Cross-tissue integration of genetic and epigenetic data offers insight into autism spectrum disorder

Targeting epigenetic dysregulation in autism spectrum disorders

Autism spectrum disorders (ASD) comprise a range of neurodevelopmental pathologies characterized by deficits in social interaction and repetitive behaviors, collectively affecting almost 1% of the worldwide population. Deciphering the etiology of ASD has proven challenging due to the intricate interplay of genetic and environmental factors and the variety of molecular pathways affected. Epigenomic alterations have emerged as key players in ASD etiology. Their research has led to the identification of biomarkers for diagnosis and pinpointed specific gene targets for therapeutic interventions. This review examines the role of epigenetic alterations, resulting from both genetic and environmental influences, as a central causative factor in ASD, delving into its contribution to pathogenesis and treatment strategies.

Statistical methods for assessing the effects of de novo variants on birth defects

With the development of next-generation sequencing technology, de novo variants (DNVs) with deleterious effects can be identified and investigated for their effects on birth defects such as congenital heart disease (CHD). However, statistical power is still limited for such studies because of the small sample size due to the high cost of recruiting and sequencing samples and the low occurrence of DNVs. DNV analysis is further complicated by genetic heterogeneity across diseased individuals. Therefore, it is critical to jointly analyze DNVs with other types of genomic/biological information to improve statistical power to identify genes associated with birth defects. In this review, we discuss the general workflow, recent developments in statistical methods, and future directions for DNV analysis.

Epigenetic changes in sperm are associated with paternal and child quantitative autistic traits in an autism-enriched cohort

There is a need to consider paternal contributions to autism spectrum disorder (ASD) more strongly. Autism etiology is complex, and heritability is not explained by genetics alone. Understanding paternal gametic epigenetic contributions to autism could help fill this knowledge gap. In the present study, we explored whether paternal autistic traits, and the sperm epigenome, were associated with autistic traits in children at 36 months enrolled in the Early Autism Risk Longitudinal Investigation (EARLI) cohort. EARLI is a pregnancy cohort that recruited and enrolled pregnant women in the first half of pregnancy who already had a child with ASD. After maternal enrollment, EARLI fathers were approached and asked to provide a semen specimen. Participants were included in the present study if they had genotyping, sperm methylation data, and Social Responsiveness Scale (SRS) score data available. Using the CHARM array, we performed genome-scale methylation analyses on DNA from semen samples contributed by EARLI fathers. The SRS-a 65-item questionnaire measuring social communication deficits on a quantitative scale-was used to evaluate autistic traits in EARLI fathers (n = 45) and children (n = 31). We identified 94 significant child SRS-associated differentially methylated regions (DMRs), and 14 significant paternal SRS-associated DMRs (fwer p < 0.05). Many child SRS-associated DMRs were annotated to genes implicated in ASD and neurodevelopment. Six DMRs overlapped across the two outcomes (fwer p < 0.1), and, 16 DMRs overlapped with previous child autistic trait findings at 12 months of age (fwer p < 0.05). Child SRS-associated DMRs contained CpG sites independently found to be differentially methylated in postmortem brains of individuals with and without autism. These findings suggest paternal germline methylation is associated with autistic traits in 3-year-old offspring. These prospective results for autism-associated traits, in a cohort with a family history of ASD, highlight the potential importance of sperm epigenetic mechanisms in autism.

Differential DNA Methylation from Autistic Children Enriches Evidence for Genes Associated with ASD and New Candidate Genes

The etiology of Autism Spectrum Disorders (ASD) is a result of the interaction between genes and the environment. The study of epigenetic factors that affect gene expression, such as DNA methylation, has become an important area of research in ASD. In recent years, there has been an increasing body of evidence pointing to epigenetic mechanisms that influence brain development, as in the case of ASD, when gene methylation dysregulation is present. Our analysis revealed 853 differentially methylated CpG in ASD patients, affecting 509 genes across the genome. Enrichment analysis showed five related diseases, including autistic disorder and mental disorders, which are particularly significant. In this work, we identified 64 genes that were previously reported in the SFARI gene database, classified according to their impact index. Additionally, we identified new genes that have not been previously reported as candidates with differences in the methylation patterns of Mexican children with ASD.

The intersection of genome, epigenome and social experience in autism spectrum disorder: Exploring modifiable pathways for intervention

The number of children diagnosed with autism spectrum disorder (ASD) has increased substantially over the past two decades. Current research suggests that both genetic and environmental risk factors are involved in the etiology of ASD. The goal of this paper is to examine how one specific environmental factor, early social experience, may be correlated with DNA methylation (DNAm) changes in genes associated with ASD. We present an innovative model which proposes that polygenic risk and changes in DNAm due to social experience may both contribute to the symptoms of ASD. Previous research on genetic and environmental factors implicated in the etiology of ASD will be reviewed, with an emphasis on the oxytocin receptor gene, which may be epigenetically altered by early social experience, and which plays a crucial role in social and cognitive development. Identifying an environmental risk factor for ASD (e.g., social experience) that could be modified via early intervention and which results in epigenetic (DNAm) changes, could transform our understanding of this condition, facilitate earlier identification of ASD, and guide early intervention efforts.

What are quantitative traits and how can they be used in autism research?

Quantitative traits are measurable characteristics distributed along a continuous scale thought to relate to underlying biology. There is growing interest in the use of quantitative traits in behavioral and psychiatric research, particularly in research on conditions diagnosed based on reports of behaviors, including autism. This brief commentary describes quantitative traits, including defining what they are, how we can measure them, and key considerations for their use in autism research. Examples of measures include behavioral report scales like the Social Responsiveness Scale and Broader Autism Phenotype Questionnaire, as well as biological measurements, like certain neuroimaging metrics; such measures can capture quantitative traits or constructs like the broader autism phenotype, social communication, and social cognition. Quantitative trait measures align with the Research Domain Criteria (RDoC) approach and can be used in autism research to help gain a better understanding of causal pathways and biological processes. They can also be used to aid identification of genetic and environmental factors involved in such pathways, and thereby lead to an understanding of influences on traits across the entire population. Finally, in some cases, they may be used to gauge treatment response, and assist screening and clinical characterization of phenotype. In addition, practical benefits of quantitative trait measures include improved statistical power relative to categorical classifications and (for some measures) efficiency. Ultimately, research across autism fields may benefit from incorporating quantitative trait measures as a complement to categorical diagnosis to advance understanding of autism and neurodevelopment.

