD group correlated with more social-communication difficulties based on the Social Affect score derived from the Autism Diagnostic Observation Scale-Generic (ADOS-G). There was no association between facial masculinity and the derived Restricted and Repetitive Behaviours score. This is the first study demonstrating facial hypermasculinisation in ASD and its relationship to social-communication difficulties in prepubescent children.

A comprehensive regional analysis of genome-wide expression profiles for major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a global health challenge. In recent years, a large number of genome-wide expression studies (GWES) have been carried out to identify the transcriptomic profiles for MDD. The objective of this work was to carry out a comprehensive meta-analysis of available GWES for MDD.

METHODS

GWES for MDD with available raw data were searched in NCBI GEO, Array Express and Stanley databases. Raw GWES data were preprocessed and normalized and meta-analytical procedures were carried out with the Network Analyst program. 743 samples from 24 primary studies were included in our meta-analyses for blood (Blo), amygdala (Amy), cerebellum (Cer), anterior cingulate cortex (ACC) and prefrontal cortex (PFC) regions. A functional enrichment analysis was carried out.

RESULTS

We identified 35, 793, 231, 668 and 252 differentially expressed (DE) genes for Blo, Amy, Cer, ACC and PFC regions. A region-dependent significant enrichment for several functional categories, such as gene ontologies, signaling pathways and topographic parameters, was identified. There was convergence with other available genome-wide studies, such as GWAS, DNA methylation analyses and miRNA expression studies.

LIMITATIONS

Raw data were not available for several primary studies that have been published previously.

CONCLUSIONS

This is the largest meta-analysis for GWES in MDD. The examination of convergence of genome-wide evidence and of the functional enrichment analysis provides a global overview of potential neural signaling mechanisms dysregulated in MDD. Our comprehensive analysis of several brain regions identified lists of DE genes for MDD that are interesting candidates for further studies.

Altered gene expression in early postnatal monoamine oxidase A knockout mice

We reported previously that monoamine oxidase (MAO) A knockout (KO) mice show increased serotonin (5-hydroxytryptamine, 5-HT) levels and autistic-like behaviors characterized by repetitive behaviors, and anti-social behaviors. We showed that administration of the serotonin synthesis inhibitor para-chlorophenylalanine (pCPA) from post-natal day 1 (P1) through 7 (P7) in MAO A KO mice reduced the serotonin level to normal and reverses the repetitive behavior. These results suggested that the altered gene expression at P1 and P7 may be important for the autistic-like behaviors seen in MAO A KO mice and was studied here. In this study, Affymetrix mRNA array data for P1 and P7 MAO A KO mice were analyzed using Partek Genomics Suite and Ingenuity Pathways Analysis to identify genes differentially expressed versus wild-type and assess their functions and relationships. The number of significant differentially expressed genes (DEGs) varied with age: P1 (664) and P7 (3307) [false discovery rate (FDR) <0.05, fold-change (FC) >1.5 for autism-linked genes and >2.0 for functionally categorized genes]. Eight autism-linked genes were differentially expressed in P1 (upregulated: NLGN3, SLC6A2; down-regulated: HTR2C, MET, ADSL, MECP2, ALDH5A1, GRIN3B) while four autism-linked genes were differentially expressed at P7 (upregulated: HTR2B; downregulated: GRIN2D, GRIN2B, CHRNA4). Many other genes involved in neurodevelopment, apoptosis, neurotransmission, and cognitive function were differentially expressed at P7 in MAO A KO mice. This result suggests that modulation of these genes by the increased serotonin may lead to neurodevelopmental alteration in MAO A KO mice and results in autistic-like behaviors.

Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined-samples mega-analysis

Blood-based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject-level data from previously published studies by mega-analysis to increase the statistical power. We identified studies that compared ex vivo blood or lymphocytes from ASD-affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta-data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate-controlled mixed-effect linear models were used to identify gene transcripts and co-expression network modules that were significantly associated with diagnostic status. Permutation-based gene-set analysis was used to identify functionally related sets of genes that were over- and under-expressed among ASD samples. Our results were consistent with diminished interferon-, EGF-, PDGF-, PI3K-AKT-mTOR-, and RAS-MAPK-signaling cascades, and increased ribosomal translation and NK-cell related activity in ASD. We explored evidence for sex-differences in the ASD-related transcriptomic signature. We also demonstrated that machine-learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood-based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD-related transcriptional differences in circulating immune cells. © 2016 Wiley Periodicals, Inc.

