Perinatal Epigenomic Signatures in Down Syndrome and Autism Spectrum Disorders

DNA methylation is a metastable epigenetic mark that reflects genetic and environmental influences on health and disease. As the epigenome is established during the perinatal period, environmental exposures experienced during this time have the greatest impact on the methylome, as well as resulting changes in gene expression and phenotype. This unique ability of DNA methylation to reflect both etiology and phenotypes of disease makes it an attractive clinical biomarker, particularly if assayed from perinatal tissues such as placenta and newborn blood. In this dissertation, we assay DNA methylation of perinatal tissues to gain etiological insights and identify potential biomarkers of congenital heart defects (CHDs) in individuals with Down syndrome (DS) and of autism spectrum disorders (ASD). DS is a genetic disorder caused by trisomy 21 that results in developmental and intellectual delays. CHDs affect approximately 50% of individuals with DS, though the molecular reasons behind this incomplete penetrance is unknown. We investigated an epigenomic signature of DS-CHD using whole genome bisulfite sequencing (WGBS) in newborn blood from DS patients with and without CHDs. We identified sex-specific differentially methylated regions (DMRs), which were enriched for terms related to cardiac and immune functions. Machine learning algorithms selected 19 DMRs identified in males that could distinguish CHD from non-CHD. This study showed a sex-specific signature of DS-CHD and showed that the newborn blood methylome can reflect the variability in phenotypes in DS.ASD is a group of neurodevelopmental disorders that arise from a poorly understood combination of genetic factors, environmental factors, and gene-environment interactions. To improve our understanding of the combinatorial effects of potential risk and/or protective factors for ASD, we identified co-methylation networks from WGBS of placental and newborn blood samples and identified modules that were significantly associated with ASD diagnosis in the child. In both tissues, these ASD co-methylation modules mapped to genes previously implicated in neurodevelopment, including CSMD1, AUTS2, and GALC from placenta, and RANBP17, TLX3, PTPRJ and TRIM49B from newborn blood. These ASD modules also associated with potential risk factors for ASD, including polychlorinated biphenyl levels in maternal serum and maternal hypertension (placental modules) and grandparental cigarette smoking, alcohol use, and advanced age when their child was born (newborn blood modules). These network analyses showed that dysregulated DNA methylation at particular loci may provide a biological connection between ASD diagnosis and multivariate factors in the parents and grandparents. We also identified a pan-tissue epigenomic signature of ASD through DMR analysis of newborn blood, placenta, cord blood, and post-mortem cortex from ASD and TD individuals. We found that the epigenomic signature of ASD was stronger in females than males, with DMRs from all four tissues showing greater overlap in females and enrichment for gene ontology terms related to neurodevelopment. In females, one or more DMRs from all four tissues mapped to BCOR, GALNT9, and OPCML, making these interesting targets for future ASD studies. The findings in this dissertation increase our understanding of the etiological basis of DS-CHD and ASD and provide evidence for the utility of perinatal methylation signatures in the development of clinical biomarkers. This work benefitted from access to perinatal tissue samples and robust participant data from multiple cohorts; methylation sequencing over the entire genome; and bioinformatic tools for DMR and co-methylation network analysis. Overall, these studies help us move towards earlier interventions for patients with DS-CHD or ASD by improving our understanding of risk factors and biomarkers for these conditions

The Promise of DNA Methylation in Understanding Multigenerational Factors in Autism Spectrum Disorders

