Open Access

Regional differences in mitochondrial DNA methylation in human post-mortem brain tissue

Clinical EpigeneticsThe official journal of the Clinical Epigenetics Society20179:47

https://doi.org/10.1186/s13148-017-0337-3

Received: 29 October 2016

Accepted: 30 March 2017

Published: 3 May 2017

Abstract

Background

DNA methylation is an important epigenetic mechanism involved in gene regulation, with alterations in DNA methylation in the nuclear genome being linked to numerous complex diseases. Mitochondrial DNA methylation is a phenomenon that is receiving ever-increasing interest, particularly in diseases characterized by mitochondrial dysfunction; however, most studies have been limited to the investigation of specific target regions. Analyses spanning the entire mitochondrial genome have been limited, potentially due to the amount of input DNA required. Further, mitochondrial genetic studies have been previously confounded by nuclear-mitochondrial pseudogenes. Methylated DNA Immunoprecipitation Sequencing is a technique widely used to profile DNA methylation across the nuclear genome; however, reads mapped to mitochondrial DNA are often discarded. Here, we have developed an approach to control for nuclear-mitochondrial pseudogenes within Methylated DNA Immunoprecipitation Sequencing data. We highlight the utility of this approach in identifying differences in mitochondrial DNA methylation across regions of the human brain and pre-mortem blood.

Results

We were able to correlate mitochondrial DNA methylation patterns between the cortex, cerebellum and blood. We identified 74 nominally significant differentially methylated regions (p < 0.05) in the mitochondrial genome, between anatomically separate cortical regions and the cerebellum in matched samples (N = 3 matched donors). Further analysis identified eight significant differentially methylated regions between the total cortex and cerebellum after correcting for multiple testing. Using unsupervised hierarchical clustering analysis of the mitochondrial DNA methylome, we were able to identify tissue-specific patterns of mitochondrial DNA methylation between blood, cerebellum and cortex.

Conclusions

Our study represents a comprehensive analysis of the mitochondrial methylome using pre-existing Methylated DNA Immunoprecipitation Sequencing data to identify brain region-specific patterns of mitochondrial DNA methylation.

Keywords

5-mC 5-Methylcytosine Blood Brain DNA methylation Epigenetics MeDIP-seq Mitochondria NUMTs

Introduction

Mitochondria are unique organelles in that they have their own circular genome, approximately 16.6 kb in size [1]. Mitochondrial DNA (mtDNA) consists of 37 genes, 22 encoding for transfer RNAs (tRNAs), two for ribosomal RNAs (rRNAs) and 13 encoding for proteins important in the electron transport chain. Each of these 13 proteins are directly involved in the regulation of cellular respiration, generating the majority of ATP required for the process. However, mitochondria have an array of other important cellular roles such as calcium homeostasis [2] and neural stem cell differentiation [3]. As such, abnormal mitochondrial function, dynamics and trafficking have been associated with a number of brain disorders including Alzheimer’s disease [4, 5], schizophrenia [6], bipolar disorder [7] and major depressive disorder [8].

Epigenetic processes mediate the reversible regulation of gene expression, occurring independently of DNA sequence variation, acting principally through chemical modifications to DNA and nucleosomal histone proteins and orchestrate a diverse range of important physiological functions. DNA methylation is the best characterized and most stable epigenetic modification modulating the transcription of mammalian genomes and, because it can be robustly assessed using existing genomic DNA resources, is the focus of most human epidemiological epigenetic research to date [9]. The most widely used method for epigenome-wide analysis of DNA methylation is the Illumina 450K methylation array, and a number of studies have recently shown differential DNA methylation of the nuclear genome (ncDNA), between different tissue types [1012] and also in a range of complex diseases, from brain disorders such as Alzheimer’s disease [1315] and schizophrenia [16, 17], to systemic diseases such as type 2 diabetes [18] and Crohn’s disease [19]. However, with no representation of the mitochondrial genome on this platform, as well as a lack of analysis on other genome-wide platforms, the role of mtDNA methylation has been largely neglected [20, 21].

Since the identification of 5-methylcytosine (5-mC) in mitochondria, research into mtDNA methylation as an independent and potentially relevant mark has received more regular attention [22, 23]. However, most research is either focussed on low resolution, global DNA methylation, or candidate gene DNA methylation changes using techniques such as bisulfite pyrosequencing [20]. These recent publications have indicated that differences in mtDNA methylation are present in a variety of different phenotypes [2429] and may have potential utility as a biomarker [30]. In addition, a recent study has explored the use of Methylated DNA Immunoprecipitation Sequencing (MeDIP-seq) to investigate changes in mtDNA methylation across 39 cell lines and tissues from publicly available data [31]. At present, genome-wide sequencing technologies have not yet been used to interrogate alterations in the mtDNA methylome across tissues in the same individuals.

A high proportion of current, publicly available, genome-wide DNA methylation data has been generated through the use of MeDIP-seq, a method designed to interrogate genome-wide changes in methylation at high throughput and low cost [32]. However, given the presence of nuclear-mitochondrial pseudogenes (NUMTs), regions of the nuclear genome that share a high sequence homology with their mitochondrial paralogue [33, 34], mitochondrial reads are often discarded from further analysis. The development of bioinformatic pipelines to investigate regions of differential mtDNA methylation from whole genome data would provide a novel way in which to interrogate the mtDNA methylome in publicly available data. Here, we control for the presence of NUMTs in a previously published MeDIP-seq dataset, to investigate differential DNA methylation across the mitochondrial genome in human post-mortem brain samples.

