A histone acetylome-wide association study of Alzheimer’s disease identifies disease-associated H3K27ac differences in the entorhinal cortex

Abstract

We quantified genome-wide patterns of lysine H3K27 acetylation (H3K27ac) in entorhinal cortex samples from Alzheimer’s disease (AD) cases and matched controls using chromatin immunoprecipitation and highly parallel sequencing. We observed widespread acetylomic variation associated with AD neuropathology, identifying 4,162 differential peaks (false discovery rate < 0.05) between AD cases and controls. Differentially acetylated peaks were enriched in disease-related biological pathways and included regions annotated to genes involved in the progression of amyloid-β and tau pathology (for example, APP, PSEN1, PSEN2, and MAPT), as well as regions containing variants associated with sporadic late-onset AD. Partitioned heritability analysis highlighted a highly significant enrichment of AD risk variants in entorhinal cortex H3K27ac peak regions. AD-associated variable H3K27ac was associated with transcriptional variation at proximal genes including CR1, GPR22, KMO, PIM3, PSEN1, and RGCC. In addition to identifying molecular pathways associated with AD neuropathology, we present a framework for genome-wide studies of histone modifications in complex disease.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Variable H3K27ac associated with AD in the entorhinal cortex.
Fig. 2: The top-ranked AD-associated hyperacetylated peak is annotated to SOX1 and TEX29 on chromosome 13.
Fig. 3: The top-ranked AD-associated hypoacetylated peak is located in intron 1 of ZNF680 on chromosome 7.
Fig. 4: Clustering of AD and low-pathology control samples by H3K27ac levels at differentially acetylated peaks.
Fig. 5: A region annotated to MAPT spanning six H3K27ac peaks is characterized by significant hyperacetylation in AD.
Fig. 6: A region annotated to PSEN2 spanning nine H3K27ac peaks is characterized by significant hyperacetylation in AD.
Fig. 7: AD-associated differential expression of transcripts annotated to differentially acetylated peaks.

Data availability

Raw data has been deposited in GEO under accession number GSE102538. Browsable UCSC genome browser tracks of our processed H3K27ac ChIP-seq data are available as a resource at: https://epigenetics.essex.ac.uk/AD_H3K27ac/.

References

  1. 1.

    Brookmeyer, R., Johnson, E., Ziegler-Graham, K. & Arrighi, H. M. Forecasting the global burden of Alzheimer’s disease. Alzheimers Dement. 3, 186–191 (2007).

    Article  Google Scholar 

  2. 2.

    Wenk, G. L. Neuropathologic changes in Alzheimer’s disease. J. Clin. Psychiatry 64(Suppl 9), 7–10 (2003).

    Google Scholar 

  3. 3.

    Hardy, J. & Selkoe, D. J. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 297, 353–356 (2002).

    CAS  Article  Google Scholar 

  4. 4.

    Karch, C. M., Cruchaga, C. & Goate, A. M. Alzheimer’s disease genetics: from the bench to the clinic. Neuron 83, 11–26 (2014).

    CAS  Article  Google Scholar 

  5. 5.

    Reitz, C., Brayne, C. & Mayeux, R. Epidemiology of Alzheimer disease. Nat. Rev. Neurol. 7, 137–152 (2011).

    Article  Google Scholar 

  6. 6.

    Liu, C. C., Liu, C. C., Kanekiyo, T., Xu, H. & Bu, G. Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy. Nat. Rev. Neurol. 9, 106–118 (2013).

    CAS  Article  Google Scholar 

  7. 7.

    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).

    CAS  Article  Google Scholar 

  8. 8.

    Lunnon, K. & Mill, J. Epigenetic studies in Alzheimer’s disease: current findings, caveats, and considerations for future studies. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 162B, 789–799 (2013).

    Article  Google Scholar 

  9. 9.

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

    CAS  Article  Google Scholar 

  10. 10.

    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).

    Article  Google Scholar 

  11. 11.

    Creyghton, M. P. et al. Histone H3K27ac separates active from poised enhancers and predictsdevelopmental state. Proc. Natl. Acad. Sci. USA 107, 21931–21936 (2010).

    CAS  Article  Google Scholar 

  12. 12.

    Cuadrado-Tejedor, M. et al. A first-in-class small-molecule that acts as a dual inhibitor of HDAC and PDE5 and that rescues hippocampal synaptic impairment in Alzheimer’s disease mice. Neuropsychopharmacology 42, 524–539 (2017).

    CAS  Article  Google Scholar 

  13. 13.

    Fischer, A. Targeting histone-modifications in Alzheimer’s disease. What is the evidence that this is a promising therapeutic avenue? Neuropharmacology 80, 95–102 (2014).

    CAS  Article  Google Scholar 

  14. 14.

