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Epigenetic modulation of AREL1 and increased HLA expression in brains of multiple system atrophy patients

Acta Neuropathologica Communications volume 8, Article number: 29 (2020) Cite this article

Abstract

Multiple system atrophy (MSA) is a rare disease with a fatal outcome. To date, little is known about the molecular processes underlying disease development. Its clinical overlap with related neurodegenerative movement disorders underlines the importance for expanding the knowledge of pathological brain processes in MSA patients to improve distinction from similar diseases. In the current study, we investigated DNA methylation changes in brain samples from 41 MSA patients and 37 healthy controls. We focused on the prefrontal cortex, a moderately affected area in MSA. Using Illumina MethylationEPIC arrays, we investigated 5-methylcytosine (5mC) as well as 5-hydroxymethylcytosine (5hmC) changes throughout the genome. We identified five significantly different 5mC probes (adj. P < 0.05), of which one probe mapping to the AREL1 gene involved in antigen presentation was decreased in MSA patients. This decrease correlated with increased 5hmC levels. Further, we identified functional DNA methylation modules involved in inflammatory processes. As expected, the decreased 5mC levels on AREL1 was concordant with increased gene expression levels of both AREL1 as well as MHC Class I HLA genes in MSA brains. We also investigated whether these changes in antigen-related processes in the brain associated with changes in peripheral mononuclear cells. Using flow cytometry on an independent cohort of MSA patients, we identified a decrease in circulating non-classical CD14+CD16++ blood monocytes, whereas T and NK cell populations were unchanged. Taken together, our results support the view of an active neuroimmune response in brains of MSA patients.

Introduction

Multiple System Atrophy (MSA) is a detrimental disease with no treatment possibilities. It is a neurodegenerative disease of the alpha-synucleinopathies where alpha-synuclein accumulates in both neurons and oligodendrocytes of the brain as neuronal or glial cytoplasmic inclusions, respectively [1, 2]. The mean onset is 55–60 years of age with an estimated survival time of 6 years [3]. The exact aetiology of MSA is unknown. The involvement of genomic factors in MSA development has been investigated [4], but so far the results have been inconclusive. This may correlate well with an estimated heritability of MSA below 7% [5]. Other mechanisms such as epigenetic changes may better explain development of MSA as they are proposed to causally reflect genetic-environmental interactions [6]. Epigenetic changes to the DNA have long been suspected to play a role in neurodegenerative diseases, including MSA [7, 8], but also Alzheimer’s disease (AD) [9, 10], Parkinson’s disease (PD) [11, 12], and Progressive Supranuclear Palsy (PSP) [13]. Specifically, 5-methylcytosine (5mC) is recognized as an important regulator of gene expression [14], but is not the only DNA-based epigenetic modification. 5-Hydroxymethylcytosine (5hmC), another equally important epigenetic regulator, is widely distributed in the brain [15] and is implicated in fetal brain development [16] as well as in different brain disorders [17, 18]. Disease-specific differences in global 5mC and 5hmC levels have been reported in selected areas of the brain in MSA patients by immunodetection [19]. However, the region-specific differences in global 5mC methylation levels could not be replicated in a recent array-based study as analysis of 5hmC was not performed [8]. Therefore, more information on the epigenetic landscape in MSA is required in order to infer on fundamental biological functions involved.

In the current study, we performed an epigenome-wide association study (EWAS) on prefrontal cortex brain tissue from 41 MSA patients and 37 normal, healthy controls (CTRLs). We utilized the Infinium MethylationEPIC array which allows for analyses of more than 850,000 methylation sites (CpGs) analysing both 5mC and 5hmC levels at single-nucleotide resolution. Our study thus represents the first detailed assessment of DNA methylation and hydroxymethylation in MSA brains. The aim of the study was to identify gene-specific epigenetic changes as well as the affected biological functions. From previous studies, we know that the prefrontal cortex is affected in MSA [20]. We validated the results from the BeadChip using NGS-based amplicon sequencing and performed RT-qPCR to confirm gene expression alterations of immune related components. Finally, in a novel cohort of 24 MSA patients and 46 CTRLs we validated the involvement of these immune-related changes during the disease course by investigating alterations in the composition of peripheral blood immune cells.

Materials and methods

Patient material

In the current study, 78 samples from the dorsomedial prefrontal cortex from frozen human brains stored at − 80 °C were included (41 MSA, 37 CTRL). The samples consisted of both grey and white matter tissue. The brains had been donated to the Brain Bank at Bispebjerg-Frederiksberg Hospital (University Hospital of Copenhagen, Denmark), the MRC London Neurodegenerative Diseases Brain Bank (King’s College London, United Kingdom), or the Netherlands Brain Bank (Royal Netherlands Academy of Arts and Science, Netherlands). For the Danish and Dutch samples, all donors provided informed written consent prior to death. For the English samples, informed written consent was retrieved from donors or their next of kin. Diagnoses were performed by trained medical personal according to the current MSA consensus guidelines [21]. Subsequently, included brains underwent pathological examinations to verify the final diagnosis. Subtype diagnoses were available for 20 patients, and they were divided into an olivopontocerebellar subtype (MSA-C), a striatonigral subtype (MSA-P) or a mixed subtype where neither cerebellar ataxia nor parkinsonism were the dominating feature (MSA mixed). For the remaining patient samples, the sub-diagnoses were unknown. Demographic data are shown in Table 1 and Suppl. Table 1, Online Resource 1. This project was approved by the regional ethical committee of the capital region of Denmark, j.nr. H-16025210. All experiments were conducted in accordance with the Declaration of Helsinki [22].

Table 1 Summary of demographic data
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DNA methylation arrays

DNA was isolated from 50 mg brain tissue as described in Online Resource 2. Bisulphite (BS) and oxidative bisulfite (oxBS) treatments were performed using the TrueMethyl Array Kit (CEGX, v. 3.1, March 2017) following the manufacturer’s recommendations. A digestion control was included for all samples. Both sample treatment, and array sample position was randomized in order to eliminate batch effects. In brief, 1 μg gDNA per sample was denatured for 5 min at 37 °C. Then, samples were divided into two fractions for subsequentBS and oxBS treatment. The samples were oxidized (oxBS fraction), converting hydroxymethylated cytosines to formylcytosines, or mock treated (BS fraction) for 10 min at 40 °C. Samples were bisulfite treated for 2 h, and then desulfonated for 5 min before elution. Digestion efficiency was assessed by PCR amplification and gel electrophoresis using the QIAquick PCR Purification Kit (Qiagen; #28104) for DNA clean-up following the manufacturer’s instructions. Amplicon concentrations were measured using the Qubit dsDNA HS Assay Kit (Invitrogen; #Q32854) on a Qubit 2.0 Fluorometer (Life Technologies). One aliquot of purified amplicons was saved for gel analysis. Amplicons were digested using restriction enzymes by incubation at 65 °C for 18 h before denaturation at 80 °C for 20 min. The digested and undigested samples were run on a 2% agarose gel with SYBR Safe (1:10; Invitrogen; #S33102) to assess digestion efficiency. Next, 200 ng of the treated samples were hybridized to Infinium Methylation EPIC BeadChip arrays (Illumina; #WG-317) and imaged on an iScan system (Illumina).

