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An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer’s disease

An Author Correction to this article was published on 08 October 2020

This article has been updated

Abstract

Protein aggregation is the hallmark of neurodegeneration, but the molecular mechanisms underlying late-onset Alzheimer’s disease (AD) are unclear. Here we integrated transcriptomic, proteomic and epigenomic analyses of postmortem human brains to identify molecular pathways involved in AD. RNA sequencing analysis revealed upregulation of transcription- and chromatin-related genes, including the histone acetyltransferases for H3K27ac and H3K9ac. An unbiased proteomic screening singled out H3K27ac and H3K9ac as the main enrichments specific to AD. In turn, epigenomic profiling revealed gains in the histone H3 modifications H3K27ac and H3K9ac linked to transcription, chromatin and disease pathways in AD. Increasing genome-wide H3K27ac and H3K9ac in a fly model of AD exacerbated amyloid-β42-driven neurodegeneration. Together, these findings suggest that AD involves a reconfiguration of the epigenome, wherein H3K27ac and H3K9ac affect disease pathways by dysregulating transcription- and chromatin–gene feedback loops. The identification of this process highlights potential epigenetic strategies for early-stage disease treatment.

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Fig. 1: Transcriptomic analysis identifies upregulation of transcription- and chromatin-related genes in AD.
Fig. 2: Mass spectrometry analysis identifies histone acetylation and methylation changes in aging and AD.
Fig. 3: H3K27ac and H3K9ac disease-specific gains and H3K122ac disease-specific losses in AD.
Fig. 4: H3K27ac and H3K9ac disease-specific gains are associated with epigenetic- and disease-related pathways in AD.
Fig. 5: Increased H3K27 and H3K9 acetylation enhances Aβ42 toxicity in Drosophila.

Data availability

The epigenomic and transcriptomic data that support the findings of this study are available through the National Center for Biotechnology Information Gene Expression Omnibus repository under accession no. GSE153875. Part of the input libraries were previously generated18 and are available under accession no. GSE84618. The proteomic data are available through the repository Chorus under accession no. 1684.

Code availability

The code developed for the analyses performed in this study is available at https://github.com/yeminlan/ADEpigenetics.

Change history

  • 08 October 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

We thank A. Shilatifard for the histone H3.3WT and the histone mutants H3.3K27M and H3.3K9M. We thank members of the Berger laboratory for comments on the data analyses and P. Ortega for insightful scientific discussions. This work was supported by the Kleberg Foundation (S.L.B. and N.M.B.), a National Institutes of Health (NIH)/National Institute on Aging grant no. P01-AG031862 (S.L.B.) and an NIH/National Institute of Neurological Disorders and Stroke grant no. R35-NS097275 (N.M.B.). C.H. is supported by grant no. R01-HG006827. C.H. is a Howard Hughes Medical Institute Investigator. L.W. is supported by grant nos. U24-AG041689 (NIAGADS) and U54-AG052427 (Global Alliance for Chronic Diseases). A.A.-W. is supported by training grant no. T32-AG00255. B.A.G. is supported by NIH grant nos. R01-NS111997 and AI118891. J.Q.T. is supported by grant no. AG10124.

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R.N., N.M.B. and S.L.B. conceived the project. R.N. performed the ChIP–seq, RNA-seq and mass spectrometry experiments and supervised most of the analyses. Y.L. performed the ChIP–seq and RNA-seq analyses. G.D. performed the comparisons with published RNA-seq data and AD SNP enrichment analysis. S.S. performed the mass spectrometry and STRING analysis. A.B., A.R.S. and O.S. performed the fly experiments. R.N. extracted genomic DNA and J.N. performed 5hmC-Seal. X.C. processed the 5hmC data. A.A.-W. performed the AD eQTL enrichment analysis. C.H., L.W., B.A.G., J.Q.T., N.M.B. and S.L.B. contributed to the methodology and resources. R.N., N.M.B. and S.L.B. wrote the manuscript. All authors reviewed the manuscript and discussed the work.

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Correspondence to Nancy M. Bonini or Shelley L. Berger.