Unveiling gene trait relationship by cross-platform meta-analysis on Chinese hamster ovary cell transcriptome

In the past few years, transcriptome analysis has been increasingly employed to better understand the physiology of Chinese hamster ovary (CHO) cells at a global level. As more transcriptome data accumulated, meta-analysis on data sets collected from various sources can potentially provide better insights on common properties of those cells. Here, we performed meta-analysis on transcriptome data of different CHO cell lines obtained using NimbleGen or Affymetrix microarray platforms. Hierarchical clustering, non-negative matrix factorization (NMF) analysis, and principal component analysis (PCA) accordantly showed the samples were clustered into two groups: one consists of adherent cells in serum-containing medium, and the other suspension cells in serum-free medium. Genes that were differentially expressed between the two clusters were enriched in a few functional classes by Database for Annotation, Visualization, and Integrated Discovery (DAVID) of which many were common with the enriched gene sets identified by Gene Set Enrichment Analysis (GSEA), including extracellular matrix (ECM) receptor interaction, cell adhesion molecules (CAMs), and lipid related metabolism pathways. Despite the heterogeneous sources of the cell samples, the adherent and suspension growth characteristics and serum-supplementation appear to be a dominant feature in the transcriptome. The results demonstrated that meta-analysis of transcriptome could uncover features in combined data sets that individual data set might not reveal. As transcriptome data sets accumulate over time, meta-analysis will become even more revealing. Biotechnol. Bioeng. 2017;114: 1583-1592. © 2017 Wiley Periodicals, Inc.

Executive Functioning Mediates the Effect of Behavioral Problems on Depression in Mothers of Children With Developmental Disabilities

The present investigation explored long-term relationships of behavioral symptoms of adolescents and adults with developmental disabilities with the mental health of their mothers. Fragile X premutation carrier mothers of an adolescent or adult child with fragile X syndrome (n = 95), and mothers of a grown child with autism (n = 213) were included. Behavioral symptoms at Time 1 were hypothesized to predict maternal depressive symptoms at Time 3 via maternal executive dysfunction at Time 2. Results provided support for the mediating pathway of executive dysfunction. Additionally, the association of behavioral symptoms with executive dysfunction differed across the two groups, suggesting that premutation carriers may be more susceptible to caregiving stress due to their genotype.

Selection of Suitable Reference Genes for Analysis of Salivary Transcriptome in Non-Syndromic Autistic Male Children

Childhood autism is a severe form of complex genetically heterogeneous and behaviorally defined set of neurodevelopmental diseases, collectively termed as autism spectrum disorders (ASD). Reverse transcriptase quantitative real-time PCR (RT-qPCR) is a highly sensitive technique for transcriptome analysis, and it has been frequently used in ASD gene expression studies. However, normalization to stably expressed reference gene(s) is necessary to validate any alteration reported at the mRNA level for target genes. The main goal of the present study was to find the most stable reference genes in the salivary transcriptome for RT-qPCR analysis in non-syndromic male childhood autism. Saliva samples were obtained from nine drug naïve non-syndromic male children with autism and also sex-, age-, and location-matched healthy controls using the RNA-stabilizer kit from DNA Genotek. A systematic two-phased measurement of whole saliva mRNA levels for eight common housekeeping genes (HKGs) was carried out by RT-qPCR, and the stability of expression for each candidate gene was analyzed using two specialized algorithms, geNorm and NormFinder, in parallel. Our analysis shows that while the frequently used HKG ACTB is not a suitable reference gene, the combination of GAPDH and YWHAZ could be recommended for normalization of RT-qPCR analysis of salivary transcriptome in non-syndromic autistic male children.

