DNA methylation status of nuclear-encoded mitochondrial genes underlies the tissue-dependent mitochondrial functions
© Takasugi et al. 2010
Received: 27 February 2010
Accepted: 19 August 2010
Published: 19 August 2010
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© Takasugi et al. 2010
Received: 27 February 2010
Accepted: 19 August 2010
Published: 19 August 2010
Mitochondria are semi-autonomous, semi-self-replicating organelles harboring their own DNA (mitochondrial DNA, mtDNA), and their dysregulation is involved in the development of various diseases. While mtDNA does not generally undergo epigenetic modifications, almost all mitochondrial proteins are encoded by nuclear DNA. However, the epigenetic regulation of nuclear-encoded mitochondrial genes (nuclear mt genes) has not been comprehensively analyzed.
We analyzed the DNA methylation status of 899 nuclear mt genes in the liver, brain, and heart tissues of mouse, and identified 636 nuclear mt genes carrying tissue-dependent and differentially methylated regions (T-DMRs). These nuclar mt genes are involved in various mitochondrial functions and they also include genes related to human diseases. T-DMRs regulate the expression of nuclear mt genes. Nuclear mt genes with tissue-specific hypomethylated T-DMRs were characterized by enrichment of the target genes of specific transcription factors such as FOXA2 in the liver, and CEBPA and STAT1 in the brain.
A substantial proportion of nuclear mt genes contained T-DMRs, and the DNA methylation status of numerous T-DMRs should underlie tissue-dependent mitochondrial functions.
Mitochondrial dysfunction is a common cause of human diseases [1, 2], and thus understanding the regulation of mitochondrial functions is critical. Mitochondria do not contain histones , and almost all mtDNA is unmethylated [4, 5], indicating that mtDNA is not epigenetically regulated. However, while mammalian mitochondria are estimated to consist of more than 1,500 proteins, only 13 proteins are encoded by mtDNA.
Methylation of nuclear DNA is a major component of epigenetic system in mammalian cells, and is involved in silencing of gene transcription and maintaining genomic stability [6, 7]. Hypomethylation of regulatory regions is required to allow expression of genes [8, 9]. Microarray-based DNA methylation analysis revealed the existence of thousands of tissue-dependent and differentially methylated regions (T-DMRs) in the mouse and human genomes [10, 11]. While the T-DMRs of some genes, such as Oct-4 and Nanog, are hypomethylated only in a few cells [12, 13], the methylation status of most T-DMRs is not specific, but common to certain cells or tissues [11, 14]. Tissue-dependent methylation status of T-DMRs, including tissue-specific methylation status of T-DMRs, forms a distinctive DNA methylation profile for each cell type [8, 11, 15].
A nuclear mt gene, Ant4, which encodes mitochondrial outer membrane protein, contains T-DMRs which is specifically hypomethylated in the testis [16, 17]. Also, few dozens of nuclear mt genes in mice are hypomethylated in the liver relative to the cerebrum . However, the presence of T-DMRs in nuclear mt genes has not been comprehensively analyzed; this is necessary for understanding the regulation of mitochondrial functions. In this study, we analyzed the DNA methylation of 899 nuclear mt genes in the liver, brain, and heart tissues of mouse; these tissues consume large amounts of energy and are highly susceptible to mitochondrial dysfunctions. Our results indicated that at least 636 nuclear mt genes, which account for 71% of the total investigated nuclear mt genes, contain T-DMRs in their transcription start site (TSS) flanking regions (-7~+3 kb of TSSs), and that the differential methylation status of these T-DMRs is associated with tissue-dependent mitochondrial functions.