Results

MtDNA methylation patterns are correlated between the cortex, cerebellum and blood

To date, no study has investigated differences in mtDNA methylation across different matched regions of human brain and blood samples. Our sample (Table 1) consisted of MeDIP-seq data from three individuals, free of any neuropathology and neuropsychiatric disease, for five different regions of the cortex (Brodmann areas (BA) 8, 9 and 10, superior temporal gyrus (STG) and entorhinal cortex (ECX)), the cerebellum (CER) and pre-mortem blood [35]. Given that MeDIP-seq data has been generated from standardly extracted total genomic DNA and thus contains a mixture of ncDNA and mtDNA [36], we initially controlled for regions of high sequence homology between the two genomes within our data by realigning mtDNA reads to a series of custom reference genomes using an in-house pipeline (see the Methods section) to specifically analyze mtDNA methylation (Fig. 1). Briefly, after an initial alignment to the GRCH37 reference genome using BWA, uniquely mapped reads were extracted and aligned to a custom GRCH37 reference genome not containing the mitochondrial sequence. Reads that did not map to this custom genome were found to share less homology with the nuclear genome and were taken forward and realigned once more to the full reference genome. Initially, we were interested to investigate whether changes in mtDNA across the mitochondrial genome were highly correlated between different tissue types. Using principal component analysis (PCA), we found that mtDNA methylation patterns are highly correlated between different cortical regions (r > 0.99, p < 2.2E−16), with a slightly weaker correlation between the cerebellum and cortex (r > 0.97, p < 2.2E−16) (Fig. 2). Due to the small number of blood samples available, deriving a significance level for the correlations between the cerebellum and blood could not be made. Instead, in an attempt to explore the similarity between matched blood and cerebellum samples, the direction of differential methylation with respect to the cortex was used. Here, we found that 93.1% of the windows analyzed in the cerebellum and blood had the same direction of methylation difference with respect to the cortex, further suggesting a strong correlation between the two tissue types.
Table 1

Demographic information

Individual

Age at death (years)

Age at bloods sampled (years)

Post-mortem delay (hours)

Gender

1

82

79

43

Female

2

92

N/A

17

Female

3

78

78

10

Male

MeDIP-seq data was available from post-mortem brain samples obtained from three individuals free of any neuropathology and neuropsychiatric disease. Data was available for five different regions of the cortex (Brodmann areas (BA) 8, 9 and 10, superior temporal gyrus (STG), entorhinal cortex (ECX), the cerebellum (CER) and pre-mortem blood (BLD). MeDIP-seq data was available for all individuals from cortical and cerebellar samples; however, blood MeDIP-seq data was not available for individual 2. Data is freely available to download from http://epigenetics.iop.kcl.ac.uk/brain

Fig. 1

Overview of the analysis pipeline

Fig. 2

MtDNA methylation patterns are correlated between the cortex, cerebellum and blood. Samples were ordered based upon the similarity of their principal components, RPKM values, with r calculated for the correlations between each tissue. BLD blood, BA8 Brodmann area 8, BA9 Brodmann area 9, BA10 Brodmann area 10, CER cerebellum, CTX cortex, ECX entorhinal cortex, STG superior temporal gyrus

Differentially methylated regions of the mitochondrial genome can be identified between anatomically distinct cortical regions and the cerebellum

Having identified correlated mtDNA methylation patterns across different brain regions, we were interested to investigate whether we could identify differentially methylated regions (DMRs) in the mitochondrial genome between different regions of the cortex and cerebellum. To identify such tissue-specific DMRs within the mitochondrial genome, paired t tests were performed across matched cortical and cerebellum samples at 100 bp windows across the mitochondrial genome (see the Methods section). In total, we identified 74 nominally significant DMRs (p < 0.05) between the five individual cortical regions and the cerebellum (Table 2; Fig. 3). Of these DMRs, seven (Table 2, bold face) were found to be present across all prefrontal cortex areas (BA8, BA9, BA10). Furthermore, the direction of methylation difference was maintained in all Brodmann area regions, with three conserved regions of hypomethylation and four conserved regions of hypermethylation, with respect to the cerebellum. Furthermore, four of the seven conserved regions were adjacent to each other within the mitochondrial displacement loop (D-Loop) (16201–16600 bp), a region associated with gene transcription and DNA replication.
Table 2

List of DMRs identified between five anatomically discreet cortical regions and cerebellum

Start (bp)

Stop (bp)

Gene(s)