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders characterized by impairments in social reciprocity and communication, restrictive interests, and repetitive behaviors. Most cases of ASD arise from a confluence of genetic susceptibility and environmental risk factors, whose interactions can be studied through epigenetic mechanisms such as DNA methylation. While various parental factors are known to increase risk for ASD, several studies have indicated that grandparental and great-grandparental factors may also contribute. In animal studies, gestational exposure to certain environmental factors, such as insecticides, medications, and social stress, increases risk for altered behavioral phenotypes in multiple subsequent generations. Changes in DNA methylation, gene expression, and chromatin accessibility often accompany these altered behavioral phenotypes, with changes often appearing in genes that are important for neurodevelopment or have been previously implicated in ASD. One hypothesized mechanism for these phenotypic and methylation changes includes the transmission of DNA methylation marks at individual chromosomal loci from parent to offspring and beyond, called multigenerational epigenetic inheritance. Alternatively, intermediate metabolic phenotypes in the parental generation may confer risk from the original grandparental exposure to risk for ASD in grandchildren, mediated by DNA methylation. While hypothesized mechanisms require further research, the potential for multigenerational epigenetics assessments of ASD risk has implications for precision medicine as the field attempts to address the variable etiology and clinical signs of ASD by incorporating genetic, environmental, and lifestyle factors. In this review, we discuss the promise of multigenerational DNA methylation investigations in understanding the complex etiology of ASD

Comethyl: a network-based methylome approach to investigate the multivariate nature of health and disease.

Health outcomes are frequently shaped by difficult to dissect inter-relationships between biological, behavioral, social and environmental factors. DNA methylation patterns reflect such multivariate intersections, providing a rich source of novel biomarkers and insight into disease etiologies. Recent advances in whole-genome bisulfite sequencing enable investigation of DNA methylation over all genomic CpGs, but existing bioinformatic approaches lack accessible system-level tools. Here, we develop the R package Comethyl, for weighted gene correlation network analysis of user-defined genomic regions that generates modules of comethylated regions, which are then tested for correlations with multivariate sample traits. First, regions are defined by CpG genomic location or regulatory annotation and filtered based on CpG count, sequencing depth and variability. Next, correlation networks are used to find modules of interconnected nodes using methylation values within the selected regions. Each module containing multiple comethylated regions is reduced in complexity to a single eigennode value, which is then tested for correlations with experimental metadata. Comethyl has the ability to cover the noncoding regulatory regions of the genome with high relevance to interpretation of genome-wide association studies and integration with other types of epigenomic data. We demonstrate the utility of Comethyl on a dataset of male cord blood samples from newborns later diagnosed with autism spectrum disorder (ASD) versus typical development. Comethyl successfully identified an ASD-associated module containing regions mapped to genes enriched for brain glial functions. Comethyl is expected to be useful in uncovering the multivariate nature of health disparities for a variety of common disorders. Comethyl is available at github.com/cemordaunt/comethyl with complete documentation and example analyses

Epigenomic signature of major congenital heart defects in newborns with Down syndrome

BackgroundCongenital heart defects (CHDs) affect approximately half of individuals with Down syndrome (DS), but the molecular reasons for incomplete penetrance are unknown. Previous studies have largely focused on identifying genetic risk factors associated with CHDs in individuals with DS, but comprehensive studies of the contribution of epigenetic marks are lacking. We aimed to identify and characterize DNA methylation differences from newborn dried blood spots (NDBS) of DS individuals with major CHDs compared to DS individuals without CHDs.MethodsWe used the Illumina EPIC array and whole-genome bisulfite sequencing (WGBS) to quantitate DNA methylation for 86 NDBS samples from the California Biobank Program: (1) 45 DS-CHD (27 female, 18 male) and (2) 41 DS non-CHD (27 female, 14 male). We analyzed global CpG methylation and identified differentially methylated regions (DMRs) in DS-CHD versus DS non-CHD comparisons (both sex-combined and sex-stratified) corrected for sex, age of blood collection, and cell-type proportions. CHD DMRs were analyzed for enrichment in CpG and genic contexts, chromatin states, and histone modifications by genomic coordinates and for gene ontology enrichment by gene mapping. DMRs were also tested in a replication dataset and compared to methylation levels in DS versus typical development (TD) WGBS NDBS samples.ResultsWe found global CpG hypomethylation in DS-CHD males compared to DS non-CHD males, which was attributable to elevated levels of nucleated red blood cells and not seen in females. At a regional level, we identified 58, 341, and 3938 CHD-associated DMRs in the Sex Combined, Females Only, and Males Only groups, respectively, and used machine learning algorithms to select 19 Males Only loci that could distinguish CHD from non-CHD. DMRs in all comparisons were enriched for gene exons, CpG islands, and bivalent chromatin and mapped to genes enriched for terms related to cardiac and immune functions. Lastly, a greater percentage of CHD-associated DMRs than background regions were differentially methylated in DS versus TD samples.ConclusionsA sex-specific signature of DNA methylation was detected in NDBS of DS-CHD compared to DS non-CHD individuals. This supports the hypothesis that epigenetics can reflect the variability of phenotypes in DS, particularly CHDs

