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Large-scale genetic analysis reveals mammalian mtDNA heteroplasmy dynamics and variance increase through lifetimes and generations
JP. Burgstaller, T. Kolbe, V. Havlicek, S. Hembach, J. Poulton, J. Piálek, R. Steinborn, T. Rülicke, G. Brem, NS. Jones, IG. Johnston,
Jazyk angličtina Země Anglie, Velká Británie
Typ dokumentu časopisecké články, práce podpořená grantem
NLK
Directory of Open Access Journals
od 2015
Free Medical Journals
od 2010
Nature Open Access
od 2010-12-01
PubMed Central
od 2012
Europe PubMed Central
od 2012
ProQuest Central
od 2010-01-01
Open Access Digital Library
od 2015-01-01
Open Access Digital Library
od 2015-01-01
Medline Complete (EBSCOhost)
od 2012-11-01
Health & Medicine (ProQuest)
od 2010-01-01
ROAD: Directory of Open Access Scholarly Resources
od 2010
- MeSH
- datové soubory jako téma MeSH
- genom mitochondriální genetika MeSH
- haplotypy genetika MeSH
- mitochondriální DNA genetika MeSH
- mitochondrie metabolismus MeSH
- modely u zvířat MeSH
- myši inbrední C57BL MeSH
- myši MeSH
- oocyty cytologie imunologie MeSH
- variabilita počtu kopií segmentů DNA genetika MeSH
- věkové faktory MeSH
- zvířata MeSH
- Check Tag
- myši MeSH
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Vital mitochondrial DNA (mtDNA) populations exist in cells and may consist of heteroplasmic mixtures of mtDNA types. The evolution of these heteroplasmic populations through development, ageing, and generations is central to genetic diseases, but is poorly understood in mammals. Here we dissect these population dynamics using a dataset of unprecedented size and temporal span, comprising 1947 single-cell oocyte and 899 somatic measurements of heteroplasmy change throughout lifetimes and generations in two genetically distinct mouse models. We provide a novel and detailed quantitative characterisation of the linear increase in heteroplasmy variance throughout mammalian life courses in oocytes and pups. We find that differences in mean heteroplasmy are induced between generations, and the heteroplasmy of germline and somatic precursors diverge early in development, with a haplotype-specific direction of segregation. We develop stochastic theory predicting the implications of these dynamics for ageing and disease manifestation and discuss its application to human mtDNA dynamics.
Department for Agrobiotechnology Biotechnology in Animal Production IFA Tulln 3430 Tulln Austria
Nuffield Department of Women's and Reproductive Health University of Oxford Oxford United Kingdom
School of Biosciences University of Birmingham Birmingham B15 2TT UK
Citace poskytuje Crossref.org
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- $a Burgstaller, Joerg P $u Department for Agrobiotechnology, Biotechnology in Animal Production, IFA Tulln, 3430, Tulln, Austria. joerg.burgstaller@vetmeduni.ac.at. Institute of Animal Breeding and Genetics, University of Veterinary Medicine Vienna, Veterinärplatz 1, 1210, Vienna, Austria. joerg.burgstaller@vetmeduni.ac.at. Department of Mathematics, Imperial College London, London, SW7 2AZ, UK. joerg.burgstaller@vetmeduni.ac.at.
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- $a Vital mitochondrial DNA (mtDNA) populations exist in cells and may consist of heteroplasmic mixtures of mtDNA types. The evolution of these heteroplasmic populations through development, ageing, and generations is central to genetic diseases, but is poorly understood in mammals. Here we dissect these population dynamics using a dataset of unprecedented size and temporal span, comprising 1947 single-cell oocyte and 899 somatic measurements of heteroplasmy change throughout lifetimes and generations in two genetically distinct mouse models. We provide a novel and detailed quantitative characterisation of the linear increase in heteroplasmy variance throughout mammalian life courses in oocytes and pups. We find that differences in mean heteroplasmy are induced between generations, and the heteroplasmy of germline and somatic precursors diverge early in development, with a haplotype-specific direction of segregation. We develop stochastic theory predicting the implications of these dynamics for ageing and disease manifestation and discuss its application to human mtDNA dynamics.
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