Article

Dysregulation of the epigenetic landscape of normal aging in Alzheimer’s disease

  • Nature Neurosciencevolume 21pages497505 (2018)
  • doi:10.1038/s41593-018-0101-9
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Abstract

Aging is the strongest risk factor for Alzheimer’s disease (AD), although the underlying mechanisms remain unclear. The chromatin state, in particular through the mark H4K16ac, has been implicated in aging and thus may play a pivotal role in age-associated neurodegeneration. Here we compare the genome-wide enrichment of H4K16ac in the lateral temporal lobe of AD individuals against both younger and elderly cognitively normal controls. We found that while normal aging leads to H4K16ac enrichment, AD entails dramatic losses of H4K16ac in the proximity of genes linked to aging and AD. Our analysis highlights the presence of three classes of AD-related changes with distinctive functional roles. Furthermore, we discovered an association between the genomic locations of significant H4K16ac changes with genetic variants identified in prior AD genome-wide association studies and with expression quantitative trait loci. Our results establish the basis for an epigenetic link between aging and AD.

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Change history

  • Correction 19 March 2018

    In the version of this article initially published online, the fifth author’s name was given as Alexander Amlie-Wolf. The correct name is Alexandre Amlie-Wolf. The error has been corrected in the print, PDF and HTML versions of this article.

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Acknowledgements

We thank T. Schuck and G. Gibbons for assistance with brain tissue selection and retrieval; the Penn Flow Cytometry core for technical assistance; P. Sen and M. Sammons for edits and comments to the manuscript and P. Ortega for insightful scientific discussions. This work was funded by NIH grant R01-NS078283 (to S.L.B., N.M.B. and F.B.J.). J.Q.T. is supported by AG10124 and AG17586.

Author information

Affiliations

  1. Epigenetics Program, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    • Raffaella Nativio
    • , Greg Donahue
    • , Yemin Lan
    •  & Shelley L. Berger
  2. Department of Biology, University of Pennsylvania, Philadelphia, PA, USA

    • Amit Berson
    • , Sager J. Gosai
    • , Brian D. Gregory
    •  & Nancy M. Bonini
  3. Department of Pathology & Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA

    • Alexandre Amlie-Wolf
    • , Jon B. Toledo
    • , John Q. Trojanowski
    • , Li-San Wang
    •  & F. Brad Johnson
  4. Department of Pathology, Drexel University College of Medicine, Philadelphia, PA, USA

    • Ferit Tuzer
    •  & Claudio Torres

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Contributions

R.N., F.B.J., N.M.B. and S.L.B. conceived the project. R.N. performed most of the experiments. R.N., G.D., A.B., Y.L., A.A.W., F.T., J.B.T., S.J.G., B.D.G., C.T., J.Q.T., L.S.W., F.B.J., N.M.B. and S.L.B. contributed to methodology and resources. R.N., G.D. and Y.L. analyzed ChIP-seq and RNA-seq data. G.D. performed the AD SNP enrichment analysis. A.A.W. performed the AD eQTL enrichment analysis. F.T. performed IF staining and analysis. R.N., F.B.J., N.M.B. and S.L.B. wrote the manuscript. All authors reviewed the manuscript and discussed the work.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to F. Brad Johnson or Nancy M. Bonini or Shelley L. Berger.

Integrated supplementary information

  1. Supplementary Figure 1 Quantification of neurons in temporal cortex of Young, Old and AD subjects.

    (A) Representative IF images showing total nuclei (DAPI-blue, left column), NeuN positive nuclei (green, central column) and merged images (right column) for Young (top row), Old (middle row), AD (bottom row). (B) Barplot (with data points) representing mean ± SEM of neuron percentages quantified by IF (no Young = 9; no Old = 10; no AD = 12). Sample size (no) represents individual brain samples. Differences are not significant (P=0.087, 1-way ANOVA; F (2,28)=2.669).

  2. Supplementary Figure 2 H4K16ac enrichment in individual and pooled samples.

    UCSC Genome browser track views of H4K16ac enrichment at the promoter of SLC35D1 gene in individual Young (blue), Old (orange), AD (gray) samples and corresponding pooled samples. The numbers on the right of each track refer to the sample ID as reported in Table S1.