Meta-Analysis of Gene Expression Patterns in Animal Models of Prenatal Alcohol Exposure Suggests Role for Protein Synthesis Inhibition and Chromatin Remodeling

BACKGROUND

Prenatal alcohol exposure (PAE) can result in an array of morphological, behavioral, and neurobiological deficits that can range in their severity. Despite extensive research in the field and a significant progress made, especially in understanding the range of possible malformations and neurobehavioral abnormalities, the molecular mechanisms of alcohol responses in development are still not well understood. There have been multiple transcriptomic studies looking at the changes in gene expression after PAE in animal models; however, there is a limited apparent consensus among the reported findings. In an effort to address this issue, we performed a comprehensive re-analysis and meta-analysis of all suitable, publically available expression data sets.

METHODS

We assembled 10 microarray data sets of gene expression after PAE in mouse and rat models consisting of samples from a total of 63 ethanol (EtOH)-exposed and 80 control animals. We re-analyzed each data set for differential expression and then used the results to perform meta-analyses considering all data sets together or grouping them by time or duration of exposure (pre- and postnatal, acute and chronic, respectively). We performed network and Gene Ontology enrichment analysis to further characterize the identified signatures.

RESULTS

For each subanalysis, we identified signatures of differential expressed genes that show support from multiple studies. Overall, the changes in gene expression were more extensive after acute EtOH treatment during prenatal development than in other models. Considering the analysis of all the data together, we identified a robust core signature of 104 genes down-regulated after PAE, with no up-regulated genes. Functional analysis reveals over representation of genes involved in protein synthesis, mRNA splicing, and chromatin organization.

CONCLUSIONS

Our meta-analysis shows that existing studies, despite superficial dissimilarity in findings, share features that allow us to identify a common core signature set of transcriptome changes in PAE. This is an important step to identifying the biological processes that underlie the etiology of fetal alcohol spectrum disorders.

Autism spectrum disorders: Integration of the genome, transcriptome and the environment

Autism spectrum disorders denote a series of lifelong neurodevelopmental conditions characterized by an impaired social communication profile and often repetitive, stereotyped behavior. Recent years have seen the complex genetic architecture of the disease being progressively unraveled with advancements in gene finding technology and next generation sequencing methods. However, a complete elucidation of the molecular mechanisms behind autism is necessary for potential diagnostic and therapeutic applications. A multidisciplinary approach should be adopted where the focus is not only on the 'genetics' of autism but also on the combinational roles of epigenetics, transcriptomics, immune system disruption and environmental factors that could all influence the etiopathogenesis of the disease. ASD is a clinically heterogeneous disorder with great genetic complexity; only through an integrated multidimensional effort can modern autism research progress further.

Merging data from genetic and epigenetic approaches to better understand autistic spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is characterized by a wide range of cognitive and behavioral abnormalities. Genetic research has identified large numbers of genes that contribute to ASD phenotypes. There is compelling evidence that environmental factors contribute to ASD through influences that differentially impact the brain through epigenetic mechanisms. Both genetic mutations and epigenetic influences alter gene expression in different cell types of the brain. Mutations impact the expression of large numbers of genes and also have downstream consequences depending on specific pathways associated with the mutation. Environmental factors impact the expression of sets of genes by altering methylation/hydroxymethylation patterns, local histone modification patterns and chromatin remodeling. Herein, we discuss recent developments in the research of ASD with a focus on epigenetic pathways as a complement to current genetic screening.

High-resolution chromosome ideogram representation of currently recognized genes for autism spectrum disorders

Recently, autism-related research has focused on the identification of various genes and disturbed pathways causing the genetically heterogeneous group of autism spectrum disorders (ASD). The list of autism-related genes has significantly increased due to better awareness with advances in genetic technology and expanding searchable genomic databases. We compiled a master list of known and clinically relevant autism spectrum disorder genes identified with supporting evidence from peer-reviewed medical literature sources by searching key words related to autism and genetics and from authoritative autism-related public access websites, such as the Simons Foundation Autism Research Institute autism genomic database dedicated to gene discovery and characterization. Our list consists of 792 genes arranged in alphabetical order in tabular form with gene symbols placed on high-resolution human chromosome ideograms, thereby enabling clinical and laboratory geneticists and genetic counsellors to access convenient visual images of the location and distribution of ASD genes. Meaningful correlations of the observed phenotype in patients with suspected/confirmed ASD gene(s) at the chromosome region or breakpoint band site can be made to inform diagnosis and gene-based personalized care and provide genetic counselling for families.