Nuclear mt genes with hypo T-DMRs
Various mitochondrial functions
Abat, Acsl5, Oat, etc
Dmgdh, Mthfd1, Star, etc
Cpt1b, Efta, Pdk4, etc
Atp5o, Ndufb2, etc
Atp5l, Cox6, Ndufs8, etc
Atp5l, Ndufs4, Sdhc, etc
Gsr, Hagh, Mgst1, Tst
Tissue-specific mitochondrial functions
Regulation of mitochondrial morphology
Regulation of mtDNA
Translation of mtDNA-encoded proteins
Human homologs of mouse nuclear mt genes possessing T-DMRs are related to various human diseases. For example, mutations of Lrpprc, Ndufs4, and Ndufs8, the genes with liver-, brain-, and heart-hypo T-DMRs, respectively, are associated with Leigh's disease [21–23]. In addition to mutations, overexpression of some nuclear mt genes with liver-hypo T-DMRs in non-liver tissues are involved in the human diseases. For example, overexpression of Acsl5 and Tgm2 are found in the human glioma and brain of Huntington's disease and are suggested to be involved in pathogenesis [24, 25].
We next investigated whether T-DMRs of nuclear mt genes are associated with any transcriptional regulatory motifs. FOXA2 is a transcription factor that activates the genes involved in mitochondrial β-oxidation and the regulation of lipid metabolism, ketogenesis, and insulin sensitivity in the mouse liver [28, 29]. We analyzed the enrichment of genes containing FOXA2-binding sites within an extended gene region (encompassing 10 kb upstream of TSS and 1 kb downstream of the 3' UTR) using known genome-wide FOXA2-binding sites in the mouse liver obtained by ChIP-sequencing . We found that the targets of FOXA2 were enriched 1.6-fold in nuclear mt genes with liver-hypo T-DMRs relative to all nuclear mt genes (P < 5 × 10-5, Fisher's exact test). Sixty-four out of 123 nuclear mt genes with liver-hypo T-DMRs were the targets of FOXA2 (Additional file 6). This is contrast to the previous report, which indicated enrichment of target genes of HNF1 and/or HNF4 in hypomethylated genes in the mouse liver .
Transcription factors whose binding-sites were enriched in analyzed sequences
Number of targets
3.1 × 10-2
3.6 × 10-2
2.3 × 10-2
2.5 × 10-2
At least 71% of investigated nuclear mt genes contains T-DMRs, and the methylation status of T-DMRs correlated with tissue-dependent expression of dozens of nuclear mt genes. Considering that there are at least 200 different cell types in the mammalian body, the total number of nuclear mt genes with T-DMRs will be higher. The differences in protein composition of mitochondria are reported to reflect tissue-dependent nuclear mt gene expression . Our data suggest that DNA methylation status of nuclear mt genes underlies tissue-dependent mitochondrial functions.
C57BL/6N male mice were obtained from Charles River Japan. Mice were euthanized at 12-13 week old, and tissues were collected and frozen at -80°C until use. All experiments using mice were carried out according to the institutional guidelines for the care and use of laboratory animals (Graduate School of Agriculture and Life Sciences, The University of Tokyo).
Genomic DNA was extracted as described previously . Briefly, tissue samples were homogenized, and incubated with lysis solution (10 mM Tris-HCl at pH 8.0, 5 mM EDTA, 200 mM NaCl, 0.2% SDS, and 200 μg/mL proteinase K) at 55°C for 30 min, and were extracted with a phenol/chloroform/isoamylalcohol (PCI) mixture (50 : 49 : 1), incubated with RNase for 30 min, and re-extracted with PCI. DNA was precipitated with ethanol and dissolved in Tris-EDTA (TE) buffer (pH 8.0).
We used T-DMR profiling with restriction-tag mediated amplification (D-REAM) analysis  to obtain tissue-dependent and differentially methylated HpyCH4IV sites within the TSS flanking regions of RefSeq genes. For D-REAM analysis, HpyCH4IV-digested genomic DNA was extracted with PCI, re-extracted with chloroform, precipitated with ethanol and dissolved in TE (pH 8.0). Using purified DNA (250 ng), following procedure of D-REAM analysis was performed as described previously . Briefly, genomic DNA was digested by the methyl-sensitive enzyme HpyCH4IV (New England Biolabs), followed by ligation-mediated PCR, and subsequent hybridization of DNA to a GeneChip Mouse Promoter 1.0R Array (Affymetrix). Comparison of the resulting signals from digested HpyCH4IV sites between different tissue samples indicates the differential methylation level at a given site.