In individuals with Williams syndrome, dysregulation of methylation in non-coding regions of neuronal and oligodendrocyte DNA is associated with pathology and cortical development

Williams syndrome (WS) is a neurodevelopmental disorder caused by a heterozygous micro-deletion in the WS critical region (WSCR) and is characterized by hyper-sociability and neurocognitive abnormalities. Nonetheless, whether and to what extent WSCR deletion leads to epigenetic modifications in the brain and induces pathological outcomes remains largely unknown. By examining DNA methylation in frontal cortex, we revealed genome-wide disruption in the methylome of individuals with WS, as compared to typically developed (TD) controls. Surprisingly, differentially methylated sites were predominantly annotated as introns and intergenic loci and were found to be highly enriched around binding sites for transcription factors that regulate neuronal development, plasticity and cognition. Moreover, by utilizing enhancer-promoter interactome data, we confirmed that most of these loci function as active enhancers in the human brain or as target genes of transcriptional networks associated with myelination, oligodendrocyte (OL) differentiation, cognition and social behavior. Cell type-specific methylation analysis revealed aberrant patterns in the methylation of active enhancers in neurons and OLs, and important neuron-glia interactions that might be impaired in individuals with WS. Finally, comparison of methylation profiles from blood samples of individuals with WS and healthy controls, along with other data collected in this study, identified putative targets of endophenotypes associated with WS, which can be used to define brain-risk loci for WS outside the WSCR locus, as well as for other associated pathologies. In conclusion, our study illuminates the brain methylome landscape of individuals with WS and sheds light on how these aberrations might be involved in social behavior and physiological abnormalities. By extension, these results may lead to better diagnostics and more refined therapeutic targets for WS.

Multi-omics data integration methods and their applications in psychiatric disorders

To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.

DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits

Studies of DNA methylation (DNAm) in solid human tissues are relatively scarce; tissue-specific characterization of DNAm is needed to understand its role in gene regulation and its relevance to complex traits. We generated array-based DNAm profiles for 987 human samples from the Genotype-Tissue Expression (GTEx) project, representing 9 tissue types and 424 subjects. We characterized methylome and transcriptome correlations (eQTMs), genetic regulation in cis (mQTLs and eQTLs) across tissues and e/mQTLs links to complex traits. We identified mQTLs for 286,152 CpG sites, many of which (>5%) show tissue specificity, and mQTL colocalizations with 2,254 distinct GWAS hits across 83 traits. For 91% of these loci, a candidate gene link was identified by integration of functional maps, including eQTMs, and/or eQTL colocalization, but only 33% of loci involved an eQTL and mQTL present in the same tissue type. With this DNAm-focused integrative analysis, we contribute to the understanding of molecular regulatory mechanisms in human tissues and their impact on complex traits.

The DNA Methylation in Neurological Diseases

DNA methylation is critical for the normal development and functioning of the human brain, such as the proliferation and differentiation of neural stem cells, synaptic plasticity, neuronal reparation, learning, and memory. Despite the physical stability of DNA and methylated DNA compared to other epigenetic modifications, some DNA methylation-based biomarkers have translated into clinical practice. Increasing reports indicate a strong association between DNA methylation profiles and various clinical outcomes in neurological diseases, making DNA methylation profiles valuable as novel clinical markers. In this review, we aim to discuss the latest evidence concerning DNA methylation alterations in the development of neurodegenerative, neurodevelopmental, and neuropsychiatric diseases. We also highlighted the relationship of DNA methylation alterations with the disease progression and outcome in many neurological diseases such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, frontotemporal dementia, and autism.

Blood RNA Sequencing Indicates Upregulated BATF2 and LY6E and Downregulated ISG15 and MT2A Expression in Children with Autism Spectrum Disorder

Mutations in over 100 genes are implicated in autism spectrum disorder (ASD). DNA SNPs, CNVs, and epigenomic modifications also contribute to ASD. Transcriptomics analysis of blood samples may offer clues for pathways dysregulated in ASD. To expand and validate published findings of RNA-sequencing (RNA-seq) studies, we performed RNA-seq of whole blood samples from an Israeli discovery cohort of eight children with ASD compared with nine age- and sex-matched neurotypical children. This revealed 10 genes with differential expression. Using quantitative real-time PCR, we compared RNAs from whole blood samples of 73 Israeli and American children with ASD and 26 matched neurotypical children for the 10 dysregulated genes detected by RNA-seq. This revealed higher expression levels of the pro-inflammatory transcripts BATF2 and LY6E and lower expression levels of the anti-inflammatory transcripts ISG15 and MT2A in the ASD compared to neurotypical children. BATF2 was recently reported as upregulated in blood samples of Japanese adults with ASD. Our findings support an involvement of these genes in ASD phenotypes, independent of age and ethnicity. Upregulation of BATF2 and downregulation of ISG15 and MT2A were reported to reduce cancer risk. Implications of the dysregulated genes for pro-inflammatory phenotypes, immunity, and cancer risk in ASD are discussed.

From Genes to Therapy in Autism Spectrum Disorder

In recent years, findings from genetic and other biological studies are starting to reveal the role of various molecular mechanisms that contribute to the etiology of ASD [...]

Genome-wide DNA methylation profiles of autism spectrum disorder

OBJECTIVES

We aimed to identify differentially methylated genes and related signaling pathways in autism spectrum disorder (ASD).

METHODS

First, the DNA methylation profile in the brain samples (GSE131706 and GSE80017) and peripheral blood samples (GSE109905) was downloaded from the Gene Expression Omnibus database (GEO) dataset, followed by identification of differentially methylated genes and functional analysis. Second, the GSE109905 data set was used to further validate the methylation state and test the ability to diagnose disease of identified differentially methylated genes. Third, expression measurement of selected differentially methylated genes was performed in whole blood from an independent sample. Finally, protein-protein interaction (PPI) network of core differentially methylated genes was constructed.

RESULTS

Totally, 74 differentially methylated genes were identified in ASD, including 38 hypermethylated genes and 36 hypomethylated genes. 15 differentially methylated genes were further identified after validation in the GSE109905 data set. Among these, major histocompatibility complex (HLA)-DQA1 was involved in the molecular function of myosin heavy chain class II receptor activity; HLA-DRB5 was involved in the signaling pathways of cell adhesion molecules, Epstein-Barr virus infection and antigen processing and presentation. In the PPI analysis, the interaction pairs of HLA-DQA1 and HLA-DRB5, FMN2 and ACTR3, and CALCOCO2 and BAZ2B were identified. Interestingly, FMN2, BAZ2B, HLA-DRB5, CALCOCO2 and DUSP22 had a potential diagnostic value for patients with ASD. The expression result of four differentially methylated genes (HLA-DRB5, NTM, IL16 and COL5A3) in the independent sample was consistent with the integrated analysis.

CONCLUSIONS

Identified differentially methylated genes and enriched signaling pathway could be associated with ASD.

Enhanced Expression of Human Endogenous Retroviruses, TRIM28 and SETDB1 in Autism Spectrum Disorder

Human endogenous retroviruses (HERVs) are relics of ancestral infections and represent 8% of the human genome. They are no longer infectious, but their activation has been associated with several disorders, including neuropsychiatric conditions. Enhanced expression of HERV-K and HERV-H envelope genes has been found in the blood of autism spectrum disorder (ASD) patients, but no information is available on syncytin 1 (SYN1), SYN2, and multiple sclerosis-associated retrovirus (MSRV), which are thought to be implicated in brain development and immune responses. HERV activation is regulated by TRIM28 and SETDB1, which are part of the epigenetic mechanisms that organize the chromatin architecture in response to external stimuli and are involved in neural cell differentiation and brain inflammation. We assessed, through a PCR realtime Taqman amplification assay, the transcription levels of pol genes of HERV-H, -K, and -W families, of env genes of SYN1, SYN2, and MSRV, as well as of TRIM28 and SETDB1 in the blood of 33 ASD children (28 males, median 3.8 years, 25–75% interquartile range 3.0–6.0 y) and healthy controls (HC). Significantly higher expressions of TRIM28 and SETDB1, as well as of all the HERV genes tested, except for HERV-W-pol, were found in ASD, as compared with HC. Positive correlations were observed between the mRNA levels of TRIM28 or SETDB1 and every HERV gene in ASD patients, but not in HC. Overexpression of TRIM28/SETDB1 and several HERVs in children with ASD and the positive correlations between their transcriptional levels suggest that these may be main players in pathogenetic mechanisms leading to ASD.

