Low-Pass Whole Genome Bisulfite Sequencing of Neonatal Dried Blood Spots Identifies a Role for RUNX1 in Down Syndrome DNA Methylation Profiles

Abstract

Neonatal dried blood spots (NDBS) are a widely banked sample source that enable retrospective investigation into early-life molecular events. Here, we performed low-pass whole genome bisulfite sequencing (WGBS) of 86 NDBS DNA to examine early-life Down syndrome (DS) DNA methylation profiles. DS represents an example of genetics shaping epigenetics, as multiple array-based studies have demonstrated that trisomy 21 is characterized by genome-wide alterations to DNA methylation. By assaying over 24 million CpG sites, thousands of genome-wide significant (q < 0.05) DMRs that distinguished DS from typical development (TD) and idiopathic developmental delay (DD) were identified. Machine learning feature selection refined these DMRs to 22 loci. The DS DMRs mapped to genes involved in neurodevelopment, metabolism, and transcriptional regulation. Based on comparisons to previous DS methylation studies and reference epigenomes, the hypermethylated DS DMRs were significantly (q < 0.05) enriched across tissues while the hypomethylated DS DMRs were significantly (q < 0.05) enriched for blood-specific chromatin states. A ∼28 kb block of hypermethylation was observed on chromosome 21 in the RUNX1 locus, which encodes a hematopoietic transcription factor whose binding motif was the most significantly enriched (q < 0.05) overall and specifically within the hypomethylated DMRs. Finally, we also identified DMRs that distinguished DS NDBS based on the presence or absence of congenital heart disease (CHD). Together, these results not only demonstrate the utility of low-pass WGBS on NDBS samples for epigenome-wide association studies, but also provide new insights into the early-life mechanisms of epigenomic dysregulation resulting from trisomy 21.

Stable DNMT3L overexpression in SH-SY5Y neurons recreates a facet of the genome-wide Down syndrome DNA methylation signature

“…To test the hypothesis that the DNMT3L DMRs represent a facet of the differentially methylated genes observed in DS, cross-tissue and pan-tissue comparisons with differentially methylated DS sites across diverse tissues were performed [ 26 – 35 ]. These results revealed a significant ( q < 0.05) enrichment of the DNMT3L DMRs within DS differentially methylated sites identified from multiple tissues (Fig.…”

StableDNMT3LOverexpression in SH-SY5Y Neurons Recreates a Facet of the Genome-Wide Down Syndrome DNA Methylation Signature

“…To determine the similarities between the DNMT3L-specific DMRs and the DNA methylation changes previously observed in DS, a pan-tissue comparison with differentially methylated DS sites across diverse tissues was performed (30,39–47). These results revealed significant overlap with DS differentially methylated sites, predominantly within the hypermethylated DNMT3L DMRs ( Figure 6C ).…”

“…Trimming of adapters and methylation bias, screening for contaminating genomes, alignments to hg38, deduplication, calculation of coverage and insert size metrics, extraction of CpG methylation values, generation of genome-wide cytosine reports (CpG count matrix), and examination of quality control metrics were performed using CpG_Me (https://github.com/ben-laufer/CpG_Me) (27–30). DMR calling as well as most downstream analyses were performed using DMRichR, which utilizes the dmrseq and bsseq algorithms (https://github.com/ben-laufer/DMRichR) (30–32).…”

“…Linear mixed effects model of the average methylation level were utilized for global and CpG island methylation level analyses, where genotype (3L or GFP) and cellular differentiation phase were direct effects, while cell line of origin was a random effect. Transcription factor motif enrichment testing using HOMER (33), DS pan-tissue enrichment testing via GAT (34), as well as enrichment testing for histone post-translational modifications (5 marks, 127 epigenomes) and chromatin states (15-state chromHMM model) (35,36) through LOLA (37), were all performed as previously described (30).…”

“…Trimming of adapters and methylation bias, screening for contaminating genomes, alignments to hg38, deduplication, calculation of coverage and insert size metrics, extraction of CpG methylation values, generation of genome-wide cytosine reports (CpG count matrix), and examination of quality control metrics were performed using CpG_Me (https://github.com/benlaufer/CpG_Me) (27)(28)(29)(30). DMR calling as well as most downstream analyses were performed using DMRichR, which utilizes the dmrseq and bsseq algorithms (https://github.com/benlaufer/DMRichR) (30)(31)(32). Linear mixed effects model of the average methylation level were utilized for global and CpG island methylation level analyses, where genotype (3L or GFP) and cellular differentiation phase were direct effects, while cell line of origin was a random effect.…”