BA8

BA9

BA10

EC

STG

p value

Δ RPKM

p value

Δ RPKM

p value

Δ RPKM

p value

Δ RPKM

p value

Δ RPKM

1

100

D-Loop

-

-

-

-

-

-

-

-

-

-

101

200

D-Loop

-

-

-

-

-

-

-

-

-

-

201

300

D-Loop

-

-

-

-

-

-

-

-

-

-

301

400

D-Loop

-

-

-

-

-

-

-

-

-

-

401

500

D-Loop

-

-

-

-

-

-

-

-

-

-

501

600

MT-TF

-

-

-

-

-

-

-

-

-

-

601

700

MT-TF/MT-RNR1

-

-

-

-

-

-

-

-

-

-

701

800

MT-RNR1

-

-

-

-

-

-

3.21E−02

79156

-

-

801

900

MT-RNR1

4.31E−02

94246

2.93E−02

−43592

-

-

-

-

1.79E−02

95257

901

1000

MT-RNR1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

1001

1100

MT-RNR1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

1101

1200

MT-RNR1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

1201

1300

MT-RNR1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

1301

1400

MT-RNR1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

1401

1500

MT-RNR1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

1501

1600

MT-RNR1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

1601

1700

MT-RNR1/MT-TV/MT-RNR2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

1701

1800

MT-RNR2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

1801

1900

MT-RNR2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

1901

2000

MT-RNR2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

2001

2100

MT-RNR2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

2101

2200

MT-RNR2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

2201

2300

MT-RNR2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

2301

2400

MT-RNR2

2.54E02

−97351

3.20E02

−29509

4.31E02

−82048

-

-

-

-

2401

2500

MT-RNR2

8.90E−03

70950

-

-

-

-

-

-

-

-

2501

2600

MT-RNR2

3.30E−03

51937

-

-

-

-

-

-

-

-

2601

2700

MT-RNR2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

2701

2800

MT-RNR2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

2801

2900

MT-RNR2

-

-

-

-

-

-

-

-

-

-

2901

3000

MT-RNR2

-

-

-

-

-

-

-

-

-

-

3001

3100

MT-RNR2

-

-

-

-

-

-

-

-

-

-

3101

3200

MT-RNR2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

3201

3300

MT-RNR2/MT-TL

-

-

-

-

-

-

2.09E−02

242424

-

-

3301

3400

MT-TL1/MT-ND1

-

-

-

-

-

-

-

-

-

-

3401

3500

MT-ND1

-

-

1.54E−02

864685

-

-

-

-

-

-

3501

3600

MT-ND1

4.41E−02

1180914

-

-

1.72E−02

226066

-

-

-

-

3601

3700

MT-ND1

4.73E02

1250436

4.52E02

1228936

3.73E02

1183681

-

-

-

-

3701

3800

MT-ND1

-

-

-

-

2.16E−02

20818

-

-

-

-

3801

3900

MT-ND1

-

-

-

-

-

-

-

-

-

-

3901

4000

MT-ND1

-

-

-

-

-

-

-

-

-

-

4001

4100

MT-ND1

2.41E−02

−39498

1.55E−02

−38704

-

-

4.68E−02

−49753

1.56E−02

243767

4101

4200

MT-ND1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

4201

4300

MT-ND1/MT-TI

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

4301

4400

MT-TI/MT-TQ

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

4401

4500

MT-TM/MT-ND2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

4501

4600

MT-ND2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

4601

4700

MT-ND2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

4701

4800

MT-ND2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

4801

4900

MT-ND2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

4901

5000

MT-ND2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

5001

5100

MT-ND2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

5101

5200

MT-ND2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

5201

5300

MT-ND2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

5301

5400

MT-ND2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

5401

5500

MT-ND2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

5501

5600

MT-ND2/MT-TW/MT-TA

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

5601

5700

MT-TA/MT-TN

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

5701

5800

MT-TN/MT-TC

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

5801

5900

MT-TC/MT-TY

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

5901

6000

MT-CO1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

6001

6100

MT-CO1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

6101

6200

MT-CO1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

6201

6300

MT-CO1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

6301

6400

MT-CO1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

6401

6500

MT-CO1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

6501

6600

MT-CO1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

6601

6700

MT-CO1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

6701

6800

MT-CO1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

6801

6900

MT-CO1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

6901

7000

MT-CO1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

7001

7100

MT-CO1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

7101

7200

MT-CO1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

7201

7300

MT-CO1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

7301

7400

MT-CO1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

7401

7500

MT-CO1/MT-TS1

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

7501

7600

MT-TS1/MT-TD/MT-CO2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

7601

7700

MT-CO2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

7701

7800

MT-CO2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

7801

7900

MT-CO2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

7901

8000

MT-CO2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

8001

8100

MT-CO2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

8101

8200

MT-CO2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

8201

8300

MT-CO2/MT-TK

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

8301

8400

MT-TK/MT-ATP8

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

8401

8500

MT-ATP8

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

8501

8600

MT-ATP8/MT-ATP6

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

8601

8700

MT-ATP6

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

8701

8800

MT-ATP6

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

8801

8900

MT-ATP6

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

8901

9000

MT-ATP6

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

9001

9100

MT-ATP6

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

9101

9200

MT-ATP6

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

9201

9300

MT-ATP6/MT-CO3

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

9301

9400

MT-CO3

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

9401

9500

MT-CO3

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

9501

9600

MT-CO3

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

9601

9700

MT-CO3

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

9701

9800

MT-CO3

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

9801

9900

MT-CO3

-

-

-

-

-

-

-

-

-

-

9901

10000

MT-CO3/MT-TG

-

-

-

-

-