Mapping the Cord Blood Transcriptome of Pregnancies Affected by Early Maternal Anemia to Identify Signatures of Fetal Programming

CONTEXT

Anemia during early pregnancy (EP) is common in developing countries and is associated with adverse health consequences for both mothers and children. Offspring of women with EP anemia often have low birth weight, which increases risk for cardiometabolic diseases, including type 2 diabetes (T2D), later in life.

OBJECTIVE

We aimed to elucidate mechanisms underlying developmental programming of adult cardiometabolic disease, including epigenetic and transcriptional alterations potentially detectable in umbilical cord blood (UCB) at time of birth.

METHODS

We leveraged global transcriptome- and accompanying epigenome-wide changes in 48 UCB from newborns of EP anemic Tanzanian mothers and 50 controls to identify differentially expressed genes (DEGs) in UCB exposed to maternal EP anemia. DEGs were assessed for association with neonatal anthropometry and cord insulin levels. These genes were further studied in expression data from human fetal pancreas and adult islets to understand their role in beta-cell development and/or function.

RESULTS

The expression of 137 genes was altered in UCB of newborns exposed to maternal EP anemia. These putative signatures of fetal programming, which included the birth weight locus LCORL, were potentially mediated by epigenetic changes in 27 genes and associated with neonatal anthropometry. Among the DEGs were P2RX7, PIK3C2B, and NUMBL, which potentially influence beta-cell development. Insulin levels were lower in EP anemia-exposed UCB, supporting the notion of developmental programming of pancreatic beta-cell dysfunction and subsequently increased risk of T2D in offspring of mothers with EP anemia.

CONCLUSIONS

Our data provide proof-of-concept on distinct transcriptional and epigenetic changes detectable in UCB from newborns exposed to maternal EP anemia.

Identification of genetic variants influencing methylation in brain with pleiotropic effects on psychiatric disorders

Psychiatric disorders affect 29% of the global population at least once in the lifespan, and genetic studies have proved a shared genetic basis among them, although the underlying molecular mechanisms remain largely unknown. DNA methylation plays an important role in complex disorders and, remarkably, enrichment of common genetic variants influencing allele-specific methylation (ASM) has been reported among variants associated with specific psychiatric disorders. In the present study we assessed the contribution of ASM to a set of eight psychiatric disorders by combining genetic, epigenetic and expression data. We interrogated a list of 3896 ASM tagSNPs in the brain in the summary statistics of a cross-disorder GWAS meta-analysis of eight psychiatric disorders from the Psychiatric Genomics Consortium, including more than 162,000 cases and 276,000 controls. We identified 80 SNPs with pleiotropic effects on psychiatric disorders that show an opposite directional effect on methylation and gene expression. These SNPs converge on eight candidate genes: ZSCAN29, ZSCAN31, BTN3A2, DDAH2, HAPLN4, ARTN, FAM109B and NAGA. ZSCAN29 shows the broadest pleiotropic effects, showing associations with five out of eight psychiatric disorders considered, followed by ZSCAN31 and BTN3A2, associated with three disorders. All these genes overlap with CNVs related to cognitive phenotypes and psychiatric traits, they are expressed in the brain, and seven of them have previously been associated with specific psychiatric disorders, supporting our results. To sum up, our integrative functional genomics analysis identified eight psychiatric disease risk genes that impact a broad list of disorders and highlight an etiologic role of SNPs that influence DNA methylation and gene expression in the brain.

Gene variations in autism spectrum disorder are associated with alteration of gut microbiota, metabolites and cytokines

The genetic variations and dysbiosis of gut microbiota are associated with ASD. However, the role of the microbiota in the etiology of ASD in terms of host genetic susceptibility remains unclear. This study aims to systematically explore the interplay between host genetic variation and gut microbiota in ASD children. Whole-exon sequencing was applied to 26 ASD children and 26 matched controls to identify the single nucleotide variations (SNVs) in ASD. Our previous study revealed alteration in gut microbiota and disorder of metabolism activity in ASD for this cohort. Systematic bioinformatic analyses were further performed to identify associations between SNVs and gut microbiota, as well as their metabolites. The ASD SNVs were significantly enriched in genes associated with innate immune response, protein glycosylation process, and retrograde axonal transport. These SNVs were also correlated with the microbiome composition and a broad aspect of microbial functions, especially metabolism. Additionally, the abundance of metabolites involved in the metabolic network of neurotransmitters was inferred to be causally related to specific SNVs and microbes. Furthermore, our data suggested that the interaction of host genetics and gut microbes may play a crucial role in the immune and metabolism homeostasis of ASD. This study may provide valuable clues to investigate the interaction of host genetic variations and gut microbiota in the pathogenesis of ASD.

Accurate Measurement of DNA Methylation: Challenges and Bias Correction

DNA methylation is a key epigenetic modification involved in gene regulation whose contribution to disease susceptibility is still not fully understood. As the cost of genome sequencing technologies continues to drop, it will soon become commonplace to perform genome-wide quantification of DNA methylation at a single base-pair resolution. However, the demand for its accurate quantification might vary across studies. When the scope of the analysis is to detect regions of the genome with different methylation patterns between two or more conditions, e.g., case vs control; treatments vs placebo, accuracy is not crucial. This is the case in epigenome-wide association studies used as genome-wide screening of methylation changes to detect new candidate genes and regions associated with a specific disease or condition. If the aim of the analysis is to use DNA methylation measurements as a biomarker for diseases diagnosis and treatment (Laird, Nat Rev Cancer 3:253-266, 2003; Bock, Epigenomics 1:99-110, 2009), it is instead recommended to produce accurate methylation measurements. Furthermore, if the objective is the detection of DNA methylation in subclonal tumor cell populations or in circulating tumor DNA or in any case of mosaicism, the importance of accuracy becomes critical. The aim of this chapter is to describe the factors that could affect the precise measurement of methylation levels and a recent Bayesian statistical method called MethylCal and its extension that have been proposed to minimize this problem.