Bioinformatics and statistics

The bioinformatic analyses were performed in R v. 3.5.0 [23] using ChAMP v. 2.13.5 [24]. Data are available at GEO (GSE143157). Data were mapped to Homo sapiens GRCh37 unless otherwise noted. Initially, two samples were excluded due to high fraction of failed probes or mismatch between the stated sex versus the predicted sex from the getSex function from the minfi package [25] (data not shown). For the remaining 78 samples, probes were loaded [25] and filtered [26, 27] based on standard settings yielding 731,661 5mC probes. Samples were normalized using BMIQ [28]. After normalization, we investigated sample variability in our setup. We calculated the intra-assay coefficient of variation to 6.3% (n = 2), and the inter-assay coefficient of variance to mean 9.9% (range 6.4–13.8%, n = 16). Using hierarchical clustering (Suppl. Fig. 2a, Online Resource 3), we identified a group of outliers consisting of eight MSA and seven CTRL samples. These were not associated with any specific technical or clinical parameters. These samples were removed from downstream analyses, and 63 samples remained. For the 5hmC fraction, methylation levels were calculated as the delta values between the BS and oxBS treated fractions of the samples. Negative values were denoted as NAs, and probes with a fraction of NAs > 0.2 were removed. The remaining NAs were imputed using kNN imputation from the impute package [29]. This left us with 405,408 5hmC probes. Following the recommendations by Lunnon et al. [30], we found 62,653 probes with β < 0.046 (the lowest 5th percentile of negative ΔβBS-oxBS across all samples) that were removed in the secondary analyses. Batch effects were investigated using SVD plots [31] (Suppl. Fig. 2b-c, Online Resource 3). No batch effects were identified for the first principal component for any of the fractions (5mC or 5hmC), which accounted for the largest single contribution to the observed variation (Suppl. Fig. 2d-e, Online Resource 3). We calculated the neuronal fraction in our samples as previously described [13] using the estimateCellCounts function from the minfi package [25], and the FlowSorted.DLPFC.450 k package. Differentially methylated probes were identified using limma [32] using a linear regression model including age and the neuronal fraction for which the Benjamini-Hochberg method was used to control the False Discovery Rate [33]. Age was included in the model since the MSA patients were significantly younger than the CTRLs (Table 1). Q-Q plots are shown in Suppl. Fig. 2f-g, Online Resource 3. We compared overlapping results with other EWAS studies on brain tissue by considering all our probes with FDR < 0.20, and compared it to available probe/gene lists from four other studies (all FDR < 0.05): Bettencourt et al. (their Suppl. Tables 2.1–2.4) [8], Weber et al. (their Suppl. Table 2) [13], De Jager et al. (their Suppl. Table 2) [34], and Gasparoni et al. (their Suppl. Tables 4, 6, 7, 12, 13, 16, 18) [10]. Bumphunter was used for identification of differentially methylated regions or blocks, the model included age and neuronal fraction as for differentially methylated probe analyses [35, 36]. The champ. EpiMod function based on the FEM package was used to identify epigenetic modules based on an agnostic approach using beta values for all probes using CTRL sample data as reference [37]. FANTOM5 [38] enhancer overlaps were evaluated with predefined brain-specific tracks from SlideBase [39]. Chromatin state overlaps were evaluated based on chromHMM [40] analyses from predefined tracks from the NIH Roadmap Epigenomics consortium [41]. Motif enrichment was analysed using Analysis of Motif Enrichment from MEME Suite v.5.0.5 [42] against the HOCOMOCO Human v. 11 database [43] using 15 bases downstream and upstream from relevant probes mapping to cytosine positions. Q-Q and Manhattan plots were produced with qqman v.0.1.4 [44].

Validation of DNA methylation and hydroxymethylation levels using amplicon sequencing

Validation was performed using the BiSulfite Amplicon Sequencing (BSAS) approach based on the separate amplification of individual regions of interest, followed by tagmentation and next generation sequencing [45] described in Online Resource 2.

RNA expression analysis

RNA was extracted from 16 MSA and 20 CTRL brain samples of which 15 MSA and 6 CTRL samples overlapped with the samples included in the EWAS analysis. Demographic data for this sub-cohort are shown in Table 1 and Suppl. Table 3, Online Resource 1. RNA was extracted using the miRNeasy Mini Kit (Qiagen; #217004) following the manufacturer’s instructions [20]. The protocol is described in detail in Online Resource 2. Reverse transcription quantitative real-time PCR was performed as earlier described [20] in accordance with the MIQE guidelines [46]. We utilized primers for AREL1 (PrimerBank ID [47]: 87116667c3, 132 bps) and MHC Class I HLAs (covering HLAs A-C and E-G; F: 5-CCTACGACGGCAAGGATTAC-3, R: 5-TGCCAGGTCAGTGTGATCTC-3 [48], 304 bps). Sample cycle threshold (Ct) values were normalised to the expression of the reference genes UBE2D2, TOP1 and CYC1, as determined earlier [49], using the geometric mean [50].

Flow cytometry

Flow cytometric analyses were performed on peripheral blood mononuclear cells from an independent cohort of 24 MSA patients and 46 CTRLs following a previously described procedure [51]. Patients with a probable MSA-P or MSA-C diagnosis were included. Four of these patients died during the course of the study and had agreed to donate their brains to the brain bank, and their diagnoses were pathologically validated. None of the patient samples overlapped with the DNA or RNA brain samples used in this study. Demographic data are shown in Table 1 and Suppl. Table 4, Online Resource 1.

Single-cell RNA expression

We investigated expression of relevant targets in public single-cell RNA data datasets from the BRAIN Initiative Cell Census Network (BICCN, RRID:SCR_015820; https://biccn.org/) uploaded to the Neuroscience Multi-omic Archive (NeMOarchive, RRID:SCR_016152; https://nemoarchive.org/data/). Data originated from the human primary motor cortex prepared using Smart-seqV4 reagents. Data were available for 11,577 cells which was prepared for analysis using PAGODA2 (https://github.com/hms-dbmi/pagoda2) and Conos [52] in R v. 3.5.0 [23].

Results

Differentially methylated probes in MSA patients

We analysed genome-wide DNA methylation and hydroxymethylation profiles using an array-based approach in the prefrontal cortex of 41 MSA patients and 37 CTRLs. We identified differentially methylated probes using a linear regression model that included age as well as an estimation of the fraction of neuronal cells. We estimated the fraction of neurons in our samples as previously described [13]. The neuronal fraction did not differ between groups (Wilcoxon’s non-parametric t-test, W = 514, P = 0.800).

For the 5mC fraction, 731,661 probes remained after filtering while for the 5hmC fraction, 405,408 probes remained. Using a stringent Benjamini-Hochberg correction for multiple comparisons (FDR < 0.05), we identified five differentially methylated probes in MSA compared with CTRLs in the 5mC fraction (Table 2). Of these five probes, two mapped to gene bodies in AREL1 or KTN1 genes whereas the other probes mapped to intergenic regions. When we assessed CpGs with a relaxed correction for multiple testing (FDR < 0.20), 234 CpGs for the 5mC fraction (Fig. 1a-d) and nine CpGs for the 5hmC fraction (Fig. 1e-h) remained (Suppl. Table 5, Online Resource 1). Considering these probes, for the 5mC fraction we identified an enrichment of CpGs in CpG island shelf regions (hypergeometric test, P = 2.77E-05, 33 probes). For the 5hmC fraction, enrichment was identified for CpGs in CpG open sea regions (hypergeometric test, P = 1.66E-03, 7 probes). Further, we investigated overlap with enhancer regions, and motif enrichment for CpGs in TSS200 or TSS1500 regions. By integration with chromHMM data for chromatin states of the dorsolateral prefrontal cortex, we identified 42.3% of the listed CpGs to reside in Transcription Start Site (TSS)-related regions or in transcription-enriched regions for 5mC probes (Fig. 1d). No CpGs resided in brain-specific FANTOM5 enhancer regions. We identified two enriched motifs for 5mC methylation levels with adj. P < 0.05 that all bind GC-rich regions on the DNA: One motif related to HINFP activity (adj. P = 1.57E-02), and one motif related to ZIC3 activity (adj. P = 2.11E-02).

