Abstract

Accumulation of tau and amyloid-β are two pathologic hallmarks of Alzheimer’s disease. We conducted an epigenome-wide association study using the histone 3 lysine 9 acetylation (H3K9ac) mark in 669 aged human prefrontal cortices; in contrast with amyloid-β, tau protein burden had a broad effect on the epigenome, affecting 5,990 of 26,384 H3K9ac domains. Tau-related alterations aggregated in large genomic segments reflecting spatial chromatin organization, and the magnitude of these effects correlated with the segment’s nuclear lamina association. Functional relevance of these chromatin changes was demonstrated by (1) consistent transcriptional changes in three independent datasets and (2) similar findings in two mouse models of Alzheimer’s disease. Finally, we found that tau overexpression in induced pluripotent stem cell-derived neurons altered chromatin structure and that these effects could be blocked by a small molecule predicted to reverse the tau effect. Thus, we report broad tau-driven chromatin rearrangements in the aging human brain that may be reversible with heat-shock protein 90 (Hsp90) inhibitors.

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Data availability

Human H3K9ac ChIP-seq data has been deposited at Synapse (Synapse: syn4896408). Mouse H3K9ac ChIP-seq data (GEO GSE97560) and ATAC-seq data from iNs (GEO GSE97409) have been deposited at Gene Expression Omnibus. Phenotype variables for the ROS/MAP studies can be requested from the RADC Research Sharing Hub (https://www.radc.rush.edu).

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References

  1. 1.

    Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013).

  2. 2.

    Klein, H. U., Bennett, D. A. & De Jager, P. L. The epigenome in Alzheimer’s disease: current state and approaches for a new path to gene discovery and understanding disease mechanism. Acta Neuropathol. 132, 503–514 (2016).

  3. 3.

    Lardenoije, R. et al. The epigenetics of aging and neurodegeneration. Prog. Neurobiol. 131, 21–64 (2015).

  4. 4.

    Nativio, R. et al. Dysregulation of the epigenetic landscape of normal aging in Alzheimer’s disease. Nat. Neurosci. 21, 497–505 (2018).

  5. 5.

    De Jager, P. L. et al. Alzheimer’s disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat. Neurosci. 17, 1156–1163 (2014).

  6. 6.

    Lunnon, K. et al. Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer’s disease. Nat. Neurosci. 17, 1164–1170 (2014).

  7. 7.

    Frost, B., Bardai, F. H. & Feany, M. B. Lamin dysfunction mediates neurodegeneration in Tauopathies. Curr. Biol. 26, 129–136 (2016).

  8. 8.

    Frost, B., Hemberg, M., Lewis, J. & Feany, M. B. Tau promotes neurodegeneration through global chromatin relaxation. Nat. Neurosci. 17, 357–366 (2014).

  9. 9.

    Mansuroglu, Z. et al. Loss of Tau protein affects the structure, transcription and repair of neuronal pericentromeric heterochromatin. Sci. Rep. 6, 33047 (2016).

  10. 10.

    Bennett, D. A., Schneider, J. A., Arvanitakis, Z. & Wilson, R. S. Overview and findings from the religious orders study. Curr. Alzheimer. Res. 9, 628–645 (2012).

  11. 11.

    Bennett, D. A. et al. Overview and findings from the rush Memory and Aging Project. Curr. Alzheimer. Res. 9, 646–663 (2012).

  12. 12.

    Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  13. 13.

    Gabriel, S. B. et al. The structure of haplotype blocks in the human genome. Science 296, 2225–2229 (2002).

  14. 14.

    Olshen, A. B., Venkatraman, E. S., Lucito, R. & Wigler, M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5, 557–572 (2004).

  15. 15.

    Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

  16. 16.

    Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).

  17. 17.

    Meuleman, W. et al. Constitutive nuclear lamina-genome interactions are highly conserved and associated with A/T-rich sequence. Genome Res. 23, 270–280 (2013).

  18. 18.

    Kind, J. et al. Genome-wide maps of nuclear lamina interactions in single human cells. Cell 163, 134–147 (2015).

  19. 19.

    Klein, H. U. & De Jager, P. L. Uncovering the role of the methylome in dementia and neurodegeneration. Trends Mol. Med. 22, 687–700 (2016).

  20. 20.