Autism-Associated DNA Methylation at Birth From Multiple Tissues Is Enriched for Autism Genes in the Early Autism Risk Longitudinal Investigation

Background: Pregnancy measures of DNA methylation, an epigenetic mark, may be associated with autism spectrum disorder (ASD) development in children. Few ASD studies have considered prospective designs with DNA methylation measured in multiple tissues and tested overlap with ASD genetic risk loci. Objectives: To estimate associations between DNA methylation in maternal blood, cord blood, and placenta and later diagnosis of ASD, and to evaluate enrichment of ASD-associated DNA methylation for known ASD-associated genes. Methods: In the Early Autism Risk Longitudinal Investigation (EARLI), an ASD-enriched risk birth cohort, genome-scale maternal blood (early n = 140 and late n = 75 pregnancy), infant cord blood (n = 133), and placenta (maternal n = 106 and fetal n = 107 compartments) DNA methylation was assessed on the Illumina 450k HumanMethylation array and compared to ASD diagnosis at 36 months of age. Differences in site-specific and global methylation were tested with ASD, as well as enrichment of single site associations for ASD risk genes (n = 881) from the Simons Foundation Autism Research Initiative (SFARI) database. Results: No individual DNA methylation site was associated with ASD at genome-wide significance, however, individual DNA methylation sites nominally associated with ASD (P < 0.05) in each tissue were highly enriched for SFARI genes (cord blood P = 7.9 × 10^-29, maternal blood early pregnancy P = 6.1 × 10^-27, maternal blood late pregnancy P = 2.8 × 10^-16, maternal placenta P = 5.6 × 10^-15, fetal placenta P = 1.3 × 10^-20). DNA methylation sites nominally associated with ASD across all five tissues overlapped at 144 (29.5%) SFARI genes. Conclusion: DNA methylation sites nominally associated with later ASD diagnosis in multiple tissues were enriched for ASD risk genes. Our multi-tissue study demonstrates the utility of examining DNA methylation prior to ASD diagnosis.

Environmental Influences Measured by Epigenetic Clock and Vulnerability Components at Birth Impact Clinical ASD Heterogeneity

Although Autism Spectrum Disorders (ASD) is recognized as being heavily influenced by genetic factors, the role of epigenetic and environmental factors is still being established. This study aimed to identify ASD vulnerability components based on familial history and intrauterine environmental stress exposure, explore possible vulnerability subgroups, access DNA methylation age acceleration (AA) as a proxy of stress exposure during life, and evaluate the association of ASD vulnerability components and AA to phenotypic severity measures. Principal Component Analysis (PCA) was used to search the vulnerability components from 67 mothers of autistic children. We found that PC1 had a higher correlation with psychosocial stress (maternal stress, maternal education, and social class), and PC2 had a higher correlation with biological factors (psychiatric family history and gestational complications). Comparing the methylome between above and below PC1 average subgroups we found 11,879 statistically significant differentially methylated probes (DMPs, p < 0.05). DMPs CpG sites were enriched in variably methylated regions (VMRs), most showing environmental and genetic influences. Hypermethylated probes presented higher rates in different regulatory regions associated with functional SNPs, indicating that the subgroups may have different affected regulatory regions and their liability to disease explained by common variations. Vulnerability components score moderated by epigenetic clock AA was associated with Vineland Total score (p = 0.0036, adjR² = 0.31), suggesting risk factors with stress burden can influence ASD phenotype.

Oncolytic Viruses in Combination Therapeutic Approaches with Epigenetic Modulators: Past, Present, and Future Perspectives

Simple Summary

Cancer rates have been accelerating significantly in recent years. Despite notable advances having been made in cancer therapy, and numerous studies being currently conducted in clinical trials, research is always looking for new treatment. Novel and promising anticancer therapies comprise combinations of oncolytic viruses and epigenetic modulators, including chromatin modifiers, such as DNA methyltransferase and histone deacetylases, and microRNA. Combinatorial treatments have several advantages: they enhance viral entry, replication, and spread between proximal cells and, moreover, they strengthen the immune response. In this review we summarize the main combination of therapeutic approaches, giving an insight into past, present, and future perspectives.

Abstract

According to the World Cancer Report, cancer rates have been increased by 50% with 15 million new cases in the year 2020. Hepatocellular carcinoma (HCC) is the only one of the most common tumors to cause a huge increase in mortality with a survival rate between 40% and 70% at 5 years, due to the high relapse and limitations associated with current therapies. Despite great progress in medicine, oncological research is always looking for new therapies: different technologies have been evaluated in clinical trials and others have been already used in clinics. Among them, oncolytic virotherapy represents a therapeutic option with a widespread possibility of approaches and applications. Oncolytic viruses are naturally occurring, or are engineered, viruses characterized by the unique features of preferentially infecting, replicating, and lysing malignant tumor cells, as well as activating the immune response. The combination of oncolytic virotherapy and chemical drugs are arousing great interest in the tumor treatment. In this scenario, novel and promising anticancer therapies comprise combinations of oncolytic viruses and epigenetic modulators or inhibitors of the signalling pathways. Combination treatments are required to improve the immune response and allow viral entry, replication, and diffusion between proximal cells. In this review, we summarize all combination therapies associated with virotherapy, including co-administered inhibitors of chromatin modifiers (combination strategies) and inserted target sites for miRNAs (recombination or arming strategies).

Genomic selection signatures in autism spectrum disorder identifies cognitive genomic tradeoff and its relevance in paradoxical phenotypes of deficits versus potentialities

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder characterized by paradoxical phenotypes of deficits as well as gain in brain function. To address this a genomic tradeoff hypothesis was tested and followed up with the biological interaction and evolutionary significance of positively selected ASD risk genes. SFARI database was used to retrieve the ASD risk genes while for population datasets 1000 genome data was used. Common risk SNPs were subjected to machine learning as well as independent tests for selection, followed by Bayesian analysis to identify the cumulative effect of selection on risk SNPs. Functional implication of these positively selected risk SNPs was assessed and subjected to ontology analysis, pertaining to their interaction and enrichment of biological and cellular functions. This was followed by comparative analysis with the ancient genomes to identify their evolutionary patterns. Our results identified significant positive selection signals in 18 ASD risk SNPs. Functional and ontology analysis indicate the role of biological and cellular processes associated with various brain functions. The core of the biological interaction network constitutes genes for cognition and learning while genes in the periphery of the network had direct or indirect impact on brain function. Ancient genome analysis identified de novo and conserved evolutionary selection clusters. The de-novo evolutionary cluster represented genes involved in cognitive function. Relative enrichment of the ASD risk SNPs from the respective evolutionary cluster or biological interaction networks may help in addressing the phenotypic diversity in ASD. This cognitive genomic tradeoff signatures impacting the biological networks can explain the paradoxical phenotypes in ASD.

Genetic impacts on DNA methylation: research findings and future perspectives

Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.

Methylation quantitative trait locus analysis of chronic postsurgical pain uncovers epigenetic mediators of genetic risk

Background: Overlap of pathways enriched by single nucleotide polymorphisms and DNA-methylation underlying chronic postsurgical pain (CPSP), prompted pilot study of CPSP-associated methylation quantitative trait loci (meQTL). Materials & methods: Children undergoing spine-fusion were recruited prospectively. Logistic-regression for genome- and epigenome-wide CPSP association and DNA-methylation-single nucleotide polymorphism association/mediation analyses to identify meQTLs were followed by functional genomics analyses. Results: CPSP (n = 20/58) and non-CPSP groups differed in pain-measures. Of 2753 meQTLs, DNA-methylation at 127 cytosine-guanine dinucleotides mediated association of 470 meQTLs with CPSP (p < 0.05). At PARK16 locus, CPSP risk meQTLs were associated with decreased DNA-methylation at RAB7L1 and increased DNA-methylation at PM20D1. Corresponding RAB7L1/PM20D1 blood eQTLs (GTEx) and cytosine-guanine dinucleotide-loci enrichment for histone marks, transcription factor binding sites and ATAC-seq peaks suggest altered transcription factor-binding. Conclusion: CPSP-associated meQTLs indicate epigenetic mechanisms mediate genetic risk. Clinical trial registration: NCT01839461, NCT01731873 (ClinicalTrials.gov).