Table 2 Differentially Methylated Probes
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Fig. 1
figure 1

CpG probe distribution. a-h Locations of differentially methylated probes in multiple system atrophy (MSA) patients and normal, healthy controls (CTRLs) for the 5-methylcytosine (5mC; a-d) or 5-hydroxymethylcytosine (5hmC; e-h) fractions at FDR < 0.20. Probe distributions are shown for a,e intergenic or gene bound genomic areas; b,f 200 or 1500 bases upstream of transcription start site (TSS), in gene bodies, at the 3′ or 5′ untranslated region (UTR), or in the first exon; c,g in CpG island, shelf or shore areas, or in open sea areas; d,h and the CpG positions according to chromHMM tracks for the dorsolateral prefrontal cortex (TssA: active TSS; TssAFlnk: flanking TssA; Tx: strong transcription; TxWk: weak transcription; EnhG: genic enhancer; Enh: enhancer; ReprPC: repressed PolyComb; ReprPCWk: weak ReprPC; Quies: quiescent or low transcription)

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AREL1 presents a shift from cytosine methylation to cytosine hydroxymethylation

Of the five significant probes with FDR < 0.05 (Fig. 2a, b), two probes showed a mean difference > 5% with one probe mapping to AREL1 (cg08753407, change (Δ) in methylation (β) = − 9.1%, P = 1.47E-07; Fig. 2b) belonging to the E3 ubiquitin ligase family [53] necessary for antigen presentation [54]. Hence, this change in methylation indicates an immune activation in MSA patients. Consistently, the probe mapping to AREL1 was also the most significant probe in the 5hmC fraction showing an increase in MSA patients (Δβ = 8.5%, P = 2.69E-07; Fig. 2c) although without passing the correction for multiple testing (Suppl. Table 5, Online Resource 1). When we removed probes with small ΔβBS-oxBS values that might not be reliably detected [30], we confirmed cg08753407 on AREL1 to be the most significant probe (P = 2.28E-07, Suppl. Table 5, Online Resource 1). Additionally, the 5mC levels correlated with the 5hmC levels (Pearson’s correlation, P < 2.2E-16, R2 = 0.80; Fig. 2d), and the 5mC/5hmC ratio differed between groups (Welch’s t-test, t = 4.77, P = 2.25E-05; Fig. 2e). The other significant probe with mean difference > 5% mapped to an intergenic region (IGR) on chromosome 5 (cg24646067, Δβ = − 0.08%, P = 2.00E-07; Table 2) 148 Mb upstream of the non-coding gene LINC00461.

Fig. 2
figure 2

EWAS results. a Manhattan plot showing the distribution of P values for differences between multiple system atrophy (MSA) patients and normal controls (CTRLs) along the chromosomes. The horizontal red line indicates the adj. P value cut-off, the horizontal blue indicates cut-off for FDR < 0.20. b-c Box plots showing b the 5-methylcytosine (5mC) or c 5-hydroxymethylcytosine (5hmC) levels of MSA patients and CTRLs. Boxes show 1st (Q1) to 3rd (Q3) quartile of data, the horizontal line shows the median, and the whiskers show Q1-inter quartile range (IQR; lower whisker) or Q3 + IQR (upper whisker). Outliers are shown with dots. d Correlation of 5mC and 5hmC beta values. MSA are green, CTRLs red. e Box plot showing the 5mC/5hmC ratio

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Subtype diagnoses were available for 17 of our 33 MSA patients, nine MSA-C, six MSA-P and two MSA mixed. We investigated differentially methylated probes between MSA-C and MSA-P patients, however, no probes passed a relaxed threshold for multiple correction (FDR < 0.20; Suppl. Table 6, Online Resource 1). Additionally, methylation of the five significant probes in the 5mC fraction did not differ between subtypes (Student’s t-test, P > 0.05) in concordance with the assumption that the two subtypes represent the same disease.

We applied an agnostic approach to investigate methylation changes in small genomic regions spanning 100 s to 1000s of bases (DMRs) as well as large genomic regions spanning millions of bases (blocks). For the 5mC fraction, ten DMRs (Suppl. Table 7, Online Resource 1) were identified. Additionally, we identified one block (chr6:64,308,555-64,423,797; P = 3.52E-06) covering PHF3. The DMRs covered regions with genes having several different functions, including neuronal signalling (CHRNE, NCS1). Additionally, the block spanned PHF3 is involved in glioblastoma development.

In order to perspective our results to other EWAS studies on brain tissue from neurodegenerative diseases, we investigated overlaps for our probes with an adj. P < 0.20 and four other studies: one MSA study [8], one PSP study [13], and two AD studies [10, 34]. In total, we investigated 2181 unique probes and 1239 unique genes from these studies. For the 5mC fraction, we identified overlaps for two probes and 20 genes whereas one gene overlapped in the 5hmC fraction (Suppl. Table 8, Online Resource 1). Four genes were shared with the other EWAS on tissue from MSA patients, eight genes and one probe were shared with the EWAS on PSP tissue, while 14 genes and one probe were shared with the AD studies. No probe or gene were shared between all studies. Fourteen probes with FDR < 0.20 on the overlapping genes were present in both the 5mC and the 5hmC fraction, and Δβ changes were oppositely corresponding to each other (Suppl. Fig. 3, Online Resource 3). The functions of these genes are related to the extracellular matrix (COL23A1, LTBP3) and the immune system (PTPRN2, CYFIP1) while TIMP2 falls in both these categories. Furthermore, we investigated total methylation levels in order to compare our results directly to the study by Bettencourt et al. [8]. Specifically, Bettencourt et al. highlights probes on HIP1, LMAN2, and MOBP genes, however, in our setup the most significant probes on HIP1 and LMAN2 genes were only nominally significant (cg08710628 on HIP1, P = 0.003; cg05408837 on LMAN2, P = 0.008), whereas no probes on MOBP were significant. We investigated overlap with the 157 probes highlighted by Bettencourt et al. from their cross-region analysis. Seven probes from the current study had a nominally significant P < 0.05, however, no probes passed correction for multiple testing (Suppl. Table 5, Online Resource 1).

We validated the validity of the results from the MethylationEPIC array using high-throughput amplicon sequencing of 19 CpG methylation levels (ten 5mC, nine 5hmC). Our approach allowed us to assess methylation changes in the regions surrounding CpGs of interest. In total, 16 of the 19 CpGs showed methylation changes in the same direction as the array. Furthermore, we identified nominally significant methylation changes on surrounding CpG positions for several of the investigated loci, including several positions on HLA-A, HLA-F, and ZIC4 (Suppl. Table 10, Online Resource 1).

SNCA and other disease-related genes show no significant differential methylation

We investigated differentially methylated CpGs on ten genes that have been deemed important to MSA. These included COQ2, ELOVL7, GBA, LRRK2, MAPT, PARK2, PARK7, PINK1, SLC1A4, and SNCA (Suppl. Table 11, Online Resource 1). For the 5mC fraction this included 442 probes, whereas it included 243 probes for the 5hmC fraction. Although we identified several probes that were nominally significant, no probes passed the correction for multiple comparisons.

Epigenetic modules on inflammation-related genes are changed in MSA patients

We continued our agnostic approach by investigation of functional epigenetic modules. All modules are summarized in Table 3. When looking further into both 5mC and 5hmC fractions separately, 11 modules were identified that are involved in biological functions such as cellular functions (5mC: DNMT3B, VAMP8; 5hmC: GRK2, SNRPB). Additionally, in support of immune system involvement in MSA, four modules were related to inflammation (5mC: FCER1G, TNF; 5hmC: ITGA4, ZBTB16; Suppl. Fig. 4–5, Online Resource 3).