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Competing interests

C.H. holds a patent on the technology used (no. US8741567) and is a shareholder in Shanghai Epican Genetech.

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Extended data

Extended Data Fig. 1 STRING network analysis for genes changing in AD.

a, Barplot showing the number of STRING (v11) interactions for genes with the top number of interactions in Fig. 1e. b, STRING interaction network for genes changing in AD vs Old (q < 0.05) that interact with EP300, CREBBP and TRAPP. Interactions that were identified in Fig. 1e are not shown in this network. The gene network was visualized with Cytoscape (v3.6)104. Size of nodes represents RNA expression values, the color represents gene expression changes (log2 fold-change) in the AD vs Old comparison (red for upregulated in AD; blue for downregulated in AD) and the thickness of the line is the confidence of the interaction calculated by STRING. Nodes circled in red or blue represent known transcription and chromatin genes.

Extended Data Fig. 2 Histone posttranslational modifications in Younger, Old and AD.

a, Amino acid sequence of canonical histone H3 (H3.1 and H3.2) tail and globular domain, and its H3.3 variant. The residue that differs between canonical H3 and H3.3 is highlighted in red. b, Amino acid sequence of histone H4 tail and globular domain. Bars below the amino acid sequence in panels a-b represent peptides generated in the trypsinization process that were identified on the mass spectrometer (LC-MS/MS). Grey bars represent peptides not reliably detected and therefore excluded from the analysis. ce, Stacked bar plots showing relative abundance of histone modifications (methylation and acetylation) on histones H3, H3.3 and H4 in (c) Younger, (d) Old and (e) AD. The lysine residues (K) analyzed are listed below the stacked bar plots.

Extended Data Fig. 3 Histone acetyl marks are enriched at both TSS and enhancers.

Metaplots showing peak enrichment of H3K27ac, H3K9ac and H3K122ac and corresponding 5hmC and H3K4me1 enrichments for peaks at transcriptional start sites (TSSs) (≤ 1 Kb from TSS) and enhancer (Enh) sites (> 1Kb from TSS) in (af) Younger, (g-l) Old and (m-r) AD brains. Histone acetyl-peaks are enriched at both TSSs and enhancers, while 5hmC and H3K4me1 mark enhancer sites.

Extended Data Fig. 4 H3K27ac, H3K9ac and H3K122ac peak distribution in Younger, Old and AD.

a, Histogram of peak density for H3K27ac (light green), H3K9ac (light blue) and H3K122ac (light red), based on their distance from the transcriptional start site (TSS) for peaks detected in Younger, Old and AD. Grey vertical lines demark (from left to right): 5, 25, 50 and 100 Kb distance from TSS. bd, Venn Diagram showing the overlap between H3K27ac, H3K9ac and H3K122ac peaks for (b) All peaks, (c) TSS peaks (≤ 1Kb from TSS) and (d) enhancer (Enh) peaks (> 1Kb from TSS) detected in Younger, Old and AD.

Extended Data Fig. 5 Correlation between ChIP-seq and RNA-seq data.

a-c, Scatterplot of (a) H3K27ac, (b) H3K9ac and (c) H3K122ac peak enrichment vs gene expression for genes expressed in Old. d-f, Scatterplot of (d) H3K27ac, (e) H3K9ac and (f) H3K122ac peak enrichment vs gene expression for genes expressed in AD. For graphical representation in a-b, 3000 randomly chosen points are shown in each panel. g-i, Scatterplot of (g) H3K27ac, (h) H3K9ac and (i) H3K122ac absolute peak fold-change vs absolute gene expression change for significantly (q < 0.05) differentially expressed genes in AD vs Old. j,k, Scatterplot of total acetyl-peak enrichment (H3K-total-ac; sum of H3K27ac, H3K9ac and H3K122ac peak enrichment at the same site) vs gene expression for genes expressed in (j) Old and (k) AD. l, Scatterplot of H3K-total-ac absolute peak fold-change vs absolute gene expression change for significantly (q < 0.05) differentially expressed genes in AD vs Old. The closest peak to the TSS was chosen for these analyses. Linear regression trendlines, Pearson’s correlation coefficients and p-values (test for association using Pearson’s product moment correlation coefficient implemented by R stats package, two-sided) are indicated in each panel (a-l).

