Epigenetic dysregulation of enhancers in neurons is associated with Alzheimer’s disease pathology and cognitive symptoms

Epigenetic control of enhancers alters neuronal functions and may be involved in Alzheimer’s disease (AD). Here, we identify enhancers in neurons contributing to AD by comprehensive fine-mapping of DNA methylation at enhancers, genome-wide. We examine 1.2 million CpG and CpH sites in enhancers in prefrontal cortex neurons of individuals with no/mild, moderate, and severe AD pathology (n = 101). We identify 1224 differentially methylated enhancer regions; most of which are hypomethylated at CpH sites in AD neurons. CpH methylation losses occur in normal aging neurons, but are accelerated in AD. Integration of epigenetic and transcriptomic data demonstrates a pro-apoptotic reactivation of the cell cycle in post-mitotic AD neurons. Furthermore, AD neurons have a large cluster of significantly hypomethylated enhancers in the DSCAML1 gene that targets BACE1. Hypomethylation of these enhancers in AD is associated with an upregulation of BACE1 transcripts and an increase in amyloid plaques, neurofibrillary tangles, and cognitive decline.

For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.
n/a Confirmed The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection EpiCompare: Predict tissue/cell type-specific enhancers/promoters ChromHMM(v1.14): Predict chromatin state data ppDesigner(v1.1): Generate padlock probes Data analysis Bismark(v0.17  Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability All sequencing data generated in this study are available from the NCBI Gene Expression Omnibus (GEO) database under the accession number GSE110732 Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

Life sciences Behavioural & social sciences Ecological, evolutionary & environmental sciences
For a reference copy of the document with all sections, see nature.com/documents/nr-reporting-summary-flat.pdf

Life sciences study design
All studies must disclose on these points even when the disclosure is negative.
Sample size DNA methylation status was interrogated at every cytosine site (CpG and CpH) covered by padlock probes targeting 35,288 regulatory regions across the genome of 131 samples (n=106 unique samples, 2 whole-genome amplified (WGA) control samples and 23 replicate samples). RNA-sequencing to profile the mRNA transcriptome in the prefrontal cortex of 25 individuals (samples also in DNA methylation study above).
Data exclusions In the DNA methylation analysis: There were 7 samples (5 unique samples and two replicates) that were excluded from further analyses due to poor inter-sample correlations (>10% difference). No samples were excluded from the RNA-seq analysis.

Replication
For our bisulfite padlock probe (DNA methylation) library preparation: Technical and sequencing replicates confirmed a high reproducibility in sample-level methylation correlation analysis (average R for technical replicates: 0.976; average R for sequencing replicates: 0.976).
We replicated our study's results/findings with two independent datasets: 1) ROSMAP data (RNA-seq, DNA methylation arrays, SNP arrays) performed in prefrontal cortex of control and AD patients and 2) proteomics dataset in laser captured cortical neurons from controls and AD patients.
Randomization All samples were randomized in the study (both in the isolation of neuronal nuclei and DNA methylation and RNA-seq library preparation).

Blinding
An experimenter blind to the sample key performed the isolation of neuronal nuclei and the DNA methylation and RNA-seq library preparation.
Reporting for specific materials, systems and methods

October 2018
We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response. The axis labels state the marker and fluorochrome used (e.g. CD4-FITC).
The axis scales are clearly visible. Include numbers along axes only for bottom left plot of group (a 'group' is an analysis of identical markers).
All plots are contour plots with outliers or pseudocolor plots.
A numerical value for number of cells or percentage (with statistics) is provided.

Methodology
Sample preparation Human brain tissue (250 mg) for each sample was minced in 2 mL PBSTA (0.3 M sucrose, 1X phosphate buffered saline (PBS), 0.1% Triton X-100). Samples were then homogenized in PreCellys CKMix tubes with a Minilys (Bertin Instruments) set at 3,000 rpm for three 5 sec intervals, 5 min on ice between intervals. Samples homogenates were filtered through Miracloth (EMD Millipore), followed by a rinse with an additional 2 mL of PBSTA. Samples were then place on a sucrose cushion (1.4 M sucrose) and nuclei were pelleted by centrifugation at 4,000 × g for 30 min 4°C using a swinging bucket rotor. For each sample, the supernatant was removed and the pellet was incubated in 700 μl of 1X PBS on ice for 20 min. The nuclei were then gently resuspended and blocking mix (100 μl of 1X PBS with 0.5% BSA (Thermo Fisher Scientific) and 10% normal goat serum (Gibco)) was added to each sample. NeuN-488 (1:500; Abcam) was added and samples were incubated 45 min at 4°C with gentle mixing.

Instrument
MoFlo Astrios (Beckman Coulter) Software Summit 6.3 Cell population abundance Approximately 1 million NeuN+ nuclei were sorted for each sample. NeuN+ sample purity (~96%) was confirmed by reanalysis on the sorter, and was confirmed by RNA analysis showing an enrichment of neuronal markers in the NeuN+ (but not NeuN-) fraction ( Supplementary Fig. 1). Immediately, after sorting nuclei were placed on ice and then precipitated with 0.3 M sucrose, 4.2 mM CaCl2 and 2.5 mM Mg(Ace)2 and centrifugation at 1,786 × g for 15 min at 4°C. The supernatant was removed from NeuN+ and NeuN-samples and pellets were stored at -80°C. Genomic DNA from each sample's NeuN+ fraction was isolated using standard phenol-chloroform extraction methods

Gating strategy
Nuclei positive for 7-AAD and either NeuN+ (neuronal) or NeuN-(non-neuronal) were sorted using a MoFlo Astrios (Beckman Coulter) running Summit 6.3 by the Van Andel Research Institute Flow Cytometry Core. Gating was based on unstained, NeuN+ only, and 7-AAD only controls (each independently run to determine the gating with FSC). Approximately 1 million NeuN+ nuclei were sorted for each sample. NeuN+ sample purity (~96%) was confirmed by reanalysis on the sorter, and was confirmed by RNA analysis showing an enrichment of neuronal markers in the NeuN+ (but not NeuN-) fraction ( Supplementary Fig. 1).
Tick this box to confirm that a figure exemplifying the gating strategy is provided in the Supplementary Information.