    Rao, J. S., Keleshian, V. L., Klein, S. & Rapoport, S. I. Epigenetic modifications in frontal cortex from Alzheimer’s disease and bipolar disorder patients. Transl. Psychiatry 2, e132 (2012).

    CAS  Article  Google Scholar 

  15. 15.

    Zhang, K. et al. Targeted proteomics for quantification of histone acetylation in Alzheimer’s disease. Proteomics. 12, 1261–1268 (2012).

    CAS  Article  Google Scholar 

  16. 16.

    Narayan, P. J., Lill, C., Faull, R., Curtis, M. A. & Dragunow, M. Increased acetyl and total histone levels in post-mortem Alzheimer’s disease brain. Neurobiol. Dis. 74, 281–294 (2015).

    CAS  Article  Google Scholar 

  17. 17.

    Sun, W. et al. Histone acetylome-wide association study of autism spectrum disorder. Cell 167, 1385–1397.e11 (2016).

    CAS  Article  Google Scholar 

  18. 18.

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

    CAS  Article  Google Scholar 

  19. 19.

    Hopperton, K. E., Mohammad, D., Trépanier, M. O., Giuliano, V. & Bazinet, R. P. Markers of microglia in post-mortem brain samples from patients with Alzheimer’s disease: a systematic review. Mol. Psychiatry 23, 177–198 (2018).

    CAS  Article  Google Scholar 

  20. 20.

    Kamphuis, W. et al. Glial fibrillary acidic protein isoform expression in plaque related astrogliosis in Alzheimer’s disease. Neurobiol. Aging. 35, 492–510 (2014).

    CAS  Article  Google Scholar 

  21. 21.

    Santpere, G., Nieto, M., Puig, B. & Ferrer, I. Abnormal Sp1 transcription factor expression in Alzheimer disease and tauopathies. Neurosci. Lett. 397, 30–34 (2006).

    CAS  Article  Google Scholar 

  22. 22.

    Citron, B. A., Dennis, J. S., Zeitlin, R. S. & Echeverria, V. Transcription factor Sp1 dysregulation in Alzheimer’s disease. J. Neurosci. Res. 86, 2499–2504 (2008).

    CAS  Article  Google Scholar 

  23. 23.

    Ittner, L. M. & Götz, J. Amyloid-β and tau--a toxic pas de deux in Alzheimer’s disease. Nat. Rev. Neurosci. 12, 65–72 (2011).

    CAS  Article  Google Scholar 

  24. 24.

    Spillantini, M. G. & Goedert, M. Tau pathology and neurodegeneration. Lancet. Neurol. 12, 609–622 (2013).

    CAS  Article  Google Scholar 

  25. 25.

    Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

    CAS  Article  Google Scholar 

  26. 26.

    Scheuner, D. et al. Secreted amyloid beta-protein similar to that in the senile plaques of Alzheimer’s disease is increased in vivo by the presenilin 1 and 2 and APP mutations linked to familial Alzheimer’s disease. Nat. Med. 2, 864–870 (1996).

    CAS  Article  Google Scholar 

  27. 27.

    Goate, A. et al. Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer’s disease. Nature 349, 704–706 (1991).

    CAS  Article  Google Scholar 

  28. 28.

    Cruchaga, C. et al. Rare variants in APP, PSEN1 and PSEN2 increase risk for AD in late-onset Alzheimer’s disease families. PLoS One 7, e31039 (2012).

    CAS  Article  Google Scholar 

  29. 29.

    De Strooper, B. et al. Deficiency of presenilin-1 inhibits the normal cleavage of amyloid precursor protein. Nature 391, 387–390 (1998).

    Article  Google Scholar 

  30. 30.

    Goate, A. & Hardy, J. Twenty years of Alzheimer’s disease-causing mutations. J. Neurochem. 120(Suppl 1), 3–8 (2012).

    CAS  Article  Google Scholar 

  31. 31.

    Crehan, H. et al. Complement receptor 1 (CR1) and Alzheimer’s disease. Immunobiology 217, 244–250 (2012).

    CAS  Article  Google Scholar 

  32. 32.

    Heppner, F. L., Ransohoff, R. M. & Becher, B. Immune attack: the role of inflammation in Alzheimer disease. Nat. Rev. Neurosci. 16, 358–372 (2015).

    CAS  Article  Google Scholar 

  33. 33.

    Villegas-Llerena, C., Phillips, A., Garcia-Reitboeck, P., Hardy, J. & Pocock, J. M. Microglial genes regulating neuroinflammation in the progression of Alzheimer’s disease. Curr. Opin. Neurobiol. 36, 74–81 (2016).

    CAS  Article  Google Scholar 

  34. 34.

