The Cellular Mitochondrial Genome Landscape in Disease

Hahn, Anne; Zuryn, Steven

doi:10.1016/j.tcb.2018.11.004

Cited by 73 publications

(69 citation statements)

References 169 publications

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“…Toward this goal, we removed substitutions potentially related to carcinogenesis, then assigned SVM prediction values to the remaining variants. We expected that pathogenic alterations are more likely to be detected as heteroplasmic within human samples, since overt mitochondrial disease can require a deleterious variant to rise to a high proportion of the mtDNA molecules maintained by the cell (Stewart and Chinnery 2015;Hahn and Zuryn 2019). Therefore, we asked whether variants in our two predicted classes could be differentiated by the percentage of samples reported as heteroplasmic within HelixMTdb.…”

Section: A Support Vector Machine Can Predict Deleterious Mtdna Polymmentioning

confidence: 99%

“…While a very limited number of mitochondrial DNA (mtDNA) lesions can be directly linked to human disease, the clinical outcome for many other mtDNA changes remains ambiguous (Vento and Pappa 2013). Heteroplasmy among the hundreds of mitochondrial DNA (mtDNA) molecules found within a cell (Stewart and Chinnery 2015;Hahn and Zuryn 2019), differential distribution of disease-causing mtDNA among tissues (Boulet et al 1992), and modifier alleles within the mitochondrial genome (Elliott et al 2008;Wei et al 2017) magnify the problem of interpreting effects of different mtDNA alterations. Mito-nuclear interactions and environmental effects may also determine the outcome of mitochondrial DNA mutations (Wolff et al 2014;Matilainen et al 2017;Hill et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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A novel phylogenetic analysis and machine learning predict pathogenicity of human mtDNA variants

Dunn

Akpinar

Carlson³

et al. 2020

Preprint

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Linking mitochondrial DNA (mtDNA) mutations to patient outcomes remains a formidable challenge. The multicopy nature and potential heteroplasmy of the mitochondrial genome, differential distribution of mutant mtDNAs among various tissues, genetic interactions among alleles, and environmental effects currently hamper clinical efforts to diagnose mitochondrial disease. Multiple sequence alignments are often deployed to estimate the potential significance of mitochondrial variants. However, factors including sample set bias, alignment errors, and sequencing errors currently limit the utility of multiple sequence alignments in pathogenicity prediction. Here, we describe an approach to assessment of site-specific conservation and variant acceptability that is reliant upon ancestral phylogenetic predictions and minimizes current alignment limitations. Using the output of our approach, we deploy machine learning in order to predict the pathogenicity of thousands of human mtDNA variants not yet linked to disease. Our work demonstrates that a substantial portion of mtDNA variants not yet characterized as harmful are, in fact, likely to be deleterious. Our findings will be of direct relevance to those at risk of mitochondria-associated illness, but the general applications of our methodology also extend beyond the context of mitochondrial disorders.

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Section: A Support Vector Machine Can Predict Deleterious Mtdna Polymmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel phylogenetic analysis and machine learning predict pathogenicity of human mtDNA variants

Dunn

Akpinar

Carlson³

et al. 2020

Preprint

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“…In animal models and humans point mutations and large deletions in mtDNA increase in frequency with age and have been implicated in the origin of age-related diseases (Greaves et al 2014;Hahn and Zuryn 2019;Li et al 2015;Sun et al 2016). It is generally agreed that in a cell: mutations accumulates faster in mtDNA, as compared to that in nuclear DNA.…”

Section: Mutagenesis Of Mtdnamentioning

confidence: 99%

DNA repair and mutagenesis in vertebrate mitochondria: evidence for the asymmetric DNA strand inheritance