-

-

-

-

-

10001

10100

MT-TG/MT-ND3

-

-

-

-

-

-

-

-

-

-

10101

10200

MT-ND3

-

-

-

-

-

-

-

-

-

-

10201

10300

MT-ND3

4.68E−02

−662126

-

-

-

-

3.14E−02

−591067

7.30E−03

−57012

10301

10400

MT-ND3/MT-TR/MT-ND4L

3.61E02

−132719

3.61E02

−135759

4.02E02

−115364

1.40E−02

−73063

3.36E−02

−105106

10401

10500

MT-ND4L

-

-

4.68E−02

−71246

-

-

-

-

-

-

10501

10600

MT-ND4L

-

-

-

-

-

-

-

-

-

-

10601

10700

MT-ND4L

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

10701

10800

MT-ND4L/MT-ND4

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

10801

10900

MT-ND4

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

10901

11000

MT-ND4

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

11001

11100

MT-ND4

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

11101

11200

MT-ND4

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

11201

11300

MT-ND4

-

-

1.94E−02

−68903

1.84E−02

−79625

4.71E−02

58742

-

-

11301

11400

MT-ND4

-

-

4.78E−02

−139847

-

-

-

-

-

-

11401

11500

MT-ND4

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

11501

11600

MT-ND4

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

11601

11700

MT-ND4

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

11701

11800

MT-ND4

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

11801

11900

MT-ND4

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

11901

12000

MT-ND4

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

12001

12100

MT-ND4

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

12101

12200

MT-ND4/MT-TH

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

12201

12300

MT-TS2/MT-TL2

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

12301

12400

MT-TL2/MT-ND5

4.71E−02

1787

-

−197592

-

-

-

-

-

-

12401

12500

MT-ND5

-

-

-

-

-

-

-

-

-

-

12501

12600

MT-ND5

-

-

-

-

-

-

-

-

-

-

12601

12700

MT-ND5

-

-

-

-

-

-

-

-

-

-

12701

12800

MT-ND5

2.05E−02

−5702

-

-

4.58E−02

−388706

-

-

4.36E−02

−394978

12801

12900

MT-ND5

2.26E−02

−89668

-

-

-

-

-

-

-

-

12901

13000

MT-ND5

-

-

3.10E−02

−130016

-

-

-

-

-

-

13001

13100

MT-ND5

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

13101

13200

MT-ND5

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

13201

13300

MT-ND5

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

13301

13400

MT-ND5

1.48E−02

−175917

-

-

9.80E−03

−144949

3.40E−02

−133694

-

-

13401

13500

MT-ND5

3.64E−02

−104010

-

-

3.40E−02

−81264

-

-

-

-

13501

13600

MT-ND5

-

-

-

-

-

-

-

-

-

-

13601

13700

MT-ND5

-

-

-

-

-

-

-

-

-

-

13701

13800

MT-ND5

1.31E−02

−422610

-

-

1.20E−03

3714

-

-

-

-

13801

13900

MT-ND5

-

-

3.72E−02

708761

2.59E−02

123249

-

-

-

-

13901

14000

MT-ND5

-

-

-

-

2.17E−02

−75118

-

-

-

-

14001

14100

MT-ND5

-

-

-

-

-

-

-

-

-

-

14101

14200

MT-ND5/MT-ND6

-

-

-

-

-

-

-

-

4.18E−02

−534766

14201

14300

MT-ND6

-

-

-

-

-

-

-

-

-

-

14301

14400

MT-ND6

-

-

-

-

-

-

-

-

-

-

14401

14500

MT-ND6

-

-

-

-

-

-

-

-

-

-

14501

14600

MT-ND6

-

-

-

-

-

-

-

-

4.86E−02

−82767

14601

14700

MT-ND6/MT-TE

-

-

-

-

-

-

-

-

-

-

14701

14800

MT-TE/MT-CYB

-

-

-

-

-

-

-

-

-

-

14801

14900

MT-CYB

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

14901

15000

MT-CYB

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

15001

15100

MT-CYB

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

15101

15200

MT-CYB

-

-

-

-

-

-

-

-

-

-

15201

15300

MT-CYB

-

-

-

-

-

-

1.20E−02

254183

-

-

15301

15400

MT-CYB

-

-

-

-

-

-

-

-

-

-

15401

15500

MT-CYB

4.38E−02

10628

-

-

2.10E−02

−554527

-

-

3.28E−02

−554225

15501

15600

MT-CYB

3.73E−02

−671707

-

-

1.23E−02

−685630

3.82E−02

9301

3.51E−02

−695515

15601

15700

MT-CYB

-

-

-

-

4.33E−02

−746270

-

-

-

-

15701

15800

MT-CYB

-

-

-

-

-

-

-

-

-

-

15801

15900

MT-CYB/MT-TT

-

-

-

-

-

-

-

-

-

-

15901

16000

MT-TT/MT-TP

-

-

-

-

-

-

-

-

-

-

16001

16100

MT-TP

-

-

-

-

-

-

-

-

-

-

16101

16200

D-Loop

-

-

3.40E−03

3014

-

-

-

-

-

-

16201

16300

D-Loop

4.64E02

380407

2.50E02

1117943

3.68E02

296890

-

-

-

-

16301

16400

D-Loop

3.65E02

−941720

1.38E02

−112453

2.73E02

−177146

-

-

-

-

16401

16500

D-Loop

4.29E02

444051

2.20E02

1151402

1.68E02

313156

-

-

-

-

16501

16600

D-Loop

3.71E02

784572

2.10E02

764994

2.46E02

836662

-

-

-

-

Shown is the location of the DMR within the mitochondrial genome (ChrM) (based on GENCODE), the gene(s) residing within the 100 bp window, and p value from paired t tests between each of the five cortical regions: Brodmann areas 8, 9 and 10 (BA8, BA9, BA10), entorhinal cortex (ECX) and superior temporal gyrus (STG) compared to the cerebellum (CER). Results are displayed in order of genomic position. RPKM and corresponding p values are shown for windows if p < 0.05. Key: - denotes data not significant (p > 0.05); ND denotes not determined as the window was not included in analysis due to removal in NUMT pipeline. Results shown in bold represent those found to be present across all prefrontal cortex areas (BA8, BA9, BA10)

Fig. 3

DNA methylation differences are seen in the mitochondrial genome between brain regions and blood. Average raw RPKM values across the mitochondrial genome for each individual cortical brain region alongside matched blood and cerebellum samples are shown in the top panel, with gene positions downloaded from GENCODE shown in the middle panel. For each 100 bp window, paired t tests were performed to compare each cortical brain region and the cerebellum, with -log10 (p) shown in the bottom panel. BLD blood, BA8 Brodmann area 8, BA9 Brodmann area 9, BA10 Brodmann area 10, CER cerebellum, CTX cortex, ECX entorhinal cortex, RPKM reads per kilobase of transcript per million mapped reads, STG superior temporal gyrus. Red dashed line denotes the Bonferroni significance, whilst blue dashed line denotes p < 0.05 in the lower panel

A number of differentially methylated regions in mtDNA can be observed between the cortex and cerebellum

We were also interested to see whether total cortical tissue was significantly different to matched cerebellum samples. Given the paired nature of the different anatomical regions of the cortex, we used a multilevel mixed effects model to compare total cortex to cerebellum (see the Methods section). This analysis revealed 48 nominally significant (p < 0.05) windows (Table 3; Fig. 4), of which eight passed the Bonferroni correction (Table 3, bold face). Interestingly, three of these eight were adjacent to each other, lying between 10301 and 10600 bp and covering MT-ND3/MT-ND4L and MT-TR. We also saw a Bonferroni significant difference in DNA methylation in the D-Loop, where we earlier noted DNA methylation changes across all three Brodmann area regions.
Table 3

List of DMRs identified between total cortex and cerebellum

Start (bp)

Stop (bp)

Gene(s)

