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Neuronal brain-region-specific DNA methylation and chromatin accessibility are associated with neuropsychiatric trait heritability

Nature Neuroscience volume 22, pages 307–316 (2019)Cite this article

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Abstract

Epigenetic modifications confer stable transcriptional patterns in the brain, and both normal and abnormal brain function involve specialized brain regions. We examined DNA methylation by whole-genome bisulfite sequencing in neuronal and non-neuronal populations from four brain regions (anterior cingulate gyrus, hippocampus, prefrontal cortex, and nucleus accumbens) as well as chromatin accessibility in the latter two. We find pronounced differences in both CpG and non-CpG methylation (CG-DMRs and CH-DMRs) only in neuronal cells across brain regions. Neuronal CH-DMRs were highly associated with differential gene expression, whereas CG-DMRs were consistent with chromatin accessibility and enriched for regulatory regions. These CG-DMRs comprise ~12 Mb of the genome that is highly enriched for genomic regions associated with heritability of neuropsychiatric traits including addictive behavior, schizophrenia, and neuroticism, thus suggesting a mechanistic link between pathology and differential neuron-specific epigenetic regulation in distinct brain regions.

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Fig. 1: Neuronal nuclei isolated from different brain regions display widespread differences in CpG methylation.
Fig. 2: Differential non-CpG methylation among neuronal nuclei from different brain regions.
Fig. 3: Chromatin accessibility landscape differs between neuronal nuclei isolated from the nucleus accumbens and prefrontal cortex.
Fig. 4: Differential gene expression is strongly associated with neuronal CA-DMRs.
Fig. 5: Neuronal CG-DMRs and DARs are highly enriched for explained heritability of multiple psychiatric, neurological, and behavioral-cognitive traits.

Data Availability

Raw and processed data generated are available through NCBI GEO under accession number GSE96615. Processed data is available through a UCSC hub, at http://genome.ucsc.edu/cgi-bin/hgTracks?db = hg19&hubUrl = https://s3.us-east-2.amazonaws.com/brainepigenome/hub.txt.

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Acknowledgements

This work was supported by funding awarded to A.P.F. (U01MH104393) through the enhanced Genotype-Tissue Expression (eGTEx) project supported by the Common Fund of the Office of the Director of NIH. This work was supported by National Cancer Institute under U24CA180996. We would like to thank H. Zhang from the Flow Cytometry Cell Sorting Core Facility at Johns Hopkins School of Public Health for flow sorting. The core facility is supported by CFAR: 5P30AI094189–04, 1S10OD016315–01, and 1S10RR13777001. Brain tissues were received from the NIH NeuroBioBank at the University of Maryland and University of Pittsburgh.

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Author notes

  1. These authors contributed equally: Lindsay F. Rizzardi, Peter F. Hickey.

Authors and Affiliations

  1. Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Lindsay F. Rizzardi, Varenka Rodriguez DiBlasi, Rakel Tryggvadóttir, Colin M. Callahan, Adrian Idrizi, Kasper D. Hansen & Andrew P. Feinberg

  2. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Lindsay F. Rizzardi, Varenka Rodriguez DiBlasi & Andrew P. Feinberg

  3. Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

    Peter F. Hickey & Kasper D. Hansen

  4. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Kasper D. Hansen

  5. Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA

    Andrew P. Feinberg

  6. Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

    Andrew P. Feinberg

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Contributions

L.F.R, K.D.H, and A.P.F designed the study; L.F.R. performed nuclei sorting, DNA and RNA extractions; V.R.D. performed ATAC-seq; R.T., A.I., C.M.C. performed WGBS and RNA-seq library preparation and sequencing; A.P.F. oversaw the experiments; K.D.H. oversaw the data analysis. L.F.R, P.F.H., K.D.H, and A.P.F. performed data analysis and interpreted the results; L.F.R, P.F.H., K.D.H, and A.P.F wrote manuscript.

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Correspondence to Kasper D. Hansen or Andrew P. Feinberg.

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Integrated supplementary information

Supplementary Figure 1 Isolation and global methylation analysis of neuronal (NeuN+) and non-neuronal (NeuN−) nuclei from frozen brain tissues.

(a) Nuclei in this representative example were isolated by fluorescence activated nuclei sorting (FANS) from prefrontal cortex (BA9) and debris, doublets, and auto-fluorescent nuclei were gated out. The remaining nuclei were separated based on detection of AlexaFluor 488-conjugated anti-NeuN antibody. FANS was repeated a minimum of 8 times per brain region. (b) Example of nuclei stained with anti-NeuN and DAPI both pre- and post-sort; staining repeated twice with similar results. (c) Proportion of NeuN+ nuclei isolated via FANS from tissue samples from indicated brain regions; arrows indicate two punches from the same tissue sample. Tissues are from BA9, anterior cingulate cortex (BA24), hippocampus (HC) and nucleus accumbens (NAcc) from six individuals as indicated. (d) Proportion of NeuN+ nuclei isolated via FANS plotted against post-mortem interval from tissue samples from indicated brain regions (BA9, n = 11; BA24, n = 13; HC, n = 13; NAcc, n = 8 individuals). Overlaid is a line showing the conditional mean, and shaded 95% confidence interval, for this relationship as estimated using a linear model within each tissue. (e) Average autosomal CpG (mCG) and (f) non-CpG (mCH) methylation of NeuN+ (n = 6 individuals for BA9, HC, NAcc and n = 5 for BA24) and NeuN− nuclei (n = 6 individuals for BA9, HC, NAcc and n = 4 for BA24); significant differences in global mC between tissues within dinucleotide contexts and NeuN status are indicated. P-values in (e,f) are adjusted for multiple comparisons using Tukey’s honest significant difference test.

