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Mitochondrial DNA haplotypes induce differential patterns of DNA methylation that result in differential chromosomal gene expression patterns.

ABSTRACT: Mitochondrial DNA copy number is strictly regulated during development as naive cells differentiate into mature cells to ensure that specific cell types have sufficient copies of mitochondrial DNA to perform their specialised functions. Mitochondrial DNA haplotypes are defined as specific regions of mitochondrial DNA that cluster with other mitochondrial sequences to show the phylogenetic origins of maternal lineages. Mitochondrial DNA haplotypes are associated with a range of phenotypes and disease. To understand how mitochondrial DNA haplotypes induce these characteristics, we used four embryonic stem cell lines that have the same set of chromosomes but possess different mitochondrial DNA haplotypes. We show that mitochondrial DNA haplotypes influence changes in chromosomal gene expression and affinity for nuclear-encoded mitochondrial DNA replication factors to modulate mitochondrial DNA copy number, two events that act synchronously during differentiation. Global DNA methylation analysis showed that each haplotype induces distinct DNA methylation patterns, which, when modulated by DNA demethylation agents, resulted in skewed gene expression patterns that highlight the effectiveness of the new DNA methylation patterns established by each haplotype. The haplotypes differentially regulate ?-ketoglutarate, a metabolite from the TCA cycle that modulates the TET family of proteins, which catalyse the transition from 5-methylcytosine, indicative of DNA methylation, to 5-hydroxymethylcytosine, indicative of DNA demethylation. Our outcomes show that mitochondrial DNA haplotypes differentially modulate chromosomal gene expression patterns of naive and differentiating cells by establishing mitochondrial DNA haplotype-specific DNA methylation patterns.

SUBMITTER: Lee WT

PROVIDER: S-EPMC5592988 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Mitochondrial DNA haplotypes induce differential patterns of DNA methylation that result in differential chromosomal gene expression patterns.

Lee William T WT Sun Xin X Tsai Te-Sha TS Johnson Jacqueline L JL Gould Jodee A JA Garama Daniel J DJ Gough Daniel J DJ Gough Daniel J DJ McKenzie Matthew M Trounce Ian A IA St John Justin C JC

Cell death discovery 20170911

Mitochondrial DNA copy number is strictly regulated during development as naive cells differentiate into mature cells to ensure that specific cell types have sufficient copies of mitochondrial DNA to perform their specialised functions. Mitochondrial DNA haplotypes are defined as specific regions of mitochondrial DNA that cluster with other mitochondrial sequences to show the phylogenetic origins of maternal lineages. Mitochondrial DNA haplotypes are associated with a range of phenotypes and dis ...[more]

PMID: 28900542

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Project description:BackgroundDNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers.MethodsIn this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM.ResultsPanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect.ConclusionsPanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html .

Dataset Information

Mitochondrial DNA haplotypes induce differential patterns of DNA methylation that result in differential chromosomal gene expression patterns.

Publications

Mitochondrial DNA haplotypes induce differential patterns of DNA methylation that result in differential chromosomal gene expression patterns.

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