p value

1

100

D-Loop

-

101

200

D-Loop

-

201

300

D-Loop

-

301

400

D-Loop

-

401

500

D-Loop

7.99E−03

501

600

MT-TF

4.49E−03

601

700

MT-TF/MT-RNR1

1.16E−02

701

800

MT-RNR1

3.22E−03

801

900

MT-RNR1

1.91E04

901

1000

MT-RNR1

ND

1001

1100

MT-RNR1

ND

1101

1200

MT-RNR1

ND

1201

1300

MT-RNR1

ND

1301

1400

MT-RNR1

ND

1401

1500

MT-RNR1

ND

1501

1600

MT-RNR1

ND

1601

1700

MT-RNR1/MT-TV/MT-RNR2

ND

1701

1800

MT-RNR2

ND

1801

1900

MT-RNR2

ND

1901

2000

MT-RNR2

ND

2001

2100

MT-RNR2

ND

2101

2200

MT-RNR2

ND

2201

2300

MT-RNR2

ND

2301

2400

MT-RNR2

8.10E−03

2401

2500

MT-RNR2

7.50E−03

2501

2600

MT-RNR2

5.14E−03

2601

2700

MT-RNR2

ND

2701

2800

MT-RNR2

ND

2801

2900

MT-RNR2

-

2901

3000

MT-RNR2

-

3001

3100

MT-RNR2

-

3101

3200

MT-RNR2

-

3201

3300

MT-RNR2/MT-TL

1.51E−03

3301

3400

MT-TL1/MT-ND1

-

3401

3500

MT-ND1

1.37E−02

3501

3600

MT-ND1

4.27E−03

3601

3700

MT-ND1

5.56E−03

3701

3800

MT-ND1

8.21E−03

3801

3900

MT-ND1

-

3901

4000

MT-ND1

-

4001

4100

MT-ND1

3.07E06

4101

4200

MT-ND1

-

4201

4300

MT-ND1/MT-TI

ND

4301

4400

MT-TI/MT-TQ

ND

4401

4500

MT-TM/MT-ND2

ND

4501

4600

MT-ND2

ND

4601

4700

MT-ND2

ND

4701

4800

MT-ND2

ND

4801

4900

MT-ND2

ND

4901

5000

MT-ND2

ND

5001

5100

MT-ND2

ND

5101

5200

MT-ND2

ND

5201

5300

MT-ND2

ND

5301

5400

MT-ND2

ND

5401

5500

MT-ND2

ND

5501

5600

MT-ND2/MT-TW/MT-TA

ND

5601

5700

MT-TA/MT-TN

ND

5701

5800

MT-TN/MT-TC

ND

5801

5900

MT-TC/MT-TY

ND

5901

6000

MT-CO1

ND

6001

6100

MT-CO1

ND

6101

6200

MT-CO1

ND

6201

6300

MT-CO1

ND

6301

6400

MT-CO1

ND

6401

6500

MT-CO1

ND

6501

6600

MT-CO1

ND

6601

6700

MT-CO1

ND

6701

6800

MT-CO1

ND

6801

6900

MT-CO1

ND

6901

7000

MT-CO1

ND

7001

7100

MT-CO1

ND

7101

7200

MT-CO1

ND

7201

7300

MT-CO1

ND

7301

7400

MT-CO1

ND

7401

7500

MT-CO1/MT-TS1

ND

7501

7600

MT-TS1/MT-TD/MT-CO2

ND

7601

7700

MT-CO2

ND

7701

7800

MT-CO2

ND

7801

7900

MT-CO2

ND

7901

8000

MT-CO2

ND

8001

8100

MT-CO2

ND

8101

8200

MT-CO2

ND

8201

8300

MT-CO2/MT-TK

ND

8301

8400

MT-TK/MT-ATP8

ND

8401

8500

MT-ATP8

ND

8501

8600

MT-ATP8/MT-ATP6

ND

8601

8700

MT-ATP6

ND

8701

8800

MT-ATP6

ND

8801

8900

MT-ATP6

ND

8901

9000

MT-ATP6

ND

9001

9100

MT-ATP6

ND

9101

9200

MT-ATP6

ND

9201

9300

MT-ATP6/MT-CO3

ND

9301

9400

MT-CO3

ND

9401

9500

MT-CO3

ND

9501

9600

MT-CO3

ND

9601

9700

MT-CO3

ND

9701

9800

MT-CO3

ND

9801

9900

MT-CO3

-

9901

10000

MT-CO3/MT-TG

-

10001

10100

MT-TG/MT-ND3

1.81E−02

10101

10200

MT-ND3

1.39E−02

10201

10300

MT-ND3

3.53E04

10301

10400

MT-ND3/MT-TR/MT-ND4L

1.19E05

10401

10500

MT-ND4L

2.61E04

10501

10600

MT-ND4L

9.05E−04

10601

10700

MT-ND4L

ND

10701

10800

MT-ND4L/MT-ND4

ND

10801

10900

MT-ND4

ND

10901

11000

MT-ND4

ND

11001

11100

MT-ND4

ND

11101

11200

MT-ND4

ND

11201

11300

MT-ND4

8.86E05

11301

11400

MT-ND4

1.65E−03

11401

11500

MT-ND4

ND

11501

11600

MT-ND4

ND

11601

11700

MT-ND4

ND

11701

11800

MT-ND4

ND

11801

11900

MT-ND4

ND

11901

12000

MT-ND4

ND

12001

12100

MT-ND4

ND

12101

12200

MT-ND4/MT-TH

ND

12201

12300

MT-TS2/MT-TL2

ND

12301

12400

MT-TL2/MT-ND5

-

12401

12500

MT-ND5

-

12501

12600

MT-ND5

-

12601

12700

MT-ND5

-

12701

12800

MT-ND5

1.81E−03

12801

12900

MT-ND5

1.05E−02

12901

13000

MT-ND5

8.23E−03

13001

13100

MT-ND5

ND

13101

13200

MT-ND5

ND

13201

13300

MT-ND5

ND

13301

13400

MT-ND5

1.44E−03

13401

13500

MT-ND5

9.13E−04

13501

13600

MT-ND5

1.61E−02

13601

13700

MT-ND5

3.89E−02

13701

13800

MT-ND5

2.77E04

13801

13900

MT-ND5

2.80E−03

13901

14000

MT-ND5

1.88E−02

14001

14100

MT-ND5

9.31E−03

14101

14200

MT-ND5/MT-ND6

-

14201

14300

MT-ND6

-

14301

14400

MT-ND6

1.04E−02

14401

14500

MT-ND6

1.99E−02

14501

14600

MT-ND6

2.26E−02

14601

14700

MT-ND6/MT-TE

3.82E−03

14701

14800

MT-TE/MT-CYB

1.92E−02

14801

14900

MT-CYB

ND

14901

15000

MT-CYB

ND

15001

15100

MT-CYB

ND

15101

15200

MT-CYB

3.30E−02

15201

15300

MT-CYB

-

15301

15400

MT-CYB

-

15401

15500

MT-CYB

8.52E−04

15501

15600

MT-CYB

7.43E−04

15601

15700

MT-CYB

1.16E−02

15701

15800

MT-CYB

2.24E−02

15801

15900

MT-CYB/MT-TT

-

15901

16000

MT-TT/MT-TP

-

16001

16100

MT-TP

-

16101

16200

D-Loop

2.23E−03

16201

16300

D-Loop

5.02E04

16301

16400

D-Loop

1.84E−03

16401

16500

D-Loop

1.12E−03

16501

16600

D-Loop

2.20E−03

Shown is the location of the DMR within ChrM (based on GENCODE), the gene(s) residing within the 100 bp window, and p value from a multilevel mixed effects model. Results are displayed in order of genomic position. RPKM and corresponding p values are shown for windows if p < 0.05. Key: - denotes data not significant (p > 0.05); ND denotes not determined as the window was not included in analysis due to removal in NUMT pipeline; bold denotes windows that reached our Bonferroni significant threshold of p < 7.04E−04

Fig. 4

DNA methylation differences are seen in the mitochondrial genome between the cerebellum and cortex. RPKM values in the total cortex and cerebellum across the mitochondrial genome are shown in the top panel, with gene positions downloaded from GENCODE shown in the middle panel. For each 100 bp window, paired t tests were performed to compare the cortex to the cerebellum, with -log10 (p) shown in the bottom panel. BLD blood, BA8 Brodmann area 8, BA9 Brodmann area 9, BA10 Brodmann area 10, CER cerebellum, CTX cortex, ECX entorhinal cortex, RPKM reads per kilobase of transcript per million mapped reads, STG superior temporal gyrus. Red dashed line denotes the Bonferroni significance, whilst blue dashed line denotes p < 0.05 in the lower panel