    Schoofs, T. et al. DNA methylation changes are a late event in acute promyelocytic leukemia and coincide with loss of transcription factor binding. Blood 121, 178–187 (2013).

  21. 21.

    Zou, F. et al. Brain expression genome-wide association study (eGWAS) identifies human disease-associated variants. PLoS Genet. 8, e1002707 (2012).

  22. 22.

    Zhong, Y., Wan, Y. W., Pang, K., Chow, L. M. & Liu, Z. Digital sorting of complex tissues for cell type-specific gene expression profiles. BMC Bioinformatics 14, 89 (2013).

  23. 23.

    Jaffe, A. E. & Irizarry, R. A. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome. Biol. 15, R31 (2014).

  24. 24.

    Liang, W. S. et al. Alzheimer’s disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proc. Natl Acad. Sci. USA. 105, 4441–4446 (2008).

  25. 25.

    Yoshiyama, Y. et al. Synapse loss and microglial activation precede tangles in a P301S tauopathy mouse model. Neuron 53, 337–351 (2007).

  26. 26.

    Cruz, J. C., Tseng, H. C., Goldman, J. A., Shih, H. & Tsai, L. H. Aberrant Cdk5 activation by p25 triggers pathological events leading to neurodegeneration and neurofibrillary tangles. Neuron 40, 471–483 (2003).

  27. 27.

    Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease. Nature 518, 365–369 (2015).

  28. 28.

    Muratore, C. R. et al. Cell-type dependent Alzheimer’s disease phenotypes: probing the biology of selective neuronal vulnerability. Stem Cell Rep. 9, 1868–1884 (2017).

  29. 29.

    Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

  30. 30.

    Lamb, J. et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

  31. 31.

    Dickey, C. A. et al. The high-affinity HSP90-CHIP complex recognizes and selectively degrades phosphorylated tau client proteins. J. Clin. Invest. 117, 648–658 (2007).

  32. 32.

    Luo, W. et al. Roles of heat-shock protein 90 in maintaining and facilitating the neurodegenerative phenotype in tauopathies. Proc. Natl Acad. Sci. USA. 104, 9511–9516 (2007).

  33. 33.

    Gao, L. et al. Discovery of the neuroprotective effects of alvespimycin by computational prioritization of potential anti-Parkinson agents. FEBS J. 281, 1110–1122 (2014).

  34. 34.

    Imakaev, M. et al. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat. Methods 9, 999–1003 (2012).

  35. 35.

    Dixon, J. R. et al. Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336 (2015).

  36. 36.

    Fortin, J. P. & Hansen, K. D. Reconstructing A/B compartments as revealed by Hi-C using long-range correlations in epigenetic data. Genome Biol. 16, 180 (2015).

  37. 37.

    Chang, K. H. et al. Nuclear envelope dispersion triggered by deregulated Cdk5 precedes neuronal death. Mol. Biol. Cell 22, 1452–1462 (2011).

  38. 38.

    Bennett, D. A., Schneider, J. A., Tang, Y., Arnold, S. E. & Wilson, R. S. The effect of social networks on the relation between Alzheimer’s disease pathology and level of cognitive function in old people: a longitudinal cohort study. Lancet Neurol. 5, 406–412 (2006).

  39. 39.

    Bennett, D. A., Schneider, J. A., Wilson, R. S., Bienias, J. L. & Arnold, S. E. Neurofibrillary tangles mediate the association of amyloid load with clinical Alzheimer disease and level of cognitive function. Arch. Neurol. 61, 378–384 (2004).

  40. 40.

    Mostafavi, S. et al. A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease. Nat. Neurosci. 21, 811–819 (2018).

  41. 41.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  42. 42.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome. Biol. 9, R137 (2008).

  43. 43.

    Landt, S. G. et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012).

  44. 44.

    Zeng, H. et al. Specification of region-specific neurons including forebrain glutamatergic neurons from human induced pluripotent stem cells. PLoS One 5, e11853 (2010).

  45. 45.

    Zhang, Y. et al. Rapid single-step induction of functional neurons from human pluripotent stem cells. Neuron 78, 785–798 (2013).

  46. 46.

    Wang, Q. et al. Tagmentation-based whole-genome bisulfite sequencing. Nat. Protoc. 8, 2022–2032 (2013).

  47. 47.