Supplementary Figure 2 Differences in DNA methylation between brain regions are restricted to neuronal nuclei.

Principal component analyses (PCA) of distances derived from average autosomal CpG methylation levels in 1 kb intervals in (a) bulk tissues, (b) NeuN+ nuclei, (c) bulk tissues and sorted nuclei combined, and (d) NeuN− nuclei. Tissues, sample type, and individuals are indicated as shown. (e) Correlation matrix between samples based on average methylation of CpGs shared across all samples. (a-e) Tissues are from anterior cingulate cortex (BA24; NeuN+ n = 5 individuals; NeuN− n = 4, bulk n = 6), dorsolateral prefrontal cortex (BA9; NeuN+ n = 6, NeuN− n = 6, bulk n = 6), hippocampus (HC; NeuN+ n = 6, NeuN− n = 6, bulk n = 6), and nucleus accumbens (NAcc; NeuN+ n = 6, NeuN− n = 6, bulk n = 6).

Supplementary Figure 3 Variability analysis across sample types.

(a) Pearson correlations for DNA methylation between samples from the same tissue within either a donor (bulk samples composed of both NeuN+ and NeuN−; n = 27) or a cell type (NeuN+, n = 23; or NeuN−, n = 22 samples). (b) Between sample variation within a brain region and sample type, as a function of methylation level. (c) Proportion of CpGs with an estimated absolute methylation difference greater than 10% (chosen because CG-DMRs all have an absolute methylation difference > 10%) between the indicated brain regions, stratified by sample type (NeuN+, NeuN−, bulk).

Supplementary Figure 4 Methylation profiles are distinct between neuronal and non-neuronal nuclei and among neurons from distinct brain regions.

(a) Hierarchical clustering of samples based on the average per sample methylation of the top 20,000 cell type-specific CG-DMRs. Examples of (b) cell type-specific CG-DMRs and a cell type-specific block of differential methylation (NeuN+ is green; NeuN− is purple) and (c) neuronal CG-DMRs. Methylation values are shown with CG-DMRs shaded pink and overlap with protein-coding genes depicted below each graph. (d) Average CpG methylation values for NeuN+ nuclei within each brain region over a 3 kb window centered on the CG-DMRs (n = 208) identified among BA9, BA24, and HC. For BA9 and HC, n = 6 individuals and n = 5 individuals for BA24. CG-DMRs were grouped by k-means clustering based on their methylation patterns. Metagene plots for each group are shown to the right with the number of CG-DMRs indicated. (e) As in (c) with normalized ATAC sequencing coverage for NeuN+ nuclei from NAcc and BA9 (individual means are transparent lines; tissue means are opaque lines). Regions of differential accessibility (DARs), CG-DMRs and CG-blocks are shaded pink.

Supplementary Figure 5 Non-CpG methylation is similar across strands and contexts.

Neuronal (NeuN+) non-CpG methylation was measured in 1 kb bins (n = 2,881,044) across the autosomes. Bins with little to no coverage of cytosines were removed. (a) Non-CpG methylation in the CA context on the positive strand compared across two biological replicates. (b) Non-CpG methylation in the CA context compared between the positive and negative strand within an individual sample. (c) Non-CpG methylation on the positive strand compared between the CA context and the CT context within an individual sample. Solid red line is y = x, dashed red line is the best regression line through the origin (correlation and equation shown on graph).

Supplementary Figure 6 Non-CpG methylation is consistent across strand and context and with CpG methylation.

Hierarchical clustering of samples based on the z-score of methylation over (a) CA(-) DMRs, (b) CT( + ) DMRs, and (c) CT(-) DMRs. (d) Hierarchical clustering of samples based on z-scores of methylation for CA( + ) DMRs that overlap CG-DMRs.

Supplementary Figure 7 Open chromatin regions clearly distinguish NeuN+ from NeuN− samples.

(a) Sample correlation matrix of ATAC-seq read counts [log2(counts per million)] in open chromatin regions (green = NeuN+ nuclei, purple = NeuN− nuclei, orange = nucleus accumbens (NAcc), blue = prefrontal cortex (BA9)). For NeuN+ and NeuN− samples from BA9 and NAcc, n = 6 individuals each. (b) Mean-difference plot of peak accessibility data comparing: NeuN+ to NeuN− nuclei. Differentially accessible regions (DARs) are shown in orange. Data were randomly sampled (100% of DARs, 10% of non-DARs).