Table 3 Functional Epigenetic Modules
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Increased AREL1 and MHC class I HLA gene expression in MSA brains

We proceeded to investigate AREL1 gene expression levels in a sub-cohort of our samples (16 MSA, 20 CTRL; Table 1, Suppl. Table 3, Online Resource 1). We observed increased expression of AREL1 in the prefrontal cortex of MSA patients compared with CTRLs (Mann-Whitney t-test, U = 67, P = 0.013; Fig. 3a). Normalized AREL1 gene expression did not correlate with neither 5mC nor 5hmC levels (P > 0.05, data not shown). Additionally, AREL1 expression did not correlate with age, sex, PMI or RIN (P > 0.05). Based on the involvement of AREL1 in MHC class I antigen presentation, we decided to investigate whether we could detect increased HLA expression in brains of the MSA patients by quantification of joint mRNA expression of MHC Class I (HLAs A-C and E-G). We observed an increased MHC Class I HLA expression in MSA patients compared with CTRLs (Welch’s t-test, t = 2.777, P = 0.013; Fig. 3b). Furthermore, MHC class I gene expression was not correlated with age, sex, PMI or RIN (P > 0.05).

Fig. 3
figure 3

Expression of AREL1, MHC Class I HLAs and blood CD14+CD16++ monocyte changes. a-b RT-qPCR experiments quantifying AREL1 (a) or MHC Class I HLAs (HLA-A, −B, −C, −E, −F, −G; b). Data were normalized to the geometric mean of three reference genes UBE2D2, RPL13, and TOP1 [49]. c Dot plot showing normalized expression of AREL1 in oligodendrocytes, astrocytes, inhibitory neurons, excitatory neurons, oligodendrocyte precursor cells (OPCs), and microglia cells from five healthy human frontal cortical samples. Data for 11,577 cells are shown. d Levels of non-classical CD14+CD16++ monocytes in peripheral blood mononuclear cells as the fraction of TCR−CD3− cells. Boxes show 1st (Q1) to 3rd (Q3) quartile of data, the horizontal line shows the median, and the whiskers show Q1-inter quartile range (IQR; lower whisker) or Q3 + IQR (upper whisker). Outliers are shown with dots

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AREL1 is mostly expressed in neurons

We investigated expression of AREL1 in single-cell RNA-sequencing data from healthy human frontal cortex samples. We identified expression of AREL1 in all the investigated cell types, including excitatory and inhibitory neurons, oligodendrocytes, oligodendrocyte precursor cells, astrocytes, and microglia. Neurons, especially excitatory neurons, were the cell type with the highest fraction of cells expressing AREL1 (Fig. 3c).

Differences in peripheral blood mononuclear cell composition in MSA patients

In order to support our observations of an immune activation in MSA patients, we investigated the peripheral immune system. We wanted to confirm that immune alterations are present during the disease course and not just during disease end-stage. Using blood samples from a new patient cohort (24 MSA, 46 CTRL), we investigated the composition of peripheral blood mononuclear cells (PBMCs). We identified a decrease in the fraction of non-classical CD14 + CD16++ monocytes (F(2,67) = 4.235, P = 0.019, R2 = 0.112) determined by group (P = 0.007; Fig. 3d). This finding was neither correlated to disease duration nor Hoehn & Yahr staging (Spearman’s correlation, P > 0.05). We did not see any difference in activated CD45RA+ non-classical monocytes (F(2,67) = 1.506, P = 0.229, R2 = 0.043). Further, we did not observe any differences in CD4+ or CD8+ T cell composition, nor in CD56+ or CD57+ NK cell fractions (Suppl. Table 12, Online Resource 1).

Discussion

In the present study, we evaluated epigenetic modifications in brains of MSA patients at both methylated and hydroxymethylated cytosines in the DNA. Most importantly, we identified a shift from cytosine methylation towards hydroxymethylation, a modification commonly associated with increased gene expression activity, at the cg08753407 probe mapping to the AREL1 gene in MSA patients. The AREL1 gene codes for an E3 ubiquitin ligase involved in protein ubiquitination and degradation [53]. Further, E3 ubiquitin ligases mark proteins for degradation in the proteasome which is necessary for antigen presentation through MHC Class I complexes (reviewed by Loureiro & Ploegh [54]). This methylation shift on AREL1 was further illustrated by a change in the 5mC/5hmC ratio between sample groups, which would not be detectable by analysing total DNA methylation levels using standard bisulfite treatment. The altered ratio accommodated an increase in AREL1 gene expression in MSA patients, which is accompanied by increased expression of MHC Class I HLA genes. In the blood, we found the fraction of non-classical CD14+CD16++ monocytes to be decreased in MSA patients, whereas no differences were observed for the T cell or NK cell fractions. Collectively, our results support that MSA patients present an active neuroimmune response through increased antigen presentation, which is further reflected by a change in the composition of blood immune cells that does not involve neither T cells nor NK cells.

Together with these findings, we also identified several changes related to immune system responses in the MSA brains. The identification of altered epigenetic modules further support the involvement of innate and adaptive immune compartments in MSA. The most prominent modules that seems to be affected in MSA, are related to the lymphocyte homing receptor (ITGA4) [55], the antibody-binding receptor FCER1G [56], and the cytokine TNF [57], which has been previously investigated in MSA patients. To characterize whether the observed differences in gene methylation are functionally relevant, we investigated gene expression of HLA molecules. We identified a joint increase in expression of HLAs A-C and E-G genes thereby showing a possible link between methylation changes and antigen presentation. Our current observations on gene methylation changes encourage further investigations into the possible involvement of these genes in the pathology of MSA.

Finally, supporting the observations in brain tissue, there seems to be a systemic immune dysregulation in MSA as shown by the results on peripheral immune cells in a novel cohort of MSA patients and controls. We did not see disease-related differences in T cell levels in blood, but we identified a decreased fraction of non-classical CD14+CD16++ monocytes in MSA patients. Only classical monocyte levels have earlier been reported for a small cohort of MSA patients, however, the authors failed to identify differences for the MSA group compared with controls [58]. The exact role of non-classical monocytes in chronic diseases is not clear, but they are considered as anti-inflammatory, as they maintain vascular homeostasis [59]. The decrease in non-classical monocytes in MSA observed by us resembles what has been described for hereditary diffuse leukoencephalopathy with spheroids (HDLS) [60]. HDLS is an autosomal dominant white matter disorder [61] that is accompanied by parkinsonian features [62] and may present with Lewy bodies inclusions [63]. HDLS is caused by mutations in CSF1R, the receptor for colony-stimulating-factor (CSF), which is a growth factor for microglia, monocytes and macrophages [64].

Of interest, both MSA [65,66,67] and HDLS [68] share pathological astro- and microgliosis. In a previous study we found that MSA patients had lower protein levels of G-CSF, a growth factor belonging to the same family as CSF, in the prefrontal cortex of MSA brains [20]. These observations seem to support each other, as the observed decline in non-classical monocytes observed in blood of MSA patients is probably associated with the neuroinflammatory state of the patients. Additionally, in support of an increased inflammatory state in MSA, our laboratory has previously shown aberrant gene expression of Toll-like receptors in different brain areas of MSA patients [69]. Expression of these receptors is induced in response to infection as well as cell death [70]. Furthermore, the recent EWAS from Bettencourt et al. on MSA patients identified reduced total methylation on a probe in the TSS1500 region of IL2RA in MSA patients with the mixed subtype compared to normal controls [8]. Taken together, these results all support an involvement of an innate immune response in MSA.

We investigated whether methylation changes related to subtype diagnoses could be detected, however, we did not observe any MSA subtype-specific methylation. In contrast, Bettencourt et al. identified several subtype-specific changes [8]. In general, they observed the strongest effect for the MSA-C subtype. However, Bettencourt et al. investigated different brain tissues including the cerebellum, an area that is severely affected in MSA-C patients [71]. Hence, our results do not support subtype-specific changes in the prefrontal cortex, which was the area of interest in the present study. Further, we investigated overlaps between our results with other EWAS studies on brain tissue. Although several overlaps were identified, the involved biological functions were equivocal (Suppl. Table 8, Online Resource 1). Several overlapping genes are related to inflammatory processes (TIMP2, CYFIP1, PTPRN2, CUX1) whereas the remaining overlaps are related to different cellular processes. The apparent inconsistency between studies and diseases could be a result of distinct pleiotropic epigenetic processes in the brain disorders as well as differences in the experimental set-up and analysed tissues. Future comparative studies are encouraged to shed light of disease-dependent and independent epigenetic traits in neurodegenerative diseases.

