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.
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Brookmeyer, R., Johnson, E., Ziegler-Graham, K. & Arrighi, H. M. Forecasting the global burden of Alzheimer’s disease. Alzheimers Dement. 3, 186–191 (2007).
Wenk, G. L. Neuropathologic changes in Alzheimer’s disease. J. Clin. Psychiatry 64(Suppl 9), 7–10 (2003).
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).
Karch, C. M., Cruchaga, C. & Goate, A. M. Alzheimer’s disease genetics: from the bench to the clinic. Neuron 83, 11–26 (2014).
Reitz, C., Brayne, C. & Mayeux, R. Epidemiology of Alzheimer disease. Nat. Rev. Neurol. 7, 137–152 (2011).
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).
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).
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).
Lunnon, K. et al. Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer’s disease. Nat. Neurosci. 17, 1164–1170 (2014).
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).
Creyghton, M. P. et al. Histone H3K27ac separates active from poised enhancers and predictsdevelopmental state. Proc. Natl. Acad. Sci. USA 107, 21931–21936 (2010).
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).
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).
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).
Zhang, K. et al. Targeted proteomics for quantification of histone acetylation in Alzheimer’s disease. Proteomics. 12, 1261–1268 (2012).
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).
Sun, W. et al. Histone acetylome-wide association study of autism spectrum disorder. Cell 167, 1385–1397.e11 (2016).
Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
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).
Kamphuis, W. et al. Glial fibrillary acidic protein isoform expression in plaque related astrogliosis in Alzheimer’s disease. Neurobiol. Aging. 35, 492–510 (2014).
Santpere, G., Nieto, M., Puig, B. & Ferrer, I. Abnormal Sp1 transcription factor expression in Alzheimer disease and tauopathies. Neurosci. Lett. 397, 30–34 (2006).
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).
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).
Spillantini, M. G. & Goedert, M. Tau pathology and neurodegeneration. Lancet. Neurol. 12, 609–622 (2013).
Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).
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).
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).
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).
De Strooper, B. et al. Deficiency of presenilin-1 inhibits the normal cleavage of amyloid precursor protein. Nature 391, 387–390 (1998).
Goate, A. & Hardy, J. Twenty years of Alzheimer’s disease-causing mutations. J. Neurochem. 120(Suppl 1), 3–8 (2012).
Crehan, H. et al. Complement receptor 1 (CR1) and Alzheimer’s disease. Immunobiology 217, 244–250 (2012).
Heppner, F. L., Ransohoff, R. M. & Becher, B. Immune attack: the role of inflammation in Alzheimer disease. Nat. Rev. Neurosci. 16, 358–372 (2015).
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).
Jaeger, S. & Pietrzik, C. U. Functional role of lipoprotein receptors in Alzheimer’s disease. Curr. Alzheimer. Res. 5, 15–25 (2008).
Sun, X. et al. Hypoxia facilitates Alzheimer’s disease pathogenesis by up-regulating BACE1 geneexpression. Proc. Natl. Acad. Sci. USA 103, 18727–18732 (2006).
Zlokovic, B. V. Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders. Nat. Rev. Neurosci. 12, 723–738 (2011).
Warren, J. D., Rohrer, J. D. & Rossor, M. N. Clinical review. Frontotemporal dementia. Br. Med. J. 347, f4827 (2013).
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).
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).
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).
Maddison, D. C. & Giorgini, F. The kynurenine pathway and neurodegenerative disease. Semin. Cell. Dev. Biol. 40, 134–141 (2015).
Visel, A., Rubin, E. M. & Pennacchio, L. A. Genomic views of distant-acting enhancers. Nature 461, 199–205 (2009).
Lunnon, K. et al. Variation in 5-hydroxymethylcytosine across human cortex and cerebellum. Genome. Biol. 17, 27 (2016).
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).
Selkoe, D. J. & Hardy, J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol. Med. 8, 595–608 (2016).
Gräff, J. & Tsai, L. H. Histone acetylation: molecular mnemonics on the chromatin. Nat. Rev. Neurosci. 14, 97–111 (2013).
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).
Nativio, R. et al. Dysregulation of the epigenetic landscape of normal aging in Alzheimer’s disease. Nat. Neurosci. 21, 497–505 (2018).
Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239–259 (1991).
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).
Andrews, S. FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).
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).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome. Biol. 9, R137 (2008).
Anders, S., Pyl, P. T. & Huber, W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
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).
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).
Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS. Comput. Biol. 9, e1003118 (2013).
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).
Ashburner, M. et al. The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
Osborne, J. D. et al. Annotating the human genome with Disease Ontology. BMC Genomics 10(Suppl 1), S6 (2009).
Thomas-Chollier, M. et al. RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets. Nucleic Acids Res. 40, e31 (2012).
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).
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).
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).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
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).
Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 29, e45 (2001).
Pidsley, R. et al. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics 14, 293 (2013).
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.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figures 1–23 and Supplementary Tables 1–3, 6–10, 17, 18, 20, 21
Hyperacetylated AD-associated H3K27ac peaks.
Hypoacetylated AD-associated H3K27ac peaks.
Enriched pathways (FDR < 0.05) for the ontology “Biological Process” amongst AD-hyperacetylated H3K27ac peaks.
Enriched pathways (FDR < 0.05) for the ontology “Molecular Function” amongst AD-hyperacetylated H3K27ac peaks.
Enriched pathways (FDR < 0.05) for the ontology “Disease Ontology” amongst AD-hyperacetylated H3K27ac peaks.
Enriched pathways (FDR < 0.05) for the ontology “Biological Process” amongst AD-hypoacetylated H3K27ac peaks.
Enriched pathways (FDR < 0.05) for the ontology “Molecular Function” amongst AD-hypoacetylated H3K27ac peaks.
Enriched pathways (FDR < 0.05) for the ontology “Disease Ontology” amongst AD-hypoacetylated H3K27ac peaks.
Correlation statistics for H3K27ac and DNA methylation at the 439 significantly correlated probe-peak pairs (FDR < 0.05).
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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
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