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- W3046156652 abstract "Abstract Mitochondrial DNA (mtDNA) encodes cellular machinery vital for cell and organism survival. Mutations, genetic manipulation, and gene therapies may produce cells where different types of mtDNA coexist in admixed populations. In these admixtures, one mtDNA type is often observed to proliferate over another, with different types dominating in different tissues. This ‘segregation bias’ is a long-standing biological mystery that may pose challenges to modern mtDNA disease therapies, leading to substantial recent attention in biological and medical circles. Here, we show how an mtDNA sequence’s balance between replication and transcription, corresponding to molecular ‘selfishness’, in conjunction with cellular selection, can potentially modulate segregation bias. We combine a new replication-transcription-selection (RTS) model with a meta-analysis of existing data to show that this simple theory predicts complex tissue-specific patterns of segregation in mouse experiments, and reversion in human stem cells. We propose the stability of G-quadruplexes in the mtDNA control region, influencing the balance between transcription and replication primer formation, as a potential molecular mechanism governing this balance. Linking mtDNA sequence features, through this molecular mechanism, to cellular population dynamics, we use sequence data to obtain and verify the sequence-specific predictions from this hypothesis on segregation behaviour in mouse and human mtDNA." @default.
- W3046156652 created "2020-08-03" @default.
- W3046156652 creator A5016846029 @default.
- W3046156652 creator A5072984826 @default.
- W3046156652 date "2020-07-27" @default.
- W3046156652 modified "2023-09-27" @default.
- W3046156652 title "MtDNA sequence features associated with ‘selfish genomes’ predict tissue-specific segregation and reversion" @default.
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- W3046156652 doi "https://doi.org/10.1093/nar/gkaa622" @default.
- W3046156652 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7470939" @default.
- W3046156652 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32716035" @default.
- W3046156652 hasPublicationYear "2020" @default.
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