    Lun, A. T. & Smyth, G. K. De novo detection of differentially bound regions for ChIP-seq data using peaks and windows: controlling error rates correctly. Nucleic Acids Res. 42, e95 (2014).

  48. 48.

    Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

  49. 49.

    Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

  50. 50.

    Smyth, G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, e3 (2004).

  51. 51.

    Zhang, Y. et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37–53 (2016).

  52. 52.

    Hebestreit, K., Dugas, M. & Klein, H. U. Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics 29, 1647–1653 (2013).

  53. 53.

    Cribari-Neto, F. & Zeileis, A. Beta regression in R. J. Stat. Softw. 34, 1–24 (2010).

  54. 54.

    Schübeler, D. Function and information content of DNA methylation. Nature 517, 321–326 (2015).

  55. 55.

    Allen, M. et al. Human whole genome genotype and transcriptome data for Alzheimer’s and other neurodegenerative diseases. Sci. Data 3, 160089 (2016).

  56. 56.

    Irizarry, R. A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).

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Acknowledgements

This work has been supported by NIH grants U01 AG046152, R01 AG036836, RF1 AG015819, R01 AG017917, RC2 AG036547. L-H. T. has been supported by NIH/NINDS/NIA (R01 NS078839) and the Robert A. and Renee R. Belfer Family Foundation. The Mayo Clinic Alzheimer’s Disease Genetic Studies were led by N. Ertekin-Taner and S. G. Younkin, Mayo Clinic, Jacksonville, FL, using samples from the Mayo Clinic Study of Aging, the Mayo Clinic Alzheimer’s Disease Research Center and the Mayo Clinic Brain Bank. Data collection was supported through funding by NIA grants P50 AG016574, R01 AG032990, U01 AG046139, R01 AG018023, U01 AG006576, U01 AG006786, R01 AG025711, R01 AG017216, R01 AG003949, NINDS grant R01 NS080820, CurePSP Foundation and support from Mayo Foundation.

Author information

Author notes

  1. These authors contributed equally: David A. Bennett, Philip L. De Jager.

Affiliations

  1. Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA

    • Hans-Ulrich Klein
    •  & Philip L. De Jager
  2. Broad Institute, Cambridge, MA, USA

    • Hans-Ulrich Klein
    • , Cristin McCabe
    • , Elizabeta Gjoneska
    • , Anna Tang
    • , Jishu Xu
    • , Li-Huei Tsai
    • , Tracy L. Young-Pearse
    •  & Philip L. De Jager
  3. Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA

    • Elizabeta Gjoneska
    •  & Li-Huei Tsai
  4. Department of Neurology, Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    • Sarah E. Sullivan
    • , Belinda J. Kaskow
    • , Robert V. Smith
    •  & Tracy L. Young-Pearse
  5. Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA

    • Andreas R. Pfenning
  6. Department of Pathology, Massachusetts General Hospital, Boston, MA, USA

    • Bradley E. Bernstein
  7. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

    • Alexander Meissner
  8. Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA

    • Julie A. Schneider
    •  & David A. Bennett
  9. Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada

    • Sara Mostafavi
  10. Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada

    • Sara Mostafavi
  11. Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada

    • Sara Mostafavi

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Contributions

P.L.D., D.A.B., T.L.Y-P., A.M. and B.E.B. conceived the study. C.M., E.G., A.T., S.E.S., B.J.K., A.T. and R.V.S. conducted experiments. J.A.S. and D.A.B. and contributed post mortem brain tissues. E.G. and L.-H.T. contributed mouse models. S.E.S. and T.L.Y-P. contributed neuronal models. H-U.K., J.X. A.R.P. and P.L.D. analyzed data. H-U.K., E.G., S.M., T.L.Y-P., D.A.B. and P.L.D. interpreted data and designed follow-up experiments. H.-U.K. and P.L.D. wrote the manuscript with contributions from all co-authors.

Competing interests

B.E.B. owns equity in Fulcrum Therapeutics, 1CellBio Inc., Nohla Therapeutics and HiFiBio Inc., and is an advisor for Fulcrum Therapeutics, HiFiBio Inc. and Cell Signaling Technologies. The other authors declare no competing interests.

Corresponding author

Correspondence to Philip L. De Jager.