D-REAM analysis was performed twice for each of the biological duplicates of heart, and once for the liver and brain in this study. For the liver and brain, we added single D-REAM data set from our previous study using tissues from different individual . Correlation coefficients of microarray probe intensities between biological duplicates were greater than 0.93. D-REAM data obtained in this study has been deposited in the ArrayExpress database (accession number A-MEXP-791).
Genomic DNA was digested with HindIII (Takara). Digested DNA (5 μg) was denatured with 0.3 M NaOH. Sodium metabisulfite (pH 5.0) and hydroquinone were added to a final concentration of 2.0 M and 0.5 mM, respectively. The reaction mixture was incubated under following conditions: 15 cycles of 95°C for 30 s and 50°C for 15 min. Next, 1.77 volume of QG buffer was added to the reaction mixture, and DNA was purified using a Quiagen gel extraction kit (Qiagen), and eluted with 100 μl of elution buffer (EB). DNA was treated with 0.3 M NaOH at 37°C for 15 min, precipitated using 6 M ammonium acetate (pH 7.0) and ethanol, and dissolved in 200 μl TE (pH 8.0). For each bisulfite PCR, 2 μl of DNA solution was used as the template, and BIOTAQ HS DNA polymerase (Bioline) was used for amplification. PCR was performed under the following conditions: denaturation at 95°C for 10 min followed by 43 cycles, each cycle comprising 95°C for 30 sec, 60°C for 45 sec, 72°C for 30 sec, followed by 10 min at 72°C. All primers used in this experiment are listed in Additional file 2. The PCR product was digested with HpyCH4IV. Restriction-enzyme-treated DNA was desalted using gel filtration through Sephadex G-50, and was analyzed using the MultiNA microchip electrophoresis system (Shimadzu). The methylation level was calculated as the ratio of the amounts of cut fragments to those of the total of cut and uncut fragments obtained from the electropherograms.
Total RNA was prepared using TRIzol reagent (Invitrogen). Before synthesis of first-strand cDNA, the RNA preparation was treated with RNase-free DNase I (Invitrogen) to eliminate any residual genomic DNA. The total RNA was then converted into first-strand cDNA using random hexamers and Superscript III First-Strand Synthesis System for RT-PCR (Invitrogen). The obtained cDNA were amplified and quantified in triplicates by using the Quantitect SYBR Green PCR Kit (Qiagen) with ABI 7500 Real Time PCR system (Applied Biosystems). PCR was performed under the following conditions: Incubation at 95°C for 10 min followed by 40 cycles of PCR, each cycle comprising 95°C for 15 sec and 60°C for 1 min. All primers used in this experiment are listed in Additional file 8. Standard curves were obtained with serial dilutions of a pool of cDNA samples derived from each tissue.
MAT  was used to analyze the tiling array data (.CEL files) and identify the hypomethylated regions based on tiling probe signals, probe sequences, and copy numbers. Original tiling probes were remapped to the mouse genome assembly version mm9 (July 2007 build) provided by UCSC genome database. For the quality control of D-REAM analysis, we monitored the selective amplification of HpyCH4IV-digested fragments for the tilling array data of each sample (Additional file 9).
For expression analysis, data from the GeneChip Mouse Genome 430 2.0 Array of liver, heart, and cerebral cortex tissues of C57BL/6N male mice (8-10 week old; n = 2 for each tissue) were downloaded from Gene Expression Omnibus (accession no. GSE10246). The array image data (.CEL files) was processed by the factor analysis for robust microarray summarization algorithm (FARMS) with quantile normalization .
Enrichment analysis of specific transcription factor targets was performed using oPOSSUM program . This program analyzed the genes using one-to-one human-mouse orthologs and detected promoter motifs in the conserved regions. The top 10% of the non-coding conserved regions with an absolute minimum percent identity of 70% in each 5 kb region upstream and downstream of the TSSs were analyzed for vertebrate promoter motifs with a matrix match threshold of 75%.
This research was funded by grants from the National Institute of Biomedical Innovation (NIBIO); Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan. The authors declare no conflicts of interest. We would like to thank Dr. Shinya Sato and Mr. Hiroki Muramoto for their helpful suggestions regarding bioinformatic analysis.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.