Epigenomic signature of major congenital heart defects in newborns with Down syndrome

Abstract Background Congenital heart defects (CHDs) affect approximately half of individuals with Down syndrome (DS), but the molecular reasons for incomplete penetrance are unknown. Previous studies have largely focused on identifying genetic risk factors associated with CHDs in individuals with DS, but comprehensive studies of the contribution of epigenetic marks are lacking. We aimed to identify and characterize DNA methylation differences from newborn dried blood spots (NDBS) of DS individuals with major CHDs compared to DS individuals without CHDs. Methods We used the Illumina EPIC array and whole-genome bisulfite sequencing (WGBS) to quantitate DNA methylation for 86 NDBS samples from the California Biobank Program: (1) 45 DS-CHD (27 female, 18 male) and (2) 41 DS non-CHD (27 female, 14 male). We analyzed global CpG methylation and identified differentially methylated regions (DMRs) in DS-CHD versus DS non-CHD comparisons (both sex-combined and sex-stratified) corrected for sex, age of blood collection, and cell-type proportions. CHD DMRs were analyzed for enrichment in CpG and genic contexts, chromatin states, and histone modifications by genomic coordinates and for gene ontology enrichment by gene mapping. DMRs were also tested in a replication dataset and compared to methylation levels in DS versus typical development (TD) WGBS NDBS samples. Results We found global CpG hypomethylation in DS-CHD males compared to DS non-CHD males, which was attributable to elevated levels of nucleated red blood cells and not seen in females. At a regional level, we identified 58, 341, and 3938 CHD-associated DMRs in the Sex Combined, Females Only, and Males Only groups, respectively, and used machine learning algorithms to select 19 Males Only loci that could distinguish CHD from non-CHD. DMRs in all comparisons were enriched for gene exons, CpG islands, and bivalent chromatin and mapped to genes enriched for terms related to cardiac and immune functions. Lastly, a greater percentage of CHD-associated DMRs than background regions were differentially methylated in DS versus TD samples. Conclusions A sex-specific signature of DNA methylation was detected in NDBS of DS-CHD compared to DS non-CHD individuals. This supports the hypothesis that epigenetics can reflect the variability of phenotypes in DS, particularly CHDs

Networks of placental DNA methylation correlate with maternal serum PCB concentrations and child neurodevelopment.

BackgroundGestational exposure to polychlorinated biphenyls (PCBs) has been associated with elevated risk for neurodevelopmental disorders. Placental epigenetics may serve as a potential mechanism of risk or marker of altered placental function. Prior studies have associated differential placental DNA methylation with maternal PCB exposure or with increased risk of autism spectrum disorder (ASD). However, sequencing-based placental methylomes have not previously been tested for simultaneous associations with maternal PCB levels and child neurodevelopmental outcomes.ObjectivesWe aimed to identify placental DNA methylation patterns associated with maternal PCB levels and child neurodevelopmental outcomes in the high-risk ASD MARBLES cohort.MethodsWe measured 209 PCB congeners in 104 maternal serum samples collected at delivery. We identified networks of DNA methylation from 147 placenta samples using the Comethyl R package, which performs weighted gene correlation network analysis for whole genome bisulfite sequencing data. We tested placental DNA methylation modules for association with maternal serum PCB levels, child neurodevelopment, and other participant traits.ResultsPCBs 153 + 168, 170, 180 + 193, and 187 were detected in over 50% of maternal serum samples and were highly correlated with one another. Consistent with previous findings, maternal age was the strongest predictor of serum PCB levels, alongside year of sample collection, pre-pregnancy BMI, and polyunsaturated fatty acid levels. Twenty seven modules of placental DNA methylation were identified, including five which significantly correlated with one or more PCBs, and four which correlated with child neurodevelopment. Two modules associated with maternal PCB levels as well as child neurodevelopment, and mapped to CSMD1 and AUTS2, genes previously implicated in ASD and identified as differentially methylated regions in mouse brain and placenta following gestational PCB exposure.ConclusionsPlacental DNA co-methylation modules were associated with maternal PCBs and child neurodevelopment. Methylation of CSMD1 and AUTS2 could be markers of altered placental function and/or ASD risk following maternal PCB exposure