Integrated supplementary information

  1. Supplementary Figure 1. H3K9ac in the human cortex occurs primarily at active promoters and enhancers and is associated with transcription.

    a, Bars depict the fraction of each chromatin state overlapped by H3K9ac domains. Chromatin states were obtained from a DLPFC sample with minimal neuropathology (E073) included in the Roadmap Epigenomics Project. b, Bars show the proportions of chromatin states for each cluster centroid obtained by clustering H3K9ac domains based on chromatin state annotation. c, Boxplot shows the number of reads observed within the 26,384 H3K9ac domains standardized by sequencing depth and domain width (median log2 rpkm values of n=669 subjects). d, Boxplot shows the width of H3K9ac domains. The center line of the boxplot shows the median, the outer edges correspond to the lower and upper quartiles, and the whiskers extend to the most extreme observed value within 1.5×IQR from the edges. e,f, Estimated effect sizes for tau (e) and for amyloid-β (f) on H3K9ac are shown separately for promoter, enhancer and other domains. Significant effect sizes (two-sided Wald test, FDR threshold of 0.05, n=669 subjects) are colored red. g,h, Boxplots show the effect size of tau (g) and amyloid-β (h) in RNA-seq data for transcripts whose H3K9ac levels were associated (p ≤ 10-4) with tau (n=2,492 transcripts) (g) or amyloid-β (n=47 transcripts) (h) respectively. Only active transcripts with a H3K9ac domain in their promoters were considered (18,257 transcripts). The center line of the boxplot shows the median, the outer edges correspond to the lower and upper quartiles, and the whiskers extend to the most extreme observed value within 1.5×IQR from the edges. i, Density plot depicts the relation between promoter H3K9ac and transcription. For each RefSeq transcript, the median standardized read count from the RNA-seq data (n=500 subjects) is plotted against median standardized read count in the respective promoter (±2 kb around transcriptional start site) from the H3K9ac ChIP-seq data (n=669 subjects). Spearman correlation is depicted in the plot

  2. Supplementary Figure 2. Tau-related but not amyloid-β-related chromatin alterations show spatial patterns in the human cortex and in induced neurons.

    a,b, Manhattan plots depict unadjusted log-transformed p-values for tau coefficients (a) and amyloid-β coefficients (b) of 26,384 H3K9ac domains in the human prefrontal cortex (two-sided Wald test, n=669 subjects). Transformed p-values were plotted with the sign of the respective coefficient to distinguish between positive and negative associations. The red dashed lines indicate the significant threshold at an FDR of 0.05. For tau (a), broad genomic segments covering H3K9ac domains showing similar associations were observed and depicted by orange line segments. c, Manhattan plot depicts the log-transformed p-values for differences between MAPT overexpressing neurons and controls of 40,637 ATAC domains (two-sided t-test, n=18 independent experiments). Transformed p-values were plotted with the sign of the test statistic to distinguish between increased and decreased chromatin accessibility. Red dashed lines indicate the significance threshold at an FDR of 0.05. Broad genomic segments covering ATAC domains showing similar changes in chromatin accessibility are depicted as purple line segments

  3. Supplementary Figure 3. Broad tau-related H3K9ac alterations in human cortical tissue are not primarily driven by changes in cell-type proportions.

    a-f, Density plots show on the x-axis the effect sizes for tau obtained from the model used for the main analysis (“unadjusted model”) applied to a subset of n=452 H3K9ac subjects where RNA-seq data was available. On the y-axis, tau effect sizes were adjusted by adding the estimated proportion of neurons (a), astrocytes (b), oligodendrocytes (c), myeloid cells (d), endothelial cells (e), or the proportions of all five cell type (f) as covariates to the regression model. Pearson correlation between effect sizes from the unadjusted model and from any adjusted model was ≥ 0.99. H3K9ac domains that were significant (FDR ≤ 0.05) in an adjusted model but not significant (FDR > 0.1) in the unadjusted model are shown as green dots. None of the H3K9ac domains that were significant in the unadjusted model lost significance in any adjusted model

  4. Supplementary Figure 4. ATAC domains are primarily located at TSS, TSS-flanking regions, and enhancer regions and are reproducibly altered by MAPT overexpression in induced neurons.