Our analyses also revealed possible effects unrelated to inflammation. On brain material from the prefrontal cortex, we identified a significant probe in the TSS1500 region of KTN1 (cg27312312, Δβ = 3.9%, P = 2.08E-07). KTN1 is a gene coding for kinesin, a protein involved in intracellular vesicle transportation and related to cytoskeletal signalling and Rho GTPase signalling. Interestingly, a meta-analysis of GWAS studies found this gene to be associated with PD [72] suggesting a possible overlap in disease processes between these related diseases. Furthermore, Bettencourt et al. similarly identified Rho GTPase signalling to be involved in MSA pathology [8]. Additionally, the remaining three probes all mapped to IGRs. The probe closest to a gene was cg16096172 on chromosome 6 upstream of ADGRF5. Whether methylation changes on KTN1 or the IGR probes are biologically relevant to MSA pathology remains to be elucidated.

The recent publication by Bettencourt et al. was the first to report on epigenome methylation changes in MSA. In opposition to our study, they employed a region-wise comparison of total methylation changes identifying several significantly different probes and regions in samples from MSA patients. When comparing their results to our most significant probes, four gene overlaps were identified, one of them involved in the immune system (CYFIP1) and others involved in extracellular matrix regulation (COL23A1, CTBP3). Furthermore, in the present study we investigated both 5mC and 5hmC levels whereas Bettencourt et al. investigated only total methylation levels. We compared total methylation levels from our study on probes mapping to relevant genes identified by Bettencourt et al., however, no apparent overlaps were found. Furthermore, whereas 141 probes overlapped with the 157 probes identified in the cross-region analysis by Bettencourt et al., only seven probes had a nominal P < 0.05 while none of them had an adj. P < 0.05. The differences between the two studies may be explained in the selection of the studied tissue. Whereas Bettencourt et al. investigated white matter samples from different areas across the brain, we investigated samples that included both white and grey matter. When considering recent technological developments for single-cell assessment of epigenetic changes, it would be relevant in future studies to investigate the epigenetic contribution to changes in brain samples from MSA patients at the single-cell level.

In the current study, the following limitations must be considered. First, although we identified methylation changes in the prefrontal cortex of MSA patients, larger effects may be found in other brain region as demonstrated by Bettencourt et al. Indeed, a recent study screened for epigenetic changes in different brain areas in PD and MSA patients which showed a global increase of 5hmC intensity in the white matter of the cerebellum in both PD and MSA patients [19], while no difference in methylation levels was observed in the neocortex between PD patients and controls. Therefore, a careful evaluation of the area of interest prior to the initiation of novel epigenetic studies for MSA should be performed. In the current study we aimed to investigate a brain area previously shown to be affected in MSA [20, 65]. This approach was chosen in order to model epigenetic changes occurring at early stages of disease development and prior to massive cell death. Therefore, it was not in the scope of the current study to compare regional 5mC or 5hmC differences. Second, in the current study we investigated methylation changes on bulk brain samples since we hypothesized epigenetic changes to be large and to some degree cell independent. However, a recent post-mortem study in AD [10] showed the importance of cell stratification for investigating epigenetic changes in neurodegenerative diseases in order to detect changes using small sample numbers, even smaller than what was employed in the current study. We sought to approach this shortcoming by estimating the fraction of neuronal and glial cells in our samples, which we included in our regression model. Nevertheless, for future studies we encourage a priori isolation of the cell populations of interest prior to the epigenetic analyses. Third, since MSA patients have a shorter life expectancy than healthy individuals [3], MSA patients were on average 6.90 years younger than CTRLs in our setup. Since global hypomethylation occurs during aging [73], we included age as a covariant in our regression model thereby rejecting identification of significant probes affected by aging. Fourth, we investigated post-mortem tissue and therefore we cannot establish causality between our findings and development of MSA.

Conversely, our study set-up holds several strengths. First, and most importantly, we investigated hydroxymethylation levels which has not earlier been investigated in MSA. By extrapolating changes identified in the 5mC fraction to the 5hmC fraction we showed strongly correlated and concordant changes between methylation states underlining the biological validity of our results. Second, we applied a stringent bioinformatical approach where we first identified and removed outliers to reduce noise in our data. Third, our patient samples were all diagnosed by trained clinical personal, and the samples originated from different centres in different countries thus minimizing a possible regional bias. Additionally, all samples underwent pathological investigations to validate the diagnosis. Fourth, we performed a technical validation of the bead arrays as well as biological validation using RT-qPCR to investigate the effect of methylation changes on gene expression levels. Finally, we sought to determine the effects of our results on brain tissue by investigation of PBMC changes in samples from a novel patient cohort. Although we identify an atypical change in non-classical CD14+CD16++ monocytes that we cannot relate directly to our results on brain tissue, future evaluations of these biological differences may further identify the exact molecular aberrancies in MSA patients explaining our results.

Conclusions

To conclude, we identified several CpGs with genome-wide significance including a shift from 5mC to 5hmC methylation of the cg08753407 probe and associated gene expression changes of AREL1 in MSA patients, a gene related to antigen presentation. Further, these results were accompanied by increased gene expression of MHC Class I HLAs further implicating antigen presentation as a disease factor in MSA. Lastly, we saw a decrease in non-classical CD14+CD16++ monocytes in blood of MSA patients. Taken together, our results provide an epigenetic link between MSA and the immune processes in MSA patients. Not only do our result increase the knowledge about disease processes in MSA, they may also pave the way for immunomodulatory approaches to diagnose, treat, or prevent the onset of MSA.

Availability of data and materials

Normalized and raw BeadChip Array data have been deposited in NCBI Gene Expression Omnibus (GEO) with the accession code GSE143157.

All other data are available within the paper and its associated supplementary material or upon reasonable request from the corresponding authors.

References

  1. Stefanova N, Bücke P, Duerr S, Wenning GK (2009) Multiple system atrophy: an update. Lancet Neurol 8(12):1172–1178

    Article  CAS  PubMed  Google Scholar 

  2. Wenning GK, Stefanova N, Jellinger KA, Poewe W, Schlossmacher MG (2008) Multiple system atrophy: a primary oligodendrogliopathy. Ann Neurol 64(3):239–246

    Article  CAS  PubMed  Google Scholar 

  3. Kollensperger M, Geser F, Ndayisaba JP, Boesch S, Seppi K, Ostergaard K, Dupont E, Cardozo A, Tolosa E, Abele M, Klockgether T, Yekhlef F, Tison F, Daniels C, Deuschl G, Coelho M, Sampaio C, Bozi M, Quinn N, Schrag A, Mathias CJ, Fowler C, Nilsson CF, Widner H, Schimke N, Oertel W, Del Sorbo F, Albanese A, Pellecchia MT, Barone P, Djaldetti R, Colosimo C, Meco G, Gonzalez-Mandly A, Berciano J, Gurevich T, Giladi N, Galitzky M, Rascol O, Kamm C, Gasser T, Siebert U, Poewe W, Wenning GK (2010) Presentation, diagnosis, and management of multiple system atrophy in Europe: final analysis of the European multiple system atrophy registry. Mov Disord 25(15):2604–2612

    Article  PubMed  Google Scholar 

  4. Sailer A, Scholz SW, Nalls MA, Schulte C, Federoff M, Price TR, Lees A, Ross OA, Dickson DW, Mok K (2016) A genome-wide association study in multiple system atrophy. Neurology 87(15):1591–1598

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Federoff M, Price TR, Sailer A, Scholz S, Hernandez D, Nicolas A, Singleton AB, Nalls M, Houlden H (2016) Genome-wide estimate of the heritability of multiple system atrophy. Parkinsonism Relat Disord 22:35–41