MtDNA methylation patterns can distinguish between tissue types

Although we have shown that mtDNA methylation patterns are highly similar between distinct anatomical regions of the human brain and blood, we were also interested to identify whether mtDNA methylation patterns could distinguish between these tissue types. Through unsupervised hierarchical clustering, we showed that average mtDNA methylation patterns can segregate these tissues (Fig. 5a). Importantly, ncDNA methylation profiles in the same samples have also been previously shown to separate the cortex, cerebellum and blood [35]. Interestingly, when we performed unsupervised hierarchical clustering on the individual samples, we found that, in most cases, intra-individual differences across tissue types are greater than inter-individual differences within each tissue type, as the cortex, cerebellum and blood samples clustered with their own tissue type, respectively (Fig. 5b).
Fig. 5

MtDNA methylation patterns can distinguish between tissue types. a Average RPKM values for each cortical brain region, cerebellum and blood samples were clustered based upon the Euclidean distance, identifying two major clusters; the cortex and blood-cerebellum. b When clustering RPKM values in the individual samples from the cortex, cerebellum and blood, we observed that individual cortex samples clustered together, whilst cerebellum and blood samples formed separate clusters. This highlights that tissue-specific differences between the cortex, cerebellum and blood are greater than intra-individual variability within a tissue. BLD blood, BA8 Brodmann area 8, BA9 Brodmann area 9, BA10 Brodmann area 10, CER cerebellum, CTX cortex, ECX entorhinal cortex, RPKM reads per kilobase of transcript per million mapped reads, STG superior temporal gyrus

Discussion

The availability of publicly available epigenomic data provides a great resource for mitochondrial epigenetics, a field that is relatively nascent and has yet to be thoroughly investigated in a range of complex diseases. Here, we present evidence that mtDNA methylation patterns across mtDNA are brain region specific. However, data such as that presented here is confounded by a lack of isolation of mtDNA prior to antibody enrichment and sequencing. As such, the potential of including NUMTs in datasets derived from data generated using total genomic DNA could lead to misleading results. Here, we controlled for regions of high sequence homology between the nuclear and mitochondrial genomes. However, this approach is likely over-conservative and does lead to the generation of a somewhat truncated consensus sequence. PCA of the mitochondrial epigenome after corrections for nuclear homology was able to separate individuals belonging to the three main tissue types, the blood, cortex and cerebellum based on mtDNA methylation variation among tissue types. This tissue specificity is further highlighted by the identification of eight DMRs that pass the Bonferroni correction for multiple testing between total cortex and cerebellum. MtDNA methylation has been shown to be cell line dependent in the past. [31] Although overall DNA methylation levels were low in all tissues, it is worth noting that the study was performed on non bisulfite-treated DNA. As such, the low percentage of mtDNA methylation is not a pitfall due to a lack of a total bisulfite treatment efficiency. One limitation of the current study is the unavailability of publicly available MeDIP-seq datasets of matched cortical and cerebellum tissue from other cohorts for validation purposes. Future work would aim to replicate our findings in additional study cohorts and also to investigate the relationship between mitochondrial DNA methylation and gene expression.

Despite a number of nominally significant windows being identified between each individual cortical region and the cerebellum, these did not pass the Bonferroni correction, although it is likely this method is too stringent. Nevertheless, the conservation of seven nominally significant windows across each Brodmann area is interesting to note. Four of these windows lie adjacent to each other and correspond to the mitochondrial D-Loop, a region containing the only two mitochondrial promoters which is typically associated with gene transcription and DNA replication. However, one limitation of this study is owed to the use of antibody-based enrichment, resulting in the analysis being limited to a window-based approach. Despite this, studies of the nuclear genome have shown high correlation between window-based approaches and, more sensitive, single-site assays such as the Illumina 450K beadarray [32]. However, given the small size of the mitochondrial genome and that 23 of the 37 genes present in the genome are below 100 bp in size, this window-based approach may not be the most appropriate for future studies designed to specifically assess mtDNA methylation as it can result in a window intersecting two genes in the polycistronic transcript.

Conclusions

This method provides a conservative approach to determine mtDNA methylation across the genome for data previously generated using next-generation sequencing approaches such as MeDIP-seq. Its conservative nature reduces the risk of the inclusion of NUMTs in the final analysis of whole genome data but may also lead to the inclusion of false negatives as well as potential gaps in the reference sequence. As such, it is best suited to analyzing previously generated whole genome data and is not a replacement for the isolation of mitochondrial DNA [36] prior to targeted methylation studies, which would be the optimal approach for investigating mitochondrial epigenomics. However, our method has allowed the identification of novel brain-region-specific DMRs in a previously generated publicly available dataset. Furthermore, the identification of brain region-specific mtDNA methylation patterns across the mitochondrial epigenome suggests the importance of a focussed, tissue-specific study design when investigating mtDNA methylation. As previously discussed, one caveat when utilizing MeDIP-seq data is the segregation of data into neighbouring windows, meaning that determining the exact corresponding gene of a DMR is difficult and, as such, future studies should aim to sequence the mitochondrial DNA methylome at single-base resolution to address this.

Methods

Data collection

We utilized publicly available MeDIP-seq data from Davies et al. [35]. In brief, this data was generated using 5 μg fragmented gDNA, which, following end repair <A> base addition and adaptor ligation, was immunoprecipitated using an anti-5-mC antibody (Diagenode, Liège, Belgium). MeDIP DNA was purified and then amplified using adaptor-mediated PCR, with DNA fragments between 220 and 320 bp subjected to highly parallel 50 bp paired-end sequencing on the Illumina Hi-Seq platform. The paired-end, raw fasta files were provided by the authors and quality checked using FastQC. Sample information is provided in Table 1.

Quality control and NUMT exclusion

Fasta files were subjected to adaptor and Phred score (q < 20) trimming. In an attempt to remove any potential contamination of possible NUMTs, multiple alignments to the reference genome were undertaken. Paired fasta files were aligned to GRCH37 using BWA. Unique and mapped reads aligning to the mitochondria were then re-mapped to a custom GRCH37 reference without the mitochondrial chromosome. Reads not mapping to the custom reference were then taken forward and realigned to the full GRCH37 reference to eliminate the possibility of homologous regions mapping falsely to the mitochondrial genome (Fig. 1). All alignments were carried out using BWA mem and default settings. Reads per kilobase of transcript per million mapped reads (RPKM) values for each sample were calculated using the MEDIPS package [37]. Methylation was averaged across 100 bp non-overlapping windows (default parameter setting in MEDIPS), and only windows with read counts >10 were considered for analysis. Due to the non-normal distribution of all cohorts, RPKM values were log2 transformed before statistical analysis.