    a, Bars show the proportions of chromatin states for each cluster centroid obtained by clustering ATAC domains based on chromatin state annotation. Chromatin states were obtained from H9-derived cultured neurons (E010) included in the Roadmap Epigenomics Project. b, Boxplot shows the number of reads observed within the n=40,637 ATAC domains standardized by sequencing depth and domain width (median log2 rpkm values of n=18 independent experiments). c, Boxplot shows the width of ATAC domains. The center line of the boxplot shows the median, the outer edges correspond to the lower and upper quartiles, and the whiskers extend to the most extreme observed value within 1.5×IQR from the edges. d, Dendrogram depicts results obtained from hierarchical clustering (Euclidian distance, average linkage) of the MAPT OE and control iNs generated in three batches. Read counts in ATAC domains were normalized for different library sizes before clustering. e-g, Scatterplot of the segments' mean tau-related changes observed in batch 1 versus batch 2 (e), batch 1 versus batch 3 (f), and batch 2 versus batch 3 (g). h-j, Scatterplot shows association between the proportion of LADs in the segments on the x-axis and and the segments' mean tau-related changes on the y-axis for batch 1 (h), batch 2 (i), and batch 3 (j). Pearson correlation weighted by the number of ATAC domains in each segment is shown in the plots e-j. Each batch consisted of n=6 independent experiments. k, Points depict the segmentation scores of the MAPT overexpressing iNs (n=9 independent experiments) and the control iNs (n=9 independent experiments). Red diamonds indicate group means. A two-sided t-test was applied to assess statistical significance. The segmentation score quantifies whether the tau-related chromatin changes observed in the human brain H3K9ac data occur in a given sample, and is calculated as described in the Methods

Supplementary information

  1. Supplementary Figures 1–4

    Supplementary Figs. 1–4 and Supplementary Tables 4, 7–9, 12, 13

  2. Reporting Summary

  3. Supplementary Table 1

    Quality control metrics for the 669 H3K9ac ChIP-seq samples from human DLPFC.

  4. Supplementary Table 2

    Locations of the 26,384 H3K9ac domains detected in the human DLPFC ChIP-seq samples.

  5. Supplementary Table 3

    Table of H3K9ac domains significantly associated with tau (or amyloid-β) at an FDR of 0.05; m = 26,384 H3k9ac domains were tested in n = 669 subjects using two-sided Wald tests.

  6. Supplementary Table 5

    Table of transcripts that were significantly associated with tau (amyloid-β) at an FDR of 0.05 in both H3K9ac data and transcription data. m =26,384 H3k9ac domains were tested in n = 669 subjects using two-sided Wald tests. m = 18,257 active transcripts were tested in n = 500 subjects using two-sided t tests. Only transcripts with an H3K9ac domain at the TSS were considered.

  7. Supplementary Table 6

    List of 178 segments derived from the H3K9ac data annotated with nuclear lamina association and chromatin accessibility. This table includes the average tau effects in the segments observed in H3K9ac data, RNA-seq data, DNA methylation data, Mayo LOAD study transcription data, neuronal transcription data, ATAC-seq data from iNs, and H3K9ac data from iNs depicted in Figs. 2e and 4i.

  8. Supplementary Table 10

    List of 99 segments derived from the ATAC-seq data annotated with the nuclear lamina association and chromatin accessibility. This table includes the average tau effects in the segments observed in the ATAC-seq data from MAPT OE iNs and control iNs.

  9. Supplementary Table 11

    Results from screening the Connectivity Map for drugs candidates using the H3K9ac signature for tau and for amyloid-β. H3K9ac domains were mapped to genes and Spearman correlations between standardized tau (amyloid-β) effects and the differential transcription signatures of the Connectivity map were calculated. Enrichment scores and permutation-based P values from the Connectivity Map search algorithm are given in columns “Enrichment” and “P”. Column “n” refers to the number of profiles for that perturbagen in the Connectivity Map data base. The Connectivity Map can be accessed at https://portals.broadinstitute.org/cmap.

  10. Supplementary Table 14

    Quality control metrics for the H3K9ac ChIP-seq samples from the tau mouse model (n = 11 mice) and from the CK-p25 mouse model (n = 12 mice).

  11. Supplementary Table 15

    Quality control metrics for the ATAC-seq (n = 36) and H3K9ac ChIP-seq experiments (n = 3) with induced neurons.

  12. Supplementary Software

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https://doi.org/10.1038/s41593-018-0291-1