    Article  CAS  PubMed  Google Scholar 

  6. Iraola-Guzman S, Estivill X, Rabionet R (2011) DNA methylation in neurodegenerative disorders: a missing link between genome and environment? Clin Genet 80(1):1–14

    Article  CAS  PubMed  Google Scholar 

  7. Sturm E, Stefanova N (2014) Multiple system atrophy: genetic or epigenetic? Exp Neurobiol 23(4):277–291

    Article  PubMed  PubMed Central  Google Scholar 

  8. Bettencourt C, Foti SC, Miki Y, Botia J, Chatterjee A, Warner TT, Revesz T, Lashley T, Balazs R, Viré E, Holton JL (2019) White matter DNA methylation profiling reveals deregulation of HIP1, LMAN2, MOBP, and other loci in multiple system atrophy. Acta Neuropathol 139:135–156

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Smith RG, Hannon E, De Jager PL, Chibnik L, Lott SJ, Condliffe D, Smith AR, Haroutunian V, Troakes C, Al-Sarraj S, Bennett DA, Powell J, Lovestone S, Schalkwyk L, Mill J, Lunnon K (2018) Elevated DNA methylation across a 48-kb region spanning the HOXA gene cluster is associated with Alzheimer's disease neuropathology. Alzheimers Dement 14(12):1580–1588

    Article  PubMed  PubMed Central  Google Scholar 

  10. Gasparoni G, Bultmann S, Lutsik P, Kraus TFJ, Sordon S, Vlcek J, Dietinger V, Steinmaurer M, Haider M, Mulholland CB, Arzberger T, Roeber S, Riemenschneider M, Kretzschmar HA, Giese A, Leonhardt H, Walter J (2018) DNA methylation analysis on purified neurons and glia dissects age and Alzheimer's disease-specific changes in the human cortex. Epigenetics Chromatin 11(1):41

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Chuang YH, Paul KC, Bronstein JM, Bordelon Y, Horvath S, Ritz B (2017) Parkinson's disease is associated with DNA methylation levels in human blood and saliva. Genome Med 9(1):76

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Moore K, McKnight AJ, Craig D, O’Neill F (2014) Epigenome-wide association study for Parkinson’s disease. NeuroMolecular Med 16(4):845–855

    Article  CAS  PubMed  Google Scholar 

  13. Weber A, Schwarz SC, Tost J, Trümbach D, Winter P, Busato F, Tacik P, Windhorst AC, Fagny M, Arzberger T, McLean C, van Swieten JC, Schwarz J, Vogt Weisenhorn D, Wurst W, Adhikary T, Dickson DW, Höglinger GU, Müller U (2018) Epigenome-wide DNA methylation profiling in progressive Supranuclear palsy reveals major changes at DLX1. Nat Commun 9(1):2929

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Smith ZD, Chan MM, Mikkelsen TS, Gu H, Gnirke A, Regev A, Meissner A (2012) A unique regulatory phase of DNA methylation in the early mammalian embryo. Nature 484(7394):339–344

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Münzel M, Globisch D, Brückl T, Wagner M, Welzmiller V, Michalakis S, Müller M, Biel M, Carell T (2010) Quantification of the sixth DNA Base Hydroxymethylcytosine in the brain. Angew Chem 49(31):5375–5377

    Article  CAS  Google Scholar 

  16. Spiers H, Hannon E, Schalkwyk LC, Bray NJ, Mill J (2017) 5-hydroxymethylcytosine is highly dynamic across human fetal brain development. BMC Genomics 18(1):738

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Bernstein AI, Lin Y, Street RC, Lin L, Dai Q, Yu L, Bao H, Gearing M, Lah JJ, Nelson PT, He C, Levey AI, Mullé JG, Duan R, Jin P (2016) 5-Hydroxymethylation-associated epigenetic modifiers of Alzheimer's disease modulate tau-induced neurotoxicity. Hum Mol Genet 25(12):2437–2450

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Cheng Y, Li Z, Manupipatpong S, Lin L, Li X, Xu T, Jiang Y-H, Shu Q, Wu H, Jin P (2018) 5-Hydroxymethylcytosine alterations in the human postmortem brains of autism spectrum disorder. Hum Mol Genet 27(17):2955–2964

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kaut O, Kuchelmeister K, Moehl C, Wüllner U (2019) 5-methylcytosine and 5-hydroxymethylcytosine in brains of patients with multiple system atrophy and patients with Parkinson’s disease. J Chem Neuroanat 96:41–48

    Article  CAS  PubMed  Google Scholar 

  20. Rydbirk R, Elfving B, Andersen MD, Langbøl MA, Folke J, Winge K, Pakkenberg B, Brudek T, Aznar S (2017) Cytokine profiling in the prefrontal cortex of Parkinson's disease and multiple system atrophy patients. Neurobiol Dis 106:269–278

    Article  CAS  PubMed  Google Scholar 

  21. Gilman S, Wenning GK, Low PA, Brooks DJ, Mathias CJ, Trojanowski JQ, Wood NW, Colosimo C, Dürr A, Fowler CJ, Kaufmann H, Klockgether T, Lees A, Poewe W, Quinn N, Revesz T, Robertson D, Sandroni P, Seppi K, Vidailhet M (2008) Second consensus statement on the diagnosis of multiple system atrophy. Neurology 71(9):670–676

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects, JAMA 310(20) (2013) 2191–4

  23. R Core Team, R: A Language and Environment for Statistical Computing, https://www.r-project.org/ (2018)

    Google Scholar 

  24. Tian Y, Morris TJ, Webster AP, Yang Z, Beck S, Feber A, Teschendorff AE (2017) ChAMP: updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics 33(24):3982–3984

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, Irizarry RA (2014) Minfi: a flexible and comprehensive bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30(10):1363–1369

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhou W, Laird PW, Shen H (2017) Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes. Nucleic Acids Res 45(4):e22–e22

    PubMed  Google Scholar 

  27. Nordlund J, Bäcklin CL, Wahlberg P, Busche S, Berglund EC, Eloranta M-L, Flaegstad T, Forestier E, Frost B-M, Harila-Saari A, Heyman M, Jónsson ÓG, Larsson R, Palle J, Rönnblom L, Schmiegelow K, Sinnett D, Söderhäll S, Pastinen T, Gustafsson MG, Lönnerholm G, Syvänen A-C (2013) Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia. Genome Biol 14(9):r105

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, Beck S (2013) A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 29(2):189–196

    Article  CAS  PubMed  Google Scholar 

  29. Hastie T, Tibshirani R, Narasimhan B, C. G, impute: Imputation for microarray data, (2019) R package version 1.60.0

  30. Lunnon K, Hannon E, Smith RG, Dempster E, Wong C, Burrage J, Troakes C, Al-Sarraj S, Kepa A, Schalkwyk L, Mill J (2016) Variation in 5-hydroxymethylcytosine across human cortex and cerebellum. Genome Biol 17(1):27

    Article  PubMed  PubMed Central  Google Scholar 

  31. Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Gayther SA, Apostolidou S, Jones A, Lechner M, Beck S, Jacobs IJ, Widschwendter M (2009) An epigenetic signature in peripheral blood predicts active ovarian cancer. PLoS One 4(12):e8274

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Smyth GK (2005) Limma: linear models for microarray data, In: Gentleman R, Carey VJ, Huber W, Irizarry RA, Dudoit S (eds) Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Statistics for Biology and Health. Springer, New York, 397–420

  33. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 57(1):289–300

    Google Scholar 

  34. De Jager PL, Srivastava G, Lunnon K, Burgess J, Schalkwyk LC, Yu L, Eaton ML, Keenan BT, Ernst J, McCabe C, Tang A, Raj T, Replogle J, Brodeur W, Gabriel S, Chai HS, Younkin C, Younkin SG, Zou F, Szyf M, Epstein CB, Schneider JA, Bernstein BE, Meissner A, Ertekin-Taner N, Chibnik LB, Kellis M, Mill J, Bennett DA (2014) Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci 17(9):1156–1163

