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dc.contributor.authorHoitzing, Hanne
dc.contributor.authorGammage, Payam
dc.contributor.authorvan Haute, Lindsey
dc.contributor.authorMinczuk, Michal
dc.contributor.authorJohnston, Iain
dc.contributor.authorJones, Nick
dc.date.accessioned2020-08-05T11:16:09Z
dc.date.available2020-08-05T11:16:09Z
dc.date.issued2019-06-26
dc.PublishedHoitzing, Gammage, van Haute, Minczuk, Johnston I G, Jones N. Energetic costs of cellular and therapeutic control of stochastic mitochondrial DNA populations. PLoS Computational Biology. 2019;15(6):e1007023eng
dc.identifier.issn1553-7358en_US
dc.identifier.issn1553-734Xen_US
dc.identifier.urihttps://hdl.handle.net/1956/23455
dc.description.abstractThe dynamics of the cellular proportion of mutant mtDNA molecules is crucial for mitochondrial diseases. Cellular populations of mitochondria are under homeostatic control, but the details of the control mechanisms involved remain elusive. Here, we use stochastic modelling to derive general results for the impact of cellular control on mtDNA populations, the cost to the cell of different mtDNA states, and the optimisation of therapeutic control of mtDNA populations. This formalism yields a wealth of biological results, including that an increasing mtDNA variance can increase the energetic cost of maintaining a tissue, that intermediate levels of heteroplasmy can be more detrimental than homoplasmy even for a dysfunctional mutant, that heteroplasmy distribution (not mean alone) is crucial for the success of gene therapies, and that long-term rather than short intense gene therapies are more likely to beneficially impact mtDNA populations.en_US
dc.language.isoengeng
dc.publisherPLoSen_US
dc.rightsAttribution CC BYeng
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/eng
dc.titleEnergetic costs of cellular and therapeutic control of stochastic mitochondrial DNA populationsen_US
dc.typePeer reviewed
dc.typeJournal article
dc.date.updated2019-12-02T09:23:33Z
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2019 The Authorsen_US
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1007023
dc.identifier.cristin1755279
dc.source.journalPLoS Computational Biology


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