Extended Data Fig. 6 Comparison between histone marks enrichments at sites with disease-specific changes.

ac, Boxplots showing H3K27ac, H3K9ac, H3K122ac and H3K4me1 peak enrichment at sites with (a) H3K27ac, (b) H3K9ac, (c) H3K122ac (highlighted in blue) disease-specific gains. df, Boxplots showing H3K9ac, H3K122ac and H3K4me1 peak enrichment at sites with (d) H3K27ac, (e) H3K9ac and (f) H3K122ac (highlighted in blue) disease-specific losses. Asterisks in (a-f) denote level of significance comparing peak enrichment across Younger (N = 11-12), Old (N = 10) and AD (N = 9–11) (* P < 0.05; ** P < 0.01, 1-way ANOVA) (Supplementary Table 2). Boxplots show minimum, first quartile, median (center line), third quartile and maximum.

Extended Data Fig. 7 H3K9ac disease-specific gain at CREBBP but not EP300.

ac, Boxplot showing (a) H3K9ac, (b) H3K27ac and (c) H3K122ac peak enrichment at the CREBBP gene in Younger, Old and AD. A H3K9ac disease-specific gain is observed at CREBBP (highlighted in blue in a). df, Boxplot showing (d) H3K9ac, (e) H3K27ac (f) H3K122ac peak enrichment at the EP300 gene in Younger, Old and AD showing no disease-specific changes. The closest peak to the gene was considered for this analysis. P-values comparing peak enrichment across Younger (N = 8-9), Old (N = 10) and AD (N = 9–11) (Supplementary Table 2) (1-way ANOVA) are reported in each panel. Boxplots show minimum, first quartile, median (center line), third quartile and maximum. Dots overlaid on boxplots represent individual data points.

Extended Data Fig. 8 Functional analysis of H3K27ac and H3K9ac disease-specific losses.

a,b, Barplot showing top GO terms (Biological Processes; GREAT, FDR < 5%, % by both the binomial and the hypergeometric tests) for (a) H3K27ac disease-specific losses and (b) H3K9ac disease-specific losses for terms with at least 20 genes. c,d, UCSC genome browser view showing an example of (c) H3K27ac disease-specific loss at the PCSK1 gene and (d) H3K9ac disease-specific loss at the SVOP gene. H3K27ac, H3K9ac, H3K122ac, H3K4me1 ChIP-seq and RNA-seq tracks are showed for Younger, Old and AD. e,f, Top DNA motifs (HOMER v4.6) for (e) H3K27ac disease-specific losses and (f) H3K9ac disease-specific losses in AD. Enrichment results are shown for known motifs (q < 0.05, Benjamini-Hochberg).

Extended Data Fig. 9 Functional analysis of disease-specific changes using DAVID.

a,b, Barplot showing top GO terms (Biological Processes, DAVID v6.7, FDR < 10%, Yekutieli) for genes targeted by (a) disease-specific gains (H3K27ac or H3K9ac) and (b) disease-specific losses (either H3K27 or H3K9ac or H3K122ac) for terms with at least 20 genes.

Extended Data Fig. 10 H3K27ac disease-specific gains are enriched with AD GWAS SNPs from Kunkle et al.

Bar plot showing the significance (-log10 p-value) of the association between each of the six classes of H3K27ac, H3K9ac and H3K122ac changes (age-regulated gains or losses, age-dysregulated gains or losses and disease-specific gains or losses) and AD SNP-regions from Kunkle et al.85 using INRICH. Red dashed horizontal line represents the threshold of significance (P < 0.05).

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Supplementary Notes 1–8 and Figs. 1–11

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Supplementary Tables 1–14

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Nativio, R., Lan, Y., Donahue, G. et al. An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer’s disease. Nat Genet 52, 1024–1035 (2020). https://doi.org/10.1038/s41588-020-0696-0

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