Exploring allele specific methylation in drug dependence susceptibility

Drug dependence is a neuropsychiatric condition that involves genetic, epigenetic and environmental factors. Allele-specific methylation (ASM) is a common and stable epigenetic mechanism that involves genetic variants correlating with differential levels of methylation at CpG sites. We selected 182 single-nucleotide polymorphisms (SNPs) described to influence cis ASM in human brain regions to evaluate their possible contribution to drug dependence susceptibility. We performed a case-control association study in a discovery sample of 578 drug-dependent patients (including 428 cocaine-dependent subjects) and 656 controls from Spain, and then, we followed-up the significant associations in an independent sample of 1119 cases (including 589 cocaine-dependent subjects) and 1092 controls. In the discovery sample, we identified five nominal associations, one of them replicated in the follow-up sample (rs6020251). The pooled analysis revealed an association between drug dependence and rs6020251 but also rs11585570, both overcoming the Bonferroni correction for multiple testing. We performed the same analysis considering only cocaine-dependent patients and obtained similar results. The rs6020251 variant correlates with differential methylation levels of cg17974185 and lies in the first intron of the CTNNBL1 gene, in a genomic region with multiple histone marks related to enhancer and promoter regions in brain. Rs11585570 is an eQTL in brain and blood for the SCP2 and ECHDC2 genes and correlates with differential methylation of cg27535305 and cg13461509, located in the promoter regions of both genes. To conclude, using an approach that combines genetic and epigenetic data, we highlighted the CTNNBL1, SCP2 and ECHDC2 genes as potential contributors to drug dependence susceptibility.

Autism spectrum disorder and severe social impairment associated with elevated plasma interleukin-8

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with an unclear etiology and pathophysiology. Previous studies have indicated that the dysregulation of cytokines may be involved in the pathogenesis of ASD and that the levels of cytokines may serve as potential biomarkers of this disorder.

METHODS

The current study employed a family triad-based case-control design to study the levels of plasma cytokines in families with ASD (n = 45 triads) and controls (n = 38 triads) with a Human Cytokine Twenty-Five-Plex Kit. The Social Responsiveness Scale (SRS) was used to measure social impairment of ASD children.

RESULTS

After controlling for the levels of parental cytokines, we identified that interferon-α (IFN-α), interleukin-7 (IL-7), IL-8, IFN-γ-inducible protein-10, and macrophage inflammatory protein-1β were associated with ASD, and IL-8 was the only cytokine also associated with the levels of both parental cytokines in the offspring-parents regression analysis and three subdomains of SRS (social awareness, cognition, and motivations) in the children with ASD. The receiver operating characteristic curve showed that the log-transformed IL-8 level discriminated children with autism from controls with an area under the curve of 0.858 (95% confidence interval: 0.777-0.939).

CONCLUSIONS

Our study suggests that IL-8 is a potential biomarker for ASD and may be involved in the pathogenesis of ASD.

IMPACT

The study suggests that IL-8 is a promising biomarker for ASD and may be involved in the pathogenesis of ASD. Only a very few studies have reported the parental cytokine levels. The significant strength of this article is that we applied the family triad-based approach to explore cytokine levels in families with autism and controls. There are no objective biomarkers, making the accurate diagnosis, prognostic prediction and effective treatment difficult, and our study provides promising results.

Analysis of Global and Local DNA Methylation Patterns in Blood Samples of Patients With Autism Spectrum Disorder

The goal of this investigation was to determine whether there are alterations in DNA methylation patterns in children with autism spectrum disorder (ASD). Material and Methods: Controlled prospective observational case-control study. Within the ASD group, children were sub-classified based on the presence (AMR subgroup) or absence (ANMR subgroup) of neurodevelopmental regression during the first 2 years of life. We analyzed the global levels of DNA methylation, reflected in LINE-1, and the local DNA methylation pattern in two candidate genes, Neural Cell Adhesion Molecule (NCAM1) and Nerve Growth Factor (NGF) that, according to our previous studies, might be associated to an increased risk for ASD. For this purpose, we utilized blood samples from pediatric patients with ASD (n = 53) and their corresponding controls (n = 45). Results: We observed a slight decrease in methylation levels of LINE-1 in the ASD group, compared to the control group. One of the CpG in LINE-1 (GenBank accession no.X58075, nucleotide position 329) was the main responsible for such reduction, highly significant in the ASD subgroup of children with AMR (p < 0.05). Furthermore, we detected higher NCAM1 methylation levels in ASD children, compared to healthy children (p < 0.001). The data, moreover, showed higher NGF methylation levels in the AMR subgroup, compared to the control group and the ANMR subgroup. These results are consistent with our prior study, in which lower plasma levels of NCAM1 and higher levels of NGF were found in the ANMR subgroup, compared to the subgroup that comprised neurotypically developing children. Conclusions: We have provided new clues about the epigenetic changes that occur in ASD, and suggest two potential epigenetic biomarkers that would facilitate the diagnosis of the disorder. We similarly present with evidence of a clear differentiation in DNA methylation between the ASD subgroups, with or without mental regression.

Network modules linking expression and methylation in prefrontal cortex of schizophrenia

Tremendous work has demonstrated the critical roles of genetics, epigenetics as well as their interplay in brain transcriptional regulations in the pathology of schizophrenia (SZ). There is great success currently in the dissection of the genetic components underlying risk-conferring transcriptomic networks. However, the study of regulating effect of epigenetics in the etiopathogenesis of SZ still faces many challenges. In this work, we investigated DNA methylation and gene expression from the dorsolateral prefrontal cortex (DLPFC) region of schizophrenia patients and healthy controls using weighted correlation network approach. We identified and replicated two expression and two methylation modules significantly associated with SZ. Among them, one pair of expression and methylation modules were significantly overlapped in the module genes which were significantly enriched in astrocyte-associated functional pathways, and specifically expressed in astrocytes. Another two linked expression-methylation module pairs were involved ageing process with module genes mostly related to oligodendrocyte development and myelination, and specifically expressed in oligodendrocytes. Further examination of underlying quantitative trait loci (QTLs) showed significant enrichment in genetic risk of most psychiatric disorders for expression QTLs but not for methylation QTLs. These results support the coherence between methylation and gene expression at the network level, and suggest a combinatorial effect of genetics and epigenetics in regulating gene expression networks specific to glia cells in relation to SZ and ageing process.

DNA Methylation at Birth Predicts Intellectual Functioning and Autism Features in Children with Fragile X Syndrome

Fragile X syndrome (FXS) is a leading single-gene cause of intellectual disability (ID) with autism features. This study analysed diagnostic and prognostic utility of the Fragile X-Related Epigenetic Element 2 DNA methylation (FREE2m) assessed by Methylation Specific-Quantitative Melt Analysis and the EpiTYPER system, in retrospectively retrieved newborn blood spots (NBS) and newly created dried blood spots (DBS) from 65 children with FXS (~2–17 years). A further 168 NBS from infants from the general population were used to establish control reference ranges, in both sexes. FREE2m analysis showed sensitivity and specificity approaching 100%. In FXS males, NBS FREE2m strongly correlated with intellectual functioning and autism features, however associations were not as strong for FXS females. Fragile X mental retardation 1 gene (FMR1) mRNA levels in blood were correlated with FREE2m in both NBS and DBS, for both sexes. In females, DNAm was significantly increased at birth with a decrease in childhood. The findings support the use of FREE2m analysis in newborns for screening, diagnostic and prognostic testing in FXS.