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Jaffe AE, Murakami P, Lee H, Leek JT, Fallin MD, Feinberg AP, Irizarry RA (2012) Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int J Epidemiol 41(1):200–209

    Article  PubMed  PubMed Central  Google Scholar 

  36. Hansen KD, Timp W, Bravo HC, Sabunciyan S, Langmead B, McDonald OG, Wen B, Wu H, Liu Y, Diep D (2011) Increased methylation variation in epigenetic domains across cancer types. Nat Genet 43(8):768

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Jiao Y, Widschwendter M, Teschendorff AE (2014) A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control. Bioinformatics 30(16):2360–2366

    Article  CAS  PubMed  Google Scholar 

  38. Lizio M, Harshbarger J, Shimoji H, Severin J, Kasukawa T, Sahin S, Abugessaisa I, Fukuda S, Hori F, Ishikawa-Kato S (2015) Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol 16(1):22

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Ienasescu H, Li K, Andersson R, Vitezic M, Rennie S, Chen Y, Vitting-Seerup K, Lagoni E, Boyd M, Bornholdt J, de Hoon MJL, Kawaji H, Lassmann T, Consortium F, Hayashizaki Y, Forrest ARR, Carninci P, Sandelin A (2016) On-the-fly selection of cell-specific enhancers, genes, miRNAs and proteins across the human body using SlideBase. Database (Oxford) 2016:baw144

    Article  Google Scholar 

  40. Ernst J, Kellis M (2012) ChromHMM: automating chromatin-state discovery and characterization. Nat Methods 9(3):215

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A, Meissner A, Kellis M, Marra MA, Beaudet AL, Ecker JR (2010) The NIH roadmap epigenomics mapping consortium. Nat Biotechnol 28(10):1045

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. McLeay RC, Bailey TL (2010) Motif enrichment analysis: a unified framework and an evaluation on ChIP data. BMC Bioinformatics 11(1):165

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Kulakovskiy IV, Vorontsov IE, Yevshin IS, Sharipov RN, Fedorova AD, Rumynskiy EI, Medvedeva YA, Magana-Mora A, Bajic VB, Papatsenko DA (2017) HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis. Nucleic Acids Res 46(D1):D252–D259

    Article  PubMed Central  CAS  Google Scholar 

  44. Turner SD (2018) qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. J Open Source Softw 3(25):731

  45. Masser DR, Stanford DR, Freeman WM (2015) Targeted DNA methylation analysis by next-generation sequencing, J Vis Exp (96)

    Google Scholar 

  46. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55(4):611–622

    Article  CAS  PubMed  Google Scholar 

  47. Wang X, Spandidos A, Wang H, Seed B (2011) PrimerBank: a PCR primer database for quantitative gene expression analysis, 2012 update. Nucleic Acids Res 40(D1):D1144–D1149

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Zhang Z-M, Guan X, Li Y-J, Zhu M-C, Yang X-J, Zou X (2012) HLA class I expressions on peripheral blood mononuclear cells in colorectal Cancer patients. Chin J Cancer Res 24(1):77–82

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Rydbirk R, Folke J, Winge K, Aznar S, Pakkenberg B, Brudek T (2016) Assessment of brain reference genes for RT-qPCR studies in neurodegenerative diseases. Sci Rep 6:37116

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J (2007) qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol 8(2):R19

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Rydbirk R, Elfving B, Folke J, Pakkenberg B, Winge K, Brudek T, Aznar S (2019) Increased prefrontal cortex interleukin-2 protein levels and shift in the peripheral T cell population in progressive supranuclear palsy patients. Sci Rep 9(1):7781

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Barkas N, Petukhov V, Nikolaeva D, Lozinsky Y, Demharter S, Khodosevich K, Kharchenko PV (2019) Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat Methods 16(8):695–698

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kim J-B, Kim SY, Kim BM, Lee H, Kim I, Yun J, Jo Y, Oh T, Jo Y, Chae H-D, Shin DY (2013) Identification of a novel anti-apoptotic E3 ubiquitin ligase that ubiquitinates antagonists of inhibitor of apoptosis proteins SMAC, HtrA2, and ARTS. J Biol Chem 288(17):12014–12021

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Loureiro J, Ploegh HL (2006) Antigen presentation and the ubiquitin-proteasome system in host–pathogen interactions. Adv Immunol 92:225–305

  55. Mitroulis I, Alexaki VI, Kourtzelis I, Ziogas A, Hajishengallis G, Chavakis T (2015) Leukocyte integrins: role in leukocyte recruitment and as therapeutic targets in inflammatory disease. Pharmacol Therapeut 147:123–135

    Article  CAS  Google Scholar 

  56. Hayes JM, Wormald MR, Rudd PM, Davey GP (2016) Fc gamma receptors: glycobiology and therapeutic prospects. J Inflam Res 9:209–219

    Article  CAS  Google Scholar 

  57. Nishimura M, Kuno S, Kaji R, Kawakami H (2005) Influence of a tumor necrosis factor gene polymorphism in Japanese patients with multiple system atrophy. Neurosci Lett 374(3):218–221

    Article  CAS  PubMed  Google Scholar 

  58. Grozdanov V, Bliederhaeuser C, Ruf WP, Roth V, Fundel-Clemens K, Zondler L, Brenner D, Martin-Villalba A, Hengerer B, Kassubek J, Ludolph AC, Weishaupt JH, Danzer KM (2014) Inflammatory dysregulation of blood monocytes in Parkinson's disease patients. Acta Neuropathol 128(5):651–663

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Babu H, Ambikan AT, Gabriel EE, Svensson Akusjärvi S, Palaniappan AN, Sundaraj V, Mupanni NR, Sperk M, Cheedarla N, Sridhar R, Tripathy SP, Nowak P, Hanna LE, Neogi U (2019) Systemic inflammation and the increased risk of Inflamm-aging and age-associated diseases in people living with HIV on long term suppressive antiretroviral therapy. Front Immunol 10:1965

  60. Hofer TP, Zawada AM, Frankenberger M, Skokann K, Satzl AA, Gesierich W, Schuberth M, Levin J, Danek A, Rotter B, Heine GH, Ziegler-Heitbrock L (2015) slan-defined subsets of CD16-positive monocytes: impact of granulomatous inflammation and M-CSF receptor mutation. Blood 126(24):2601–2610

    Article  CAS  PubMed  Google Scholar 

  61. Alturkustani M, Keith J, Hazrati LN, Rademakers R, Ang LC (2015) Pathologic staging of white matter lesions in adult-onset leukoencephalopathy/leukodystrophy with axonal spheroids. J Neuropathol Exp Neurol 74(3):233–240

    Article  CAS  PubMed  Google Scholar 

  62. Sundal C, Fujioka S, Van Gerpen JA, Wider C, Nicholson AM, Baker M, Shuster EA, Aasly J, Spina S, Ghetti B, Roeber S, Garbern J, Tselis A, Swerdlow RH, Miller BB, Borjesson-Hanson A, Uitti RJ, Ross OA, Stoessl AJ, Rademakers R, Josephs KA, Dickson DW, Broderick D, Wszolek ZK (2013) Parkinsonian features in hereditary diffuse leukoencephalopathy with spheroids (HDLS) and CSF1R mutations. Parkinsonism Relat Disord 19(10):869–877

    Article  PubMed  PubMed Central  Google Scholar 

  63. Sharma R, Graff-Radford J, Rademakers R, Boeve BF, Petersen RC, Jones DT (2019) CSF1R mutation presenting as dementia with Lewy bodies. Neurocase 25(1–2):17–20