Supplementary Figure 8 DNA methylation and chromatin accessibility are correlated with gene expression.

(a) Average methylation levels around protein coding genes (n = 19,823) with different expression levels in neuronal nuclei isolated from nucleus accumbens (NAcc) and prefrontal cortex (BA9). Each gene length is split into 100 bins and data extend two lengths up and downstream. Gene expression is split into quartiles based on RPKM. (b,c) Scatterplots showing Pearson correlation of differential gene expression [log2(fold change)] (n = 16,538) with: (b) differential mCA and mCG, and (c) differential accessibility [log2(fold change)] from NAcc vs BA9 neuronal nuclei. Differentially expressed protein coding genes (DEGs) are shown in orange. Correlation values (with 95% CI) are given below each graph. (d,e) Scatterplots showing Pearson correlations of differential gene expression [log2(fold change)] with differential epigenetic features from NAcc vs BA9 neuronal nuclei that overlap (d) promoters and (e) gene bodies. DEGs are shown in orange and the number of genes (n) with a feature(s) is indicated. (f) Relationships between expression of protein coding genes and chromatin accessibility, mCA, and mCG within the gene body in NAcc and BA9 neuronal nuclei. Contours show point densities; red line shows smoothed trend. (a-f) n = 6 individuals for both NAcc and BA9 neuronal samples.

Supplementary Figure 9 SLDSR analysis of each feature.

Each feature depicted in the figure was included separately in an SLDSR analysis including 53 baseline features. Results from our differential and the 3 non-differential epigenomic features (Brain H3K27ac30, CNS51, chromHMM15) are shown by trait; the same results are displayed in Fig. 5 without the ability to identify individual traits. (a) Coefficient z-scores. Enrichment is only reported for traits where at least one feature has a z-score significantly larger than 0 (one-sided z-test with alpha = 0.05, P-values corrected within each trait using Holm’s method). (b) Enrichment + /− 2 standard errors. GWAS sizes for each trait are reported in Supplementary Table 24.

Supplementary Figure 10 SLDSR analysis of differential epigenomic features, controlling for nondifferential epigenomic features.

Each of the differential epigenomic features (neuronal CG-DMRs, DARs and cell-type CG-DMRs and DARs) was included separately in a SLDSR analysis with the 3 non-differential epigenomic features (Brain H3K27ac30, CNS51, chromHMM15) included in the baseline (53 features). Results are shown by trait; the same results are displayed in Fig. 5 without the ability to identify individual traits. (a) Coefficient z-scores. Enrichment is only reported for traits where at least one feature has a z-score significantly larger than 0 (one-sided z-test with alpha = 0.05, P-values corrected within each trait using Holm’s method). (b) Enrichment + /− 2 standard errors. GWAS sizes for each trait are reported in Supplementary Table 24.

Supplementary Figure 11 SLDSR analysis of differential epigenomic features, controlling for nondifferential epigenomic features by excluding their overlap from baseline.

Each of the differential epigenomic features (neuronal CG-DMRs, DARs and cell-type CG-DMRs and DARs) was including separately in a SLDSR analysis including the 3 non-differential epigenomic features (Brain H3K27ac30, CNS51, chromHMM15) in the baseline. For the 3 non-differential epigenomic features included in the baseline, we removed any overlap with the differential epigenomic features. Results are shown by trait; the same results are displayed in Fig. 5 without the ability to identify individual traits. (a) Coefficient z-scores. Enrichment is only reported for traits where at least one feature has a z-score significantly larger than 0 (one-sided z-test with alpha = 0.05, P-values corrected within each trait using Holm’s method). (b) Enrichment + /− 2 standard errors. GWAS sizes for each trait are reported in Supplementary Table 24.

Supplementary information

Supplementary Figures 1–11

Reporting Summary

Supplementary Tables 1–4, 6–9, 11, 12, 14–16, 21, 23, 24, 26

Supplementary Table 5

Supplementary Table 10

GREAT Analysis of CG-DMRs where NAcc is hypomethylated compared to other three brain regions.

Supplementary Table 13

List of non-CpG DMRs (CA- and CT-DMRs).

Supplementary Table 17

GO analysis of differentially expressed genes in NAcc vs. BA9 in neurons.

Supplementary Table 18

Differentially accessible regions between NeuN-positive and NeuN-negative samples.

Supplementary Table 19

Differentially accessible regions between NAcc and BA9 neurons.

Supplementary Table 20

Differentially accessible regions between NAcc and BA9 in NeuN-negative samples (non-neurons).

Supplementary Table 22

Transcription factor motif enrichments with neuronal DARs overlapping hyper- or hypomethylated neuronal NAcc CG-DMRs.

Supplementary Table 25

SLDSR analysis results.

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Rizzardi, L.F., Hickey, P.F., Rodriguez DiBlasi, V. et al. Neuronal brain-region-specific DNA methylation and chromatin accessibility are associated with neuropsychiatric trait heritability. Nat Neurosci 22, 307–316 (2019). https://doi.org/10.1038/s41593-018-0297-8

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