Matkarimov

Saparbaev

2019

Preprint

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A variety of endogenous and exogenous factors induce chemical and structural alterations to cellular DNA, as well as errors occurring throughout DNA synthesis. These DNA damages are cytotoxic, miscoding, or both, and are believed to be at the origin of cancer and other age related diseases. A human cell, in addition to nuclear DNA, contains thousands copies of mitochondrial DNA (mtDNA), a double-stranded, circular molecule of 16,569 bp. It was proposed that mtDNA is a critical target for reactive oxygen species (ROS), by-products of the oxidative phosphorylation (OXPHOS), generated in the organelle during aerobic respiration. Indeed, oxidative damage to mtDNA are more extensive and persistent as compared to that of nuclear DNA. Although, transversions are the hallmarks of mutations induced by ROS, paradoxically, the majority of mtDNA mutations that occurred during ageing and cancer are transitions. Furthermore, these mutations exhibit a striking strand orientation bias: T→C/G→A transitions preferentially occur on the Light strand, whereas C→T/A→G on the Heavy strand of mtDNA. Here, we propose that the majority of mtDNA progenies, created after multiple rounds of DNA replication, are derived from the Heavy strand only, due to asymmetric replication of the DNA strand anchored to inner membrane via D-loop structure.

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“…Hundreds to 38 thousands of mtDNA copies can populate each cell, which can result in a mixture of multiple 39 mtDNA variants within individual cells and organelles (Morris et al, 2017). This state, termed 40 heteroplasmy, and the factors that influence its dynamics are critical determinants for the 41 pathogenesis of rare mitochondrial diseases, and possibly a wide range of common age-onset 42 inflictions, including neurodegeneration, diabetes, and cancer (Hahn and Zuryn, 2018; 43 Stewart and Chinnery, 2015). 44 45 At any given time, an interplay between stochastic and deterministic processes 46 influence cellular heteroplasmy (Hahn and Zuryn, 2018).…”

Section: Introduction 29 30mentioning

confidence: 99%

“…This state, termed 40 heteroplasmy, and the factors that influence its dynamics are critical determinants for the 41 pathogenesis of rare mitochondrial diseases, and possibly a wide range of common age-onset 42 inflictions, including neurodegeneration, diabetes, and cancer (Hahn and Zuryn, 2018; 43 Stewart and Chinnery, 2015). 44 45 At any given time, an interplay between stochastic and deterministic processes 46 influence cellular heteroplasmy (Hahn and Zuryn, 2018). Mitotic segregation, genetic drift, 47 and the competing effects of homeostatic (Gitschlag et the cells within a tissue, creating heteroplasmy mosaicism.…”

Section: Introduction 29 30mentioning

confidence: 99%

PINK1 and parkin shape the organism-wide distribution of a deleterious mitochondrial genome

Ahier

Cummins

Dai

et al. 2019

Preprint

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In multiple species, certain tissue types are prone to acquiring greater loads of mitochondrial genome (mtDNA) mutations relative to others, however the mechanisms that drive these heteroplasmy differences are unknown. We found that the conserved PTEN-induced putative kinase (PINK1/PINK-1) and the E3 ubiquitin-protein ligase parkin (PDR-1), which are required for mitochondrial autophagy (mitophagy), underlie stereotyped differences in heteroplasmy of a deleterious mitochondrial genome mutation (ΔmtDNA) between major somatic tissues types in Caenorhabditis elegans. We demonstrate that tissues prone to accumulating ΔmtDNA have lower mitophagy responses than those with low mutation levels, such as neurons. Moreover, we show that ΔmtDNA heteroplasmy increases when proteotoxic species that are associated with neurodegenerative disease and mitophagy inhibition are overexpressed in the nervous system. Together, these results suggest that PINK1 and parkin drive organism-wide patterns of heteroplasmy and provide evidence of a causal link between proteotoxicity, mitophagy, and mtDNA mutation levels in neurons.

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The Cellular Mitochondrial Genome Landscape in Disease

Cited by 73 publications

References 169 publications

A novel phylogenetic analysis and machine learning predict pathogenicity of human mtDNA variants

A novel phylogenetic analysis and machine learning predict pathogenicity of human mtDNA variants

DNA repair and mutagenesis in vertebrate mitochondria: evidence for the asymmetric DNA strand inheritance

PINK1 and parkin shape the organism-wide distribution of a deleterious mitochondrial genome

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