    Article  PubMed  Google Scholar 

  64. Rademakers R, Baker M, Nicholson AM, Rutherford NJ, Finch N, Soto-Ortolaza A, Lash J, Wider C, Wojtas A, DeJesus-Hernandez M, Adamson J, Kouri N, Sundal C, Shuster EA, Aasly J, MacKenzie J, Roeber S, Kretzschmar HA, Boeve BF, Knopman DS, Petersen RC, Cairns NJ, Ghetti B, Spina S, Garbern J, Tselis AC, Uitti R, Das P, Van Gerpen JA, Meschia JF, Levy S, Broderick DF, Graff-Radford N, Ross OA, Miller BB, Swerdlow RH, Dickson DW, Wszolek ZK (2011) Mutations in the colony stimulating factor 1 receptor (CSF1R) gene cause hereditary diffuse leukoencephalopathy with spheroids. Nat Genet 44(2):200–205

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Salvesen L, Winge K, Brudek T, Agander TK, Lokkegaard A, Pakkenberg B (2017) Neocortical neuronal loss in patients with multiple system atrophy: a stereological study. Cereb Cortex 27(1):400–410

    PubMed  Google Scholar 

  66. Nykjaer CH, Brudek T, Salvesen L, Pakkenberg B (2017) Changes in the cell population in brain white matter in multiple system atrophy. Mov Disord 32(7):1074–1082

    Article  CAS  PubMed  Google Scholar 

  67. Salvesen L, Ullerup BH, Sunay FB, Brudek T, Lokkegaard A, Agander TK, Winge K, Pakkenberg B (2015) Changes in total cell numbers of the basal ganglia in patients with multiple system atrophy - a stereological study. Neurobiol Dis 74:104–113

    Article  PubMed  Google Scholar 

  68. Tada M, Konno T, Tada M, Tezuka T, Miura T, Mezaki N, Okazaki K, Arakawa M, Itoh K, Yamamoto T, Yokoo H, Yoshikura N, Ishihara K, Horie M, Takebayashi H, Toyoshima Y, Naito M, Onodera O, Nishizawa M, Takahashi H, Ikeuchi T, Kakita A (2016) Characteristic microglial features in patients with hereditary diffuse leukoencephalopathy with spheroids. Ann Neurol 80(4):554–565

    Article  CAS  PubMed  Google Scholar 

  69. Brudek T, Winge K, Agander TK, Pakkenberg B (2013) Screening of toll-like receptors expression in multiple system atrophy brains. Neurochem Res 38(6):1252–1259

    Article  CAS  PubMed  Google Scholar 

  70. Drexler SK, Foxwell BM (2010) The role of toll-like receptors in chronic inflammation. Int J Biochem Cell Biol 42(4):506–518

    Article  CAS  PubMed  Google Scholar 

  71. Rusholt EHL, Salvesen L, Brudek T, Tesfay B, Pakkenberg B, Olesen MV (2020) Pathological changes in the cerebellum of patients with multiple system atrophy and Parkinson’s disease – a stereological study, Brain Pathol. https://doi.org/10.1111/bpa.12806

  72. Chang D, Nalls MA, Hallgrimsdottir IB, Hunkapiller J, van der Brug M, Cai F, Kerchner GA, Ayalon G, Bingol B, Sheng M, Hinds D, Behrens TW, Singleton AB, Bhangale TR, Graham RR (2017) A meta-analysis of genome-wide association studies identifies 17 new Parkinson's disease risk loci. Nat Genet 49(10):1511–1516

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Jung M, Pfeifer GP (2015) Aging and DNA methylation. BMC Biol 13:7–7

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Pernille Christensen, Jane Eldahl Jørgensen, and Nanna Michelle Nyvang for assistance during blood sampling, and Claus Ekstrøm for statistical guidance. London Neurodegenerative Disease Brain Bank – funded by the MRC and by Brains for Dementia Research (jointly funded by Alzheimer’s society and Alzheimer’s research UK) provided a part of the samples. This work has been supported by the Lundbeck Foundation, Kai Hansens Fond, Brdr. Hartmanns Fond, Oda og Hans Svenningsens Fond, Hørslev Fonden, Jascha Fonden, Danmodis, the Parkinson Foundation Denmark, the Research Foundation of Bispebjerg-Frederiksberg Hospital, the Danish National Association for Multiple System Atrophy and the French National Agency for Research (ANR) through grants ANR-13-EPIG-0003-05 (EPIPD) and ANR-18-RAR3-0001-01 (MSA-omics). The sponsors were not involved in the study design; the collection, analysis and interpretation of data; writing of the report; or in the decision to submit the article for publication.

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Author notes

  1. Jorg Tost and Susana Aznar Joint senior authorship

Authors and Affiliations

  1. Research Laboratory for Stereology and Neuroscience, Bispebjerg-Frederiksberg Hospital, University Hospital of Copenhagen, Nielsine Nielsens Vej 6B, stair 11B, 2nd floor, DK-2400, Copenhagen, Denmark

    Rasmus Rydbirk, Jonas Folke, Alisha Shahzad Chauhan, Tomasz Brudek, Bente Pakkenberg & Susana Aznar

  2. Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie Francois Jacob, 2 rue Gaston Crémieux, FR-91000, Evry, France

    Rasmus Rydbirk, Florence Busato, Elodie Roché & Jorg Tost

  3. Copenhagen Centre for Translational Research, Bispebjerg-Frederiksberg Hospital, University Hospital of Copenhagen, Nielsine Nielsens Vej 4B, DK-2200 Copenhagen, Denmark

    Rasmus Rydbirk, Jonas Folke, Alisha Shahzad Chauhan, Tomasz Brudek & Susana Aznar

  4. Present address: Biotech Research and Innovation Centre, Faculty of Health, University of Copenhagen, DK-2200 Copenhagen, Denmark

    Rasmus Rydbirk

  5. Department of Neurology, Bispebjerg-Frederiksberg Hospital, University Hospital of Copenhagen, Ebba Lunds Vej 44, DK-2400, Copenhagen, Denmark

    Annemette Løkkegaard & Anne-Mette Hejl

  6. Institute of Clinical Medicine, Faculty of Health, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen, Denmark

    Annemette Løkkegaard & Bente Pakkenberg

  7. Department of Neurology, Odense University Hospital, J.B. Winsløws Vej 4, DK-5000, Odense, Denmark

    Matthias Bode & Morten Blaabjerg

  8. Department of Clinical Research, University of Southern Denmark, DK-5000 Odense, Denmark

    Morten Blaabjerg

  9. Department of Neurology, Aarhus University Hospital, DK-8200 Aarhus, Denmark

    Mette Møller & Erik Hvid Danielsen

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  1. Rasmus Rydbirk
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Contributions

R.R., J.T. and S.A. conceived the research. R.R., J. T, S.A., T.B. and J.F. designed the experiments. R.R., J.F., F,B., and E.R. conducted the experiments. R.R. and F.B. analysed the data. R.R., S.A., J.T. and J.F. interpreted the results. R.R. wrote the main manuscript. R.R., J.F. and F.B. prepared the figures and tables. A.L., A.H., Ma.B., Mo.B., M.M., and E.H.D. included patients for blood sampling. B.P. supplied brain tissue samples. S.A. and J.T. elaborated the manuscript. All authors reviewed the manuscript. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Susana Aznar.

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Supplementary information

Additional file 1.

Online Resource 1: Supplementary Tables.

Additional file 2.

Online Resource 2: Material and Methods.

Additional file 3.

Online Resource 3: Supplementary Figures.

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Rydbirk, R., Folke, J., Busato, F. et al. Epigenetic modulation of AREL1 and increased HLA expression in brains of multiple system atrophy patients. acta neuropathol commun 8, 29 (2020). https://doi.org/10.1186/s40478-020-00908-7

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Acta Neuropathologica Communications

ISSN: 2051-5960

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