    Jaeger, S. & Pietrzik, C. U. Functional role of lipoprotein receptors in Alzheimer’s disease. Curr. Alzheimer. Res. 5, 15–25 (2008).

    CAS  Article  Google Scholar 

  35. 35.

    Sun, X. et al. Hypoxia facilitates Alzheimer’s disease pathogenesis by up-regulating BACE1 geneexpression. Proc. Natl. Acad. Sci. USA 103, 18727–18732 (2006).

    CAS  Article  Google Scholar 

  36. 36.

    Zlokovic, B. V. Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders. Nat. Rev. Neurosci. 12, 723–738 (2011).

    CAS  Article  Google Scholar 

  37. 37.

    Warren, J. D., Rohrer, J. D. & Rossor, M. N. Clinical review. Frontotemporal dementia. Br. Med. J. 347, f4827 (2013).

    Article  Google Scholar 

  38. 38.

    Limon, A., Reyes-Ruiz, J. M. & Miledi, R. Loss of functional GABA(A) receptors in the Alzheimer diseased brain. Proc. Natl. Acad. Sci. USA 109, 10071–10076 (2012).

    CAS  Article  Google Scholar 

  39. 39.

    Counts, S. E. & Mufson, E. J. Regulator of cell cycle (RGCC) expression during the progression of Alzheimer’s disease. Cell Transplant. 26, 693–702 (2017).

    Article  Google Scholar 

  40. 40.

    Zhao, J., Deng, Y., Jiang, Z. & Qing, H. G protein-coupled receptors (GPCRs) in Alzheimer’s disease: a focus on BACE1 related GPCRs. Front. Aging Neurosci. 8, 58 (2016).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Maddison, D. C. & Giorgini, F. The kynurenine pathway and neurodegenerative disease. Semin. Cell. Dev. Biol. 40, 134–141 (2015).

    CAS  Article  Google Scholar 

  42. 42.

    Visel, A., Rubin, E. M. & Pennacchio, L. A. Genomic views of distant-acting enhancers. Nature 461, 199–205 (2009).

    CAS  Article  Google Scholar 

  43. 43.

    Lunnon, K. et al. Variation in 5-hydroxymethylcytosine across human cortex and cerebellum. Genome. Biol. 17, 27 (2016).

    Article  Google Scholar 

  44. 44.

    Kinde, B., Gabel, H. W., Gilbert, C. S., Griffith, E. C. & Greenberg, M. E. Reading the unique DNA methylation landscape of the brain: non-CpG methylation, hydroxymethylation, and MeCP2. Proc. Natl. Acad. Sci. USA 112, 6800–6806 (2015).

    CAS  Article  Google Scholar 

  45. 45.

    Selkoe, D. J. & Hardy, J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol. Med. 8, 595–608 (2016).

    CAS  Article  Google Scholar 

  46. 46.

    Gräff, J. & Tsai, L. H. Histone acetylation: molecular mnemonics on the chromatin. Nat. Rev. Neurosci. 14, 97–111 (2013).

    Article  Google Scholar 

  47. 47.

    Guintivano, J., Aryee, M. J. & Kaminsky, Z. A. A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression. Epigenetics 8, 290–302 (2013).

    CAS  Article  Google Scholar 

  48. 48.

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

    CAS  Article  Google Scholar 

  49. 49.

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

    Article  Google Scholar 

  50. 50.

    McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    CAS  Article  Google Scholar 

  51. 51.

    Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239–259 (1991).

    CAS  Article  Google Scholar 

  52. 52.

    Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    CAS  Article  Google Scholar 

  53. 53.

    Andrews, S. FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).

  54. 54.

    Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome. Biol. 10, R25 (2009).

    Article  Google Scholar 

  55. 55.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  56. 56.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  Article  Google Scholar 

  57. 57.

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

    Article  Google Scholar 

  58. 58.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  Article  Google Scholar 

  59. 59.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  Article  Google Scholar 

  60. 60.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome. Biol. 15, 550 (2014).

    Article  Google Scholar 

  61. 61.

    Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS. Comput. Biol. 9, e1003118 (2013).

    CAS  Article  Google Scholar 

  62. 62.

    Lun, A. T., Chen, Y. & Smyth, G. K. It’s D × 10–licious: a recipe for differential expression analyses of RNA-seq experiments using quasi-likelihood methods in edgeR. Methods Mol. Biol. 1418, 391–416 (2016).

    Article  Google Scholar 

  63. 63.

    Ashburner, M. et al. The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    CAS  Article  Google Scholar 

  64. 64.

    Osborne, J. D. et al. Annotating the human genome with Disease Ontology. BMC Genomics 10(Suppl 1), S6 (2009).

    Article  Google Scholar 

  65. 65.

    Thomas-Chollier, M. et al. RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets. Nucleic Acids Res. 40, e31 (2012).