Cord blood DNA methylome in newborns later diagnosed with autism spectrum disorder reflects early dysregulation of neurodevelopmental and X-linked genes

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with complex heritability and higher prevalence in males. The neonatal epigenome has the potential to reflect past interactions between genetic and environmental factors during early development and influence future health outcomes.

METHODS

We performed whole-genome bisulfite sequencing of 152 umbilical cord blood samples from the MARBLES and EARLI high-familial risk prospective cohorts to identify an epigenomic signature of ASD at birth. Samples were split into discovery and replication sets and stratified by sex, and their DNA methylation profiles were tested for differentially methylated regions (DMRs) between ASD and typically developing control cord blood samples. DMRs were mapped to genes and assessed for enrichment in gene function, tissue expression, chromosome location, and overlap with prior ASD studies. DMR coordinates were tested for enrichment in chromatin states and transcription factor binding motifs. Results were compared between discovery and replication sets and between males and females.

RESULTS

We identified DMRs stratified by sex that discriminated ASD from control cord blood samples in discovery and replication sets. At a region level, 7 DMRs in males and 31 DMRs in females replicated across two independent groups of subjects, while 537 DMR genes in males and 1762 DMR genes in females replicated by gene association. These DMR genes were significantly enriched for brain and embryonic expression, X chromosome location, and identification in prior epigenetic studies of ASD in post-mortem brain. In males and females, autosomal ASD DMRs were significantly enriched for promoter and bivalent chromatin states across most cell types, while sex differences were observed for X-linked ASD DMRs. Lastly, these DMRs identified in cord blood were significantly enriched for binding sites of methyl-sensitive transcription factors relevant to fetal brain development.

CONCLUSIONS

At birth, prior to the diagnosis of ASD, a distinct DNA methylation signature was detected in cord blood over regulatory regions and genes relevant to early fetal neurodevelopment. Differential cord methylation in ASD supports the developmental and sex-biased etiology of ASD and provides novel insights for early diagnosis and therapy.

The PedBE clock accurately estimates DNA methylation age in pediatric buccal cells

The development of biological markers of aging has primarily focused on adult samples. Epigenetic clocks are a promising tool for measuring biological age that show impressive accuracy across most tissues and age ranges. In adults, deviations from the DNA methylation (DNAm) age prediction are correlated with several age-related phenotypes, such as mortality and frailty. In children, however, fewer such associations have been made, possibly because DNAm changes are more dynamic in pediatric populations as compared to adults. To address this gap, we aimed to develop a highly accurate, noninvasive, biological measure of age specific to pediatric samples using buccal epithelial cell DNAm. We gathered 1,721 genome-wide DNAm profiles from 11 different cohorts of typically developing individuals aged 0 to 20 y old. Elastic net penalized regression was used to select 94 CpG sites from a training dataset (n = 1,032), with performance assessed in a separate test dataset (n = 689). DNAm at these 94 CpG sites was highly predictive of age in the test cohort (median absolute error = 0.35 y). The Pediatric-Buccal-Epigenetic (PedBE) clock was characterized in additional cohorts, showcasing the accuracy in longitudinal data, the performance in nonbuccal tissues and adult age ranges, and the association with obstetric outcomes. The PedBE tool for measuring biological age in children might help in understanding the environmental and contextual factors that shape the DNA methylome during child development, and how it, in turn, might relate to child health and disease.

DNA methylation analysis for screening and diagnostic testing in neurodevelopmental disorders

DNA methylation (mDNA) plays an important role in the pathogenesis of neurodevelopmental disorders (NDDs), however its use in diagnostic testing has been largely restricted to a handful of methods for locus-specific analysis in monogenic syndromes. Recent studies employing genome-wide methylation analysis (GWMA) have explored utility of a single array-based test to detect methylation changes in probands negative by exome sequencing, and to diagnose different monogenic NDDs with defined epigenetic signatures. While this may be a more efficient approach, several significant barriers remain. These include non-uniform and low coverage of regulatory regions that may have CG-rich sequences, and lower analytical sensitivity as compared with locus-specific analyses that may result in methylation mosaicism not being detected. A major challenge associated with the above technologies, regardless of whether the analysis is locus specific or genome wide, is the technical bias introduced by indirect analysis of methylation. This review summarizes evidence from the most recent studies in this field and discusses future directions, including direct analysis of methylation using long-read technologies and detection of 5-methylcytosine (5-mC or total mDNA) and 5-hydroxymethylacytosine (5-hmC) as biomarkers of NDDs.

Beyond the looking glass: recent advances in understanding the impact of environmental exposures on neuropsychiatric disease

The etiologic pathways leading to neuropsychiatric diseases remain poorly defined. As genomic technologies have advanced over the past several decades, considerable progress has been made linking neuropsychiatric disorders to genetic underpinnings. Interest and consideration of nongenetic risk factors (e.g., lead exposure and schizophrenia) have, in contrast, lagged behind heritable frameworks of explanation. Thus, the association of neuropsychiatric illness to environmental chemical exposure, and their potential interactions with genetic susceptibility, are largely unexplored. In this review, we describe emerging approaches for considering the impact of chemical risk factors acting alone and in concert with genetic risk, and point to the potential role of epigenetics in mediating exposure effects on transcription of genes implicated in mental disorders. We highlight recent examples of research in nongenetic risk factors in psychiatric disorders that point to potential shared biological mechanisms-synaptic dysfunction, immune alterations, and gut-brain interactions. We outline new tools and resources that can be harnessed for the study of environmental factors in psychiatric disorders. These tools, combined with emerging experimental evidence, suggest that there is a need to broadly incorporate environmental exposures in psychiatric research, with the ultimate goal of identifying modifiable risk factors and informing new treatment strategies for neuropsychiatric disease.

Pancan-meQTL: a database to systematically evaluate the effects of genetic variants on methylation in human cancer

DNA methylation is an important epigenetic mechanism for regulating gene expression. Aberrant DNA methylation has been observed in various human diseases, including cancer. Single-nucleotide polymorphisms can contribute to tumor initiation, progression and prognosis by influencing DNA methylation, and DNA methylation quantitative trait loci (meQTL) have been identified in physiological and pathological contexts. However, no database has been developed to systematically analyze meQTLs across multiple cancer types. Here, we present Pancan-meQTL, a database to comprehensively provide meQTLs across 23 cancer types from The Cancer Genome Atlas by integrating genome-wide genotype and DNA methylation data. In total, we identified 8 028 964 cis-meQTLs and 965 050 trans-meQTLs. Among these, 23 432 meQTLs are associated with patient overall survival times. Furthermore, we identified 2 214 458 meQTLs that overlap with known loci identified through genome-wide association studies. Pancan-meQTL provides a user-friendly web interface (http://bioinfo.life.hust.edu.cn/Pancan-meQTL/) that is convenient for browsing, searching and downloading data of interest. This database is a valuable resource for investigating the roles of genetics and epigenetics in cancer.