Statistical analyses

All analyses were performed in the R statistical environment version 3.2.1 [38]. For all analyses, a nominally significant threshold of p < 0.05 and a Bonferroni significant threshold of p < 7.04E−04 were used. Given the matched sample nature of this cohort, two-tailed, paired t tests were performed at each window along the mitochondrial genome to identify DMRs between the individual cortical regions and cerebellum. To compare the total cortex to cerebellum, we performed a multilevel mixed effects model in the Lme4 package in R [39], using the brain region as the random effect and individual as the fixed effect. To assess the similarity of the brain regions, we used the R function “hclust” to cluster average RPKM values for the brain regions using the Euclidean distance. We used the R function “corrgram” within the corrgram package [40] to order samples based upon the similarity of their principal components.

Abbreviations

5-mC: 

5-methylcytosine

BA: 

Brodmann area

DMR: 

Differentially methylated region

MeDIP-seq: 

Methylated DNA Immunoprecipitation Sequencing

mtDNA: 

Mitochondrial DNA

ncDNA: 

Nuclear DNA

NUMTs: 

Nuclear-mitochondrial DNA

PCA: 

Principal component analysis

rRNA: 

Ribosomal ribonucleic acid

tRNA: 

Transfer ribonucleic acid

Declarations

Acknowledgements

Not applicable.

Funding

This work was funded by an Alzheimer’s Society project grant to KL (grant number AS-PG-14-038), an Alzheimer’s Research UK pilot grant to KL (grant number ARUK-PPG2013A-5) and an Alzheimer’s Association New Investigator Research Grant to KL (grant number NIRG-14-320878).

Availability of data and materials

Paired-end, raw fastq files from Davies et al. were provided by the authors [35].

Authors’ contributions

MD, MW, RS, AJ and EH undertook the data analysis and bioinformatics. MD and MW developed the pipeline for the analysis. MND and JM provided the data for the analysis. KL conceived and supervised the project. MD and KL drafted the manuscript. All authors read and approved the final submission.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
University of Exeter Medical School, RILD, University of Exeter
(2)
Institute of Psychiatry, Psychology and Neuroscience, King’s College London
(3)
Department of Twin Research & Genetic Epidemiology, King’s College London
(4)
School of Biological Sciences, University of Essex