    CAS  Article  Google Scholar 

  66. 66.

    Thomas-Chollier, M. et al. A complete workflow for the analysis of full-size ChIP-seq (and similar) data sets using peak-motifs. Nat. Protoc. 7, 1551–1568 (2012).

    CAS  Article  Google Scholar 

  67. 67.

    Mathelier, A. et al. JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 44(D1), D110–D115 (2016).

    CAS  Article  Google Scholar 

  68. 68.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  Article  Google Scholar 

  69. 69.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  Google Scholar 

  70. 70.

    Hannon, E. et al. An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation. Genome. Biol. 17, 176 (2016).

    Article  Google Scholar 

  71. 71.

    Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  Article  Google Scholar 

  72. 72.

    Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS  Article  Google Scholar 

  73. 73.

    Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 29, e45 (2001).

    CAS  Article  Google Scholar 

  74. 74.

    Pidsley, R. et al. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics 14, 293 (2013).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the help of K. Paszkiewicz at the University of Exeter Sequencing Service for advice on ChIP-seq experiments. We also acknowledge the help of V. Teif at the University of Essex in generating the UCSC Genome Browser tracks. Analysis was facilitated by access to the Genome high performance computing cluster at the University of Essex School of Biological Sciences. We acknowledge the help of B. Lee from the University of Exeter Medical School for advice on gene expression assays. We acknowledge S. Prabhakar and W. Sun from the Genome Institute of Singapore for sharing their brain H3K27ac data for our comparative analyses. This work was funded by US National Institutes of Health grant R01 AG036039 to J.M. S.J.M. and T.R. were funded by the EU-FP7 Marie Curie ITN EpiTrain (REA grant agreement no. 316758). S.K.L. is funded by a Medical Research Council (MRC) CASE PhD studentship. Sequencing infrastructure was supported by a Wellcome Trust Multi User Equipment Award (WT101650MA) and Medical Research Council (MRC) Clinical Infrastructure Funding (MR/M008924/1). Generation of DNA hydroxymethylation data was funded by an Alzheimer’s Association US New Investigator Research Grant (grant number NIRG-14-320878) and a grant from BRACE (Bristol Research into Alzheimer’s and Care of the Elderly), both to K.L.

Author information

Affiliations

Authors

Contributions

S.J.M., S.K.L., T.R., E.P., and K.M. conducted laboratory experiments. J.M., L.C.S., and S.J.M. designed the study. J.M. supervised the project and obtained funding. S.J.M. undertook primary data analyses and bioinformatics, with analytical and computational input from L.C.S., E.H., and S.N. E.H. undertook the LD score regression and GWAS enrichment analyses. C.T. and S.A.-S. provided brain tissue for analysis. K.L. and A.R.S. generated and preprocessed the DNA modification data. J.P. and S.B. provided advice for the ChIP-seq analyses. S.J.M. and J.M. drafted the manuscript. All of the authors read and approved the final submission.

Corresponding author

Correspondence to Jonathan Mill.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–23 and Supplementary Tables 1–3, 6–10, 17, 18, 20, 21

Reporting Summary

Supplementary Table 4

Hyperacetylated AD-associated H3K27ac peaks.

Supplementary Table 5

Hypoacetylated AD-associated H3K27ac peaks.

Supplementary Table 11

Enriched pathways (FDR < 0.05) for the ontology “Biological Process” amongst AD-hyperacetylated H3K27ac peaks.

Supplementary Table 12

Enriched pathways (FDR < 0.05) for the ontology “Molecular Function” amongst AD-hyperacetylated H3K27ac peaks.

Supplementary Table 13

Enriched pathways (FDR < 0.05) for the ontology “Disease Ontology” amongst AD-hyperacetylated H3K27ac peaks.

Supplementary Table 14

Enriched pathways (FDR < 0.05) for the ontology “Biological Process” amongst AD-hypoacetylated H3K27ac peaks.

Supplementary Table 15

Enriched pathways (FDR < 0.05) for the ontology “Molecular Function” amongst AD-hypoacetylated H3K27ac peaks.

Supplementary Table 16

Enriched pathways (FDR < 0.05) for the ontology “Disease Ontology” amongst AD-hypoacetylated H3K27ac peaks.

Supplementary Table 19

Correlation statistics for H3K27ac and DNA methylation at the 439 significantly correlated probe-peak pairs (FDR < 0.05).

Supplementary Software

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Marzi, S.J., Leung, S.K., Ribarska, T. et al. A histone acetylome-wide association study of Alzheimer’s disease identifies disease-associated H3K27ac differences in the entorhinal cortex. Nat Neurosci 21, 1618–1627 (2018). https://doi.org/10.1038/s41593-018-0253-7

Download citation

Further reading