Network Structure Analysis Identifying Key Genes of Autism and Its Mechanism

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

Maternal Gestational Diabetes Mellitus and Newborn DNA Methylation: Findings From the Pregnancy and Childhood Epigenetics Consortium

OBJECTIVE

Maternal gestational diabetes mellitus (GDM) has been associated with adverse outcomes in the offspring. Growing evidence suggests that the epigenome may play a role, but most previous studies have been small and adjusted for few covariates. The current study meta-analyzed the association between maternal GDM and cord blood DNA methylation in the Pregnancy and Childhood Epigenetics (PACE) consortium.

RESEARCH DESIGN AND METHODS

Seven pregnancy cohorts (3,677 mother-newborn pairs [317 with GDM]) contributed results from epigenome-wide association studies, using DNA methylation data acquired by the Infinium HumanMethylation450 BeadChip array. Associations between GDM and DNA methylation were examined using robust linear regression, with adjustment for potential confounders. Fixed-effects meta-analyses were performed using METAL. Differentially methylated regions (DMRs) were identified by taking the intersection of results obtained using two regional approaches: comb-p and DMRcate.

RESULTS

Two DMRs were identified by both comb-p and DMRcate. Both regions were hypomethylated in newborns exposed to GDM in utero compared with control subjects. One DMR (chr 1: 248100345-248100614) was located in the OR2L13 promoter, and the other (chr 10: 135341870-135342620) was located in the gene body of CYP2E1. Individual CpG analyses did not reveal any differentially methylated loci based on a false discovery rate-adjusted P value threshold of 0.05.

CONCLUSIONS

Maternal GDM was associated with lower cord blood methylation levels within two regions, including the promoter of OR2L13, a gene associated with autism spectrum disorder, and the gene body of CYP2E1, which is upregulated in type 1 and type 2 diabetes. Future studies are needed to understand whether these associations are causal and possible health consequences.

DNA Methylation and Susceptibility to Autism Spectrum Disorder

The prevalence of autism spectrum disorder (ASD) has been increasing steadily over the last 20 years; however, the molecular basis for the majority of ASD cases remains unknown. Recent advances in next-generation sequencing and detection of DNA modifications have made methylation-dependent regulation of transcription an attractive hypothesis for being a causative factor in ASD etiology. Evidence for abnormal DNA methylation in ASD can be seen on multiple levels, from genetic mutations in epigenetic machinery to loci-specific and genome-wide changes in DNA methylation. Epimutations in DNA methylation can be acquired throughout life, as global DNA methylation reprogramming is dynamic during embryonic development and the early postnatal period that corresponds to the peak time of synaptogenesis. However, technical advances and causative evidence still need to be established before abnormal DNA methylation and ASD can be confidently associated.

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

Genetic Causes and Modifiers of Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is one of the most prevalent neurodevelopmental disorders, affecting an estimated 1 in 59 children. ASD is highly genetically heterogeneous and may be caused by both inheritable and de novo gene variations. In the past decade, hundreds of genes have been identified that contribute to the serious deficits in communication, social cognition, and behavior that patients often experience. However, these only account for 10-20% of ASD cases, and patients with similar pathogenic variants may be diagnosed on very different levels of the spectrum. In this review, we will describe the genetic landscape of ASD and discuss how genetic modifiers such as copy number variation, single nucleotide polymorphisms, and epigenetic alterations likely play a key role in modulating the phenotypic spectrum of ASD patients. We also consider how genetic modifiers can alter convergent signaling pathways and lead to impaired neural circuitry formation. Lastly, we review sex-linked modifiers and clinical implications. Further understanding of these mechanisms is crucial for both comprehending ASD and for developing novel therapies.

Integrated genetic and methylomic analyses identify shared biology between autism and autistic traits

Previous studies have identified differences in DNA methylation in autistic individuals compared to neurotypical individuals. Yet, it is unclear if this extends to autistic traits-subclinical manifestation of autism features in the general population. Here, we investigate the association between DNA methylation at birth (cord blood), and scores on the Social and Communication Disorders Checklist (SCDC), a measure of autistic traits, in 701 8-year-olds, by conducting a methylome-wide association study (MWAS). We did not identify significant CpGs associated with SCDC. The most significant CpG site was cg14379490, on chromosome 9 (MWAS beta = - 1.78 ± 0.35, p value = 5.34 × 10 -7 ). Using methylation data for autism in peripheral tissues, we did not identify a significant concordance in effect direction of CpGs with p value < 10-4 in the SCDC MWAS (binomial sign test, p value > 0.5). In contrast, using methylation data for autism from post-mortem brain tissues, we identify a significant concordance in effect direction of CpGs with a p value < 10-4 in the SCDC MWAS (binomial sign test, p value = 0.004). Supporting this, we observe an enrichment for genes that are dysregulated in the post-mortem autism brain (one-sided Wilcoxon rank-sum test, p value = 6.22 × 10-5). Finally, integrating genome-wide association study (GWAS) data for autism (n = 46,350) with mQTL maps from cord-blood (n = 771), we demonstrate that mQTLs of CpGs associated with SCDC scores at p value thresholds of 0.01 and 0.005 are significantly shifted toward lower p values in the GWAS for autism (p < 5 × 10-3). We provide additional support for this using a GWAS of SCDC, and demonstrate a lack of enrichment in a GWAS of Alzheimer's disease. Our results highlight the shared cross-tissue methylation architecture of autism and autistic traits, and demonstrate that mQTLs associated with differences in DNA methylation associated with childhood autistic traits are enriched for common genetic variants associated with autism and autistic traits.

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

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

Microglia along sex lines: From brain colonization, maturation and function, to implication in neurodevelopmental disorders

In addition to their traditional role as immune sentinels, recent discoveries over the last decade have shown that microglial functions now include regulation of neuronal/glial cell migration, differentiation and maturation, as well as neuronal network formation. It was thus proposed that disruption of these microglial roles, during critical periods of brain development, could lead to the pathological onset of several neurodevelopmental disorders, including autism spectrum disorder, attention deficit hyperactivity disorder, epilepsy, schizophrenia, and major depressive disorder. The prevalence of these disorders exhibits a clear distinction along sex lines with very little known about the mechanisms underlying this difference. One of the fundamental discoveries that arose from recent research into the physiological roles of microglia in neurodevelopment is their sexual dimorphism, raising the intriguing possibility that sex differences in microglial colonization, maturation and/or function in the developing brain could underlie the emergence of various neurodevelopmental disorders. This review discusses the physiological roles of microglia across neurodevelopment, these roles in the two sexes, and the recent evidence that microglial sexually dimorphic nature may contribute, at least partially, to neurodevelopmental disorders.

The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders

Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.

DNA methylation signatures as biomarkers of prior environmental exposures

Purpose of review

This review demonstrates the growing body of evidence connecting DNA methylation to prior exposure. It highlights the potential to use DNA methylation patterns as a feasible, stable, and accurate biomarker of past exposure, opening new opportunities for environmental and gene-environment interaction studies among existing banked samples.