  3. Supplementary Figure 3 Comparison between H4K16ac peaks detected in the brain and other cell types.

    (A) Genomic compartment analysis showing % of H4K16ac shared peaks (in common between Young, Old and AD) is similar to Taylor et al. [20] in mouse ESC and NPC cells. (B) Heatmap showing H4K16ac enrichment in Young, Old and AD over previously mapped H4K16ac peaks in human IMR90 cells [15]. Peaks are sorted in decreasing order of Young brain enrichment. (C). Barplot showing % of tissue-specific expressed genes targeted by H4K16ac (top 10% of Young peaks and up to 1 kb from TSS) for H4K16ac shared peaks (UniProt tissue expression database, DAVID). Tissue terms were filtered by gene count > 200 and FDR < 1%.

  4. Supplementary Figure 4 Correlation between H4K16ac and gene expression in Young, Old and AD subjects.

    (a-c) Scatter plot of H4K16ac peak enrichment (log2 AUC +1) vs gene expression (log2 DESeq-score +1) of genes expressed in (a) Young, (b) Old and (C) AD considering the closest peak from the TSS. For graphical representation, 3000 randomly chosen points are shown in each panel. (d-f) Absolute H4K16ac fold-changes (closest peak from the TSS) vs absolute gene expression changes for the significantly (P < 0.05, FDR< 0.05) differentially expressed genes for the comparisons (d) Young to Old, (e) Old to AD and (f) Young to AD. Linear regression trendlines and Pearson correlation coefficients are indicated.

  5. Supplementary Figure 5 Comparison between RNA-seq and published microarray data (Blalock et al. 2011) in Old and AD subjects.

    Boxplot showing RNA-seq gene expression (DEseq-scores) in Old (a) or AD (b) for genes identified in a published AD microarray study [21] from human hippocampus. All expressed genes from the published microarray data (control Old and AD) were sorted into ten groups of increasing expression and compared to Old (a) or AD (b) in our RNA-seq data.

  6. Supplementary Figure 6 GO analysis of genes with H4K16ac changes in aging and AD subjects.

    (A-D) Bar plot for top eight most significantly enriched GO terms (BP; DAVID) for genes with significant (P < 0.05, Welch's t-test, two-sided) H4K16ac (A) gains or (B) losses during aging (Young-Old comparison) and H4K16ac (C) gains or (D) losses in AD (Old-AD comparison). Reported GO terms contain > 20 genes and FDR < 10%.

  7. Supplementary Figure 7 Correlation across patients at the three classes of H4K16ac changes.

    Dendrogram of Spearman Rank Correlation across patients (rep1 and rep2) for the three classes of peaks (Age-regulate, Age-dysregulated and Disease-specific; P < 0.05, 1-way Anova) showing patients clustering according to study group (Young, Old and AD). The numbers for each patient refer to the sample ID as reported in Table S1.

  8. Supplementary Figure 8 H4K16ac overlap analysis with tissue eQTLs from the GTEx project.

    Heatmap of Bonferroni adjusted p-values for sampling-based analysis of H4K16ac overlap (the three classes of changes) with tissue eQTLs from the GTEx project.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–8

  2. Life Sciences Reporting Summary(PDF 84 kb)

  3. Supplementary Table 1 - Tissue donor information

    Tissue donors were categorized by gender, age at death, postmortem interval (PMI), Cerad scores and Braak stages, RNA integrity number (RIN) of RNA extracted from the same brain region, neuronal fractions (measured by flow cytometry) and cause of death. Four control brains had a small degree of neurofibrillary tangles (Braak I/II) limited to the medial temporal lobe; low level pathology is commonly present in aged subjects and is not associated with cognitive changes in the absence of other brain pathologies (Montine et al., Acta Neuropathol, 2012. 123(1): p. 1-11).

  4. Supplementary Table 2 - Sequencing and alignment statistics for H4K16ac ChIP-seq experiment

    For each H4K16ac ChIP-seq sample the number of total tags, uniquely aligned tags and % uniquely aligned tags are reported.

  5. Supplementary Table 3 - Sequencing and alignment statistics for RNA-seq experiment

    For each RNA-seq sample the number of total tags, uniquely aligned tags and % uniquely aligned tags are reported. No RNA-seq is available for sample 1 as there was no tissue left for this experiment.

  6. Supplementary Table 4

    Functional analysis of genes with significant H4K16ac changes (P < 0.05, Welch's t-test, two-sided) in the 2-way comparison (Aging and Disease).

  7. Supplementary Table 5

    Functional analysis of genes with significant H4K16ac changes (P < 0.05, 1-way Anova) in the three-way comparison (Age-regulated, Age-dysregulated, Disease-specific).

  8. Supplementary Table 6

    Supplementary Table 6.