Additional file 1 of Epigenomic signature of major congenital heart defects in newborns with Down syndrome

Additional file 1: Table S1. Study characteristics: description of sample traits included in study. Table S2. Sample traits: values of traits for each sample. Table S3. Welch’s t test for differences in sample traits in DS-CHD versus DS non-CHD samples. Table S4. Pearson correlation coefficients and p values (unadjusted and adjusted) for sample traits. Table S5. EPIC array beta values. Table S6. Logistic regression for CHD and linear regression for global methylation. Table S7. Annotated Sex Combined DMRs (adjusted for sex, age of blood collection, cell types). Table S8. Annotated Females Only DMRs (adjusted for age of blood collection, cell types). Table S9. Annotated Males Only DMRs (adjusted for age of blood collection, cell types). Table S10. Annotated Males Only DMRs (adjusted for age of blood collection, cell types) Sensitivity Analysis (5 samples with nRBC > 20% removed). Table S11. Machine Learning DMRs. Table S12. Smoothed methylation of Sex-combined DMRs in replication dataset. Table S13. Smoothed methylation of Females Only DMRs in replication dataset. Table S14. Smoothed methylation of Males Only DMRs in replication dataset. Table S15. Stats from permutation testing of DMR overlaps from Sex Combined, Females Only, and Males Only comparisons. Table S16. Chromosome location enrichments of Sex Combined, Females Only, and Males Only DMRs. Table S17. CpG and Genic enrichments for Sex Combined, Females Only, and Males Only comparisons. Table S18. GREAT gene ontology enrichments for Sex-Combined DMRs compared to background regions. Table S19. GREAT gene ontology enrichments for Females Only DMRs compared to background regions. Table S20. GREAT gene ontology enrichments for Males Only DMRs compared to background regions. Table S21. Smoothed methylation of Sex-combined DMRs in DSvTD dataset. Table S22. Smoothed methylation of Females Only DMRs in DSvTD dataset. Table S23. Smoothed methylation of Males Only DMRs in DSvTD dataset

Placental methylome reveals a 22q13.33 brain regulatory gene locus associated with autism.

BackgroundAutism spectrum disorder (ASD) involves complex genetics interacting with the perinatal environment, complicating the discovery of common genetic risk. The epigenetic layer of DNA methylation shows dynamic developmental changes and molecular memory of in utero experiences, particularly in placenta, a fetal tissue discarded at birth. However, current array-based methods to identify novel ASD risk genes lack coverage of the most structurally and epigenetically variable regions of the human genome.ResultsWe use whole genome bisulfite sequencing in placenta samples from prospective ASD studies to discover a previously uncharacterized ASD risk gene, LOC105373085, renamed NHIP. Out of 134 differentially methylated regions associated with ASD in placental samples, a cluster at 22q13.33 corresponds to a 118-kb hypomethylated block that replicates in two additional cohorts. Within this locus, NHIP is functionally characterized as a nuclear peptide-encoding transcript with high expression in brain, and increased expression following neuronal differentiation or hypoxia, but decreased expression in ASD placenta and brain. NHIP overexpression increases cellular proliferation and alters expression of genes regulating synapses and neurogenesis, overlapping significantly with known ASD risk genes and NHIP-associated genes in ASD brain. A common structural variant disrupting the proximity of NHIP to a fetal brain enhancer is associated with NHIP expression and methylation levels and ASD risk, demonstrating a common genetic influence.ConclusionsTogether, these results identify and initially characterize a novel environmentally responsive ASD risk gene relevant to brain development in a hitherto under-characterized region of the human genome