References

  1. Anderson S, Bankier AT, Barrell BG, de Bruijn MH, Coulson AR, Drouin J, Eperon IC, Nierlich DP, Roe BA, Sanger F, et al. Sequence and organization of the human mitochondrial genome. Nature. 1981;290:457–65.View ArticlePubMedGoogle Scholar
  2. Murgia M, Rizzuto R. Molecular diversity and pleiotropic role of the mitochondrial calcium uniporter. Cell Calcium. 2015;58:11–7.View ArticlePubMedGoogle Scholar
  3. Wang W, Esbensen Y, Kunke D, Suganthan R, Rachek L, Bjoras M, Eide L. Mitochondrial DNA damage level determines neural stem cell differentiation fate. J Neurosci. 2011;31:9746–51.View ArticlePubMedGoogle Scholar
  4. Lunnon K, Ibrahim Z, Proitsi P, Lourdusamy A, Newhouse S, Sattlecker M, Furney S, Saleem M, Soininen H, Kloszewska I, et al. Mitochondrial dysfunction and immune activation are detectable in early Alzheimer’s disease blood. J Alzheimers Dis. 2012;30:685–710.PubMedGoogle Scholar
  5. Lunnon K, Keohane A, Pidsley R, Newhouse S, Riddoch-Contreras J, Thubron EB, Devall M, Soininen H, Kloszewska I, Mecocci P, et al. Mitochondrial genes are altered in blood early in Alzheimer’s disease. Neurobiol Aging. 2017;53:36–47.View ArticlePubMedGoogle Scholar
  6. Prabakaran S, Swatton JE, Ryan MM, Huffaker SJ, Huang JT, Griffin JL, Wayland M, Freeman T, Dudbridge F, Lilley KS, et al. Mitochondrial dysfunction in schizophrenia: evidence for compromised brain metabolism and oxidative stress. Mol Psychiatry. 2004;9:684–97. 643.View ArticlePubMedGoogle Scholar
  7. Clay HB, Sillivan S, Konradi C. Mitochondrial dysfunction and pathology in bipolar disorder and schizophrenia. Int J Dev Neurosci. 2011;29:311–24.View ArticlePubMedGoogle Scholar
  8. Chang CC, Jou SH, Lin TT, Lai TJ, Liu CS. Mitochondria DNA change and oxidative damage in clinically stable patients with major depressive disorder. PLoS One. 2015;10:e0125855.View ArticlePubMedPubMed CentralGoogle Scholar
  9. 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. 2013;162B:789–99.View ArticlePubMedGoogle Scholar
  10. Lunnon K, Hannon E, Smith RG, Dempster E, Wong C, Burrage J, Troakes C, Al-Sarraj S, Kepa A, Schalkwyk L, Mill J. Variation in 5-hydroxymethylcytosine across human cortex and cerebellum. Genome Biol. 2016;17:27.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Lowe R, Slodkowicz G, Goldman N, Rakyan VK. The human blood DNA methylome displays a highly distinctive profile compared with other somatic tissues. Epigenetics. 2015;10:274–81.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Lokk K, Modhukur V, Rajashekar B, Martens K, Magi R, Kolde R, Koltsina M, Nilsson TK, Vilo J, Salumets A, Tonisson N. DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns. Genome Biol. 2014;15:r54.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Lunnon K, Smith R, Hannon EJ, De Jager PL, Srivastava G, Volta M, Troakes C, Al-Sarraj S, Burrage J, Macdonald R, et al. Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer’s disease. Nat Neurosci. 2014;17:1164–70.View ArticlePubMedPubMed CentralGoogle Scholar
  14. De Jager PL, Srivastava G, Lunnon K, Burgess J, Schalkwyk LC, Yu L, Eaton ML, Keenan BT, Ernst J, McCabe C, et al. Alzheimer’s disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci. 2014;17:1156–63.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Smith AR, Smith RG, Condliffe D, Hannon E, Schalkwyk L, Mill J, Lunnon K. Increased DNA methylation near TREM2 is consistently seen in the superior temporal gyrus in Alzheimer’s disease brain. Neurobiol Aging. 2016;47:35–40.View ArticlePubMedGoogle Scholar
  16. Pidsley R, Viana J, Hannon E, Spiers HH, Troakes C, Al-Saraj S, Mechawar N, Turecki G, Schalkwyk LC, Bray NJ, Mill J. Methylomic profiling of human brain tissue supports a neurodevelopmental origin for schizophrenia. Genome Biol. 2014;15:483.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Hannon E, Spiers H, Viana J, Pidsley R, Burrage J, Murphy TM, Troakes C, Turecki G, O’Donovan MC, Schalkwyk LC, et al. Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Nat Neurosci. 2016;19:48−+.View ArticlePubMedGoogle Scholar
  18. Florath I, Butterbach K, Heiss J, Bewerunge-Hudler M, Zhang Y, Schottker B, Brenner H. Type 2 diabetes and leucocyte DNA methylation: an epigenome-wide association study in over 1,500 older adults. Diabetologia. 2016;59:130–8.View ArticlePubMedGoogle Scholar
  19. Adams AT, Kennedy NA, Hansen R, Ventham NT, O’Leary KR, Drummond HE, Noble CL, El-Omar E, Russell RK, Wilson DC, et al. Two-stage genome-wide methylation profiling in childhood-onset Crohn’s Disease implicates epigenetic alterations at the VMP1/MIR21 and HLA loci. Inflamm Bowel Dis. 2014;20:1784–93.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Devall M, Mill J, Lunnon K. The mitochondrial epigenome: a role in Alzheimer’s disease? Epigenomics. 2014;6:665–75.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Devall M, Roubroeks J, Mill J, Weedon M, Lunnon K. Epigenetic regulation of mitochondrial function in neurodegenerative disease: New insights from advances in genomic technologies. Neurosci Lett. 2016;625:47–55.View ArticlePubMedGoogle Scholar
  22. Shock LS, Thakkar PV, Peterson EJ, Moran RG, Taylor SM. DNA methyltransferase 1, cytosine methylation, and cytosine hydroxymethylation in mammalian mitochondria. Proc Natl Acad Sci U S A. 2011;108:3630–5.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Chestnut BA, Chang Q, Price A, Lesuisse C, Wong M, Martin LJ. Epigenetic regulation of motor neuron cell death through DNA methylation. J Neurosci. 2011;31:16619–36.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Feng S, Xiong L, Ji Z, Cheng W, Yang H. Correlation between increased ND2 expression and demethylated displacement loop of mtDNA in colorectal cancer. Mol Med Rep. 2012;6:125–30.PubMedGoogle Scholar
  25. Pirola CJ, Gianotti TF, Burgueno AL, Rey-Funes M, Loidl CF, Mallardi P, Martino JS, Castano GO, Sookoian S. Epigenetic modification of liver mitochondrial DNA is associated with histological severity of nonalcoholic fatty liver disease. Gut. 2013;62:1356–63.View ArticlePubMedGoogle Scholar
  26. Infantino V, Castegna A, Iacobazzi F, Spera I, Scala I, Andria G, Iacobazzi V. Impairment of methyl cycle affects mitochondrial methyl availability and glutathione level in Down’s syndrome. Mol Genet Metab. 2011;102:378–82.View ArticlePubMedGoogle Scholar
  27. Wong M, Gertz B, Chestnut BA, Martin LJ. Mitochondrial DNMT3A and DNA methylation in skeletal muscle and CNS of transgenic mouse models of ALS. Front Cell Neurosci. 2013;7:279.PubMedPubMed CentralGoogle Scholar
  28. Wen SL, Zhang F, Feng S. Decreased copy number of mitochondrial DNA: a potential diagnostic criterion for gastric cancer. Oncol Lett. 2013;6:1098–102.PubMedPubMed CentralGoogle Scholar
  29. Blanch M, Mosquera JL, Ansoleaga B, Ferrer I, Barrachina M. Altered mitochondrial DNA methylation pattern in Alzheimer disease-related pathology and in Parkinson disease. Am J Pathol. 2016;186:385–97.View ArticlePubMedGoogle Scholar
  30. Iacobazzi V, Castegna A, Infantino V, Andria G. Mitochondrial DNA methylation as a next-generation biomarker and diagnostic tool. Mol Genet Metab. 2013;110:25–34.View ArticlePubMedGoogle Scholar
  31. Ghosh S, Sengupta S, Scaria V. Comparative analysis of human mitochondrial methylomes shows distinct patterns of epigenetic regulation in mitochondria. Mitochondrion. 2014;18:58–62.View ArticlePubMedGoogle Scholar
  32. Clark C, Palta P, Joyce CJ, Scott C, Grundberg E, Deloukas P, Palotie A, Coffey AJ. A comparison of the whole genome approach of MeDIP-seq to the targeted approach of the Infinium HumanMethylation450 BeadChip® for methylome profiling. PLoS One. 2012;7:e50233.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Thangaraj K, Joshi MB, Reddy AG, Rasalkar AA, Singh L. Sperm mitochondrial mutations as a cause of low sperm motility. J Androl. 2003;24:388–92.View ArticlePubMedGoogle Scholar
  34. Yao YG, Kong QP, Salas A, Bandelt HJ. Pseudomitochondrial genome haunts disease studies. J Med Genet. 2008;45:769–72.View ArticlePubMedGoogle Scholar
  35. Davies MN, Volta M, Pidsley R, Lunnon K, Dixit A, Lovestone S, Coarfa C, Harris RA, Milosavljevic A, Troakes C, et al. Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biol. 2012;13:R43.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Devall M, Burrage J, Caswell R, Johnson M, Troakes C, Al-Sarraj S, Jeffries AR, Mill J, Lunnon K. A comparison of mitochondrial DNA isolation methods in frozen post-mortem human brain tissue-applications for studies of mitochondrial genetics in brain disorders. Biotechniques. 2015;59:241–6.View ArticlePubMedGoogle Scholar
  37. Lienhard MGC, Morkel M, Herwig R, Chavez L. MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments. Bioinformatics. 2014;30:284–6.View ArticlePubMedGoogle Scholar
  38. R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2012. p. 2012.Google Scholar
  39. Vazquez AI, Bates DM, Rosa GJ, Gianola D, Weigel KA. Technical note: an R package for fitting generalized linear mixed models in animal breeding. J Anim Sci. 2010;88:497–504.View ArticlePubMedGoogle Scholar
  40. Wright K. Package corrgram. R package version 1.9. 2016. Available from: https://cran.r-project.org/web/packages/corrgram/index.html. Accessed 30 Sept 2016.

Copyright

© The Author(s). 2017