Recent findings

We present the evidence for association between past exposure, including prenatal exposures, and DNA methylation measured at a later time in the life course. We demonstrate the potential utility of DNA methylation-based biomarkers of past exposure using results from multiple studies of smoking as an example. Multiple studies show the ability to accurately predict prenatal smoking exposure based on DNA methylation measured at birth, in childhood, and even adulthood. Separate sets of DNA methylation loci have been used to predict past personal smoking exposure (postnatal) as well. Further, it appears that these two types of exposures, prenatal and previous personal exposure, can be isolated from each other. There is also a suggestion that quantitative methylation scores may be useful for estimating dose. We highlight the remaining needs for rigor in methylation biomarker development including analytic challenges as well as the need for development across multiple developmental windows, multiple tissue types, and multiple ancestries.

Summary

If fully developed, DNA methylation-based biomarkers can dramatically shift our ability to carry out environmental and genetic-environmental epidemiology using existing biobanks, opening up unprecedented opportunities for environmental health.

Integration of DNA methylation patterns and genetic variation in human pediatric tissues help inform EWAS design and interpretation

Background

The widespread use of accessible peripheral tissues for epigenetic analyses has prompted increasing interest in the study of tissue-specific DNA methylation (DNAm) variation in human populations. To date, characterizations of inter-individual DNAm variability and DNAm concordance across tissues have been largely performed in adult tissues and therefore are limited in their relevance to DNAm profiles from pediatric samples. Given that DNAm patterns in early life undergo rapid changes and have been linked to a wide range of health outcomes and environmental exposures, direct investigations of tissue-specific DNAm variation in pediatric samples may help inform the design and interpretation of DNAm analyses from early life cohorts. In this study, we present a systematic comparison of genome-wide DNAm patterns between matched pediatric buccal epithelial cells (BECs) and peripheral blood mononuclear cells (PBMCs), two of the most widely used peripheral tissues in human epigenetic studies. Specifically, we assessed DNAm variability, cross-tissue DNAm concordance and genetic determinants of DNAm across two independent early life cohorts encompassing different ages.

Results

BECs had greater inter-individual DNAm variability compared to PBMCs and highly the variable CpGs are more likely to be positively correlated between the matched tissues compared to less variable CpGs. These sites were enriched for CpGs under genetic influence, suggesting that a substantial proportion of DNAm covariation between tissues can be attributed to genetic variation. Finally, we demonstrated the relevance of our findings to human epigenetic studies by categorizing CpGs from published DNAm association studies of pediatric BECs and peripheral blood.

Conclusions

Taken together, our results highlight a number of important considerations and practical implications in the design and interpretation of EWAS analyses performed in pediatric peripheral tissues.

Electronic supplementary material

The online version of this article (10.1186/s13072-018-0245-6) contains supplementary material, which is available to authorized users.

Autism Associated With Anti-NMDAR Encephalitis: Glutamate-Related Therapy

The purpose of this review is to correlate autism with autoimmune dysfunction in the absence of an explanation for the etiology of autism spectrum disorder. The anti-N-methyl-D-aspartate receptor (anti-NMDAR) autoantibody is a typical synaptic protein that can bind to synaptic NMDA glutamate receptors, leading to dysfunctional glutamate neurotransmission in the brain that manifests as psychiatric symptoms (psychosis, hallucinations, and personality changes). Detection of autoantibodies, cytokines, decreased lymphocytes, serum immunoglobulin level imbalance, T-cell mediated immune profile, maternal infection history, and children's infection history can all be vital biological markers of autoimmune autism. Diagnosing autoimmune encephalitis sooner can increase the effectiveness of curative treatments-such as immune therapy or immune modulatory therapy-that may prevent the long-term consequence of being misdiagnosed with autism spectrum disorder. Glutamate therapy primarily normalizes glutamate neurotransmission and can be a new add-on intervention alongside antipsychotics for treating autoimmune autism.

A Perspective of the Cross-Tissue Interplay of Genetics, Epigenetics, and Transcriptomics, and Their Relation to Brain Based Phenotypes in Schizophrenia

Genetic association studies of psychiatric disorders have provided unprecedented insight into disease risk profiles with high confidence. Yet, the next research challenge is how to translate this rich information into mechanisms of disease, and further help interventions and treatments. Given other comprehensive reviews elsewhere, here we want to discuss the research approaches that integrate information across various tissue types. Taking schizophrenia as an example, the tissues, cells, or organisms being investigated include postmortem brain tissues or neurons, peripheral blood and saliva, in vivo brain imaging, and in vitro cell lines, particularly human induced pluripotent stem cells (iPSC) and iPSC derived neurons. There is a wealth of information on the molecular signatures including genetics, epigenetics, and transcriptomics of various tissues, along with neuronal phenotypic measurements including neuronal morphometry and function, together with brain imaging and other techniques that provide data from various spatial temporal points of disease development. Through consistent or complementary processes across tissues, such as cross-tissue methylation quantitative trait loci (QTL) and expression QTL effects, systemic integration of such information holds the promise to put the pieces of puzzle together for a more complete view of schizophrenia disease pathogenesis.

Case-control meta-analysis of blood DNA methylation and autism spectrum disorder

Background

Several reports have suggested a role for epigenetic mechanisms in ASD etiology. Epigenome-wide association studies (EWAS) in autism spectrum disorder (ASD) may shed light on particular biological mechanisms. However, studies of ASD cases versus controls have been limited by post-mortem timing and severely small sample sizes. Reports from in-life sampling of blood or saliva have also been very limited in sample size and/or genomic coverage. We present the largest case-control EWAS for ASD to date, combining data from population-based case-control and case-sibling pair studies.

Methods

DNA from 968 blood samples from children in the Study to Explore Early Development (SEED 1) was used to generate epigenome-wide array DNA methylation (DNAm) data at 485,512 CpG sites for 453 cases and 515 controls, using the Illumina 450K Beadchip. The Simons Simplex Collection (SSC) provided 450K array DNAm data on an additional 343 cases and their unaffected siblings. We performed EWAS meta-analysis across results from the two data sets, with adjustment for sex and surrogate variables that reflect major sources of biological variation and technical confounding such as cell type, batch, and ancestry. We compared top EWAS results to those from a previous brain-based analysis. We also tested for enrichment of ASD EWAS CpGs for being targets of meQTL associations using available SNP genotype data in the SEED sample.

Findings

In this meta-analysis of blood-based DNA from 796 cases and 858 controls, no single CpG met a Bonferroni discovery threshold of p < 1.12 × 10^− 7. Seven CpGs showed differences at p < 1 × 10^− 5 and 48 at 1 × 10^− 4. Of the top 7, 5 showed brain-based ASD associations as well, often with larger effect sizes, and the top 48 overall showed modest concordance (r = 0.31) in direction of effect with cerebellum samples. Finally, we observed suggestive evidence for enrichment of CpG sites controlled by SNPs (meQTL targets) among the EWAS CpG hits, which was consistent across EWAS and meQTL discovery p value thresholds.

Conclusions

No single CpG site showed a large enough DNAm difference between cases and controls to achieve epigenome-wide significance in this sample size. However, our results suggest the potential to observe disease associations from blood-based samples. Among the seven sites achieving suggestive statistical significance, we observed consistent, and stronger, effects at the same sites among brain samples. Discovery-oriented EWAS for ASD using blood samples will likely need even larger samples and unified genetic data to further understand DNAm differences in ASD.

Electronic supplementary material

The online version of this article (10.1186/s13229-018-0224-6) contains supplementary material, which is available to authorized users.