Evidence map›Paper›PMID 31242175›Full record

ArticlePLoS computational biology2019

Energetic costs of cellular and therapeutic control of stochastic mitochondrial DNA populations.

Hanne Hoitzing, Payam A Gammage, Lindsey Van Haute, Michal Minczuk, Iain G Johnston, Nick S Jones

Abstract read
In one paragraph

Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Stochastic survival of the densest and mitochondrial DNA clonal expansion in aging.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  11. Review
  12. Article
  13. Article
  14. The social nature of mitochondria: Implications for human health.Neuroscience and biobehavioral reviews · 2021
    Review
  15. Article
  16. Evolving mtDNA populations within cells.Biochemical Society transactions · 2019
    Review
  17. Article
  18. Varied Mechanisms and Models for the Varying Mitochondrial Bottleneck.Frontiers in cell and developmental biology · 2019
    Article
  19. Article
  20. Mitochondrial Heterogeneity.Frontiers in genetics · 2018
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Hanne HoitzingDepartment of Mathematics, Imperial College London, London, SW7 2AZ, United Kingdom.
Payam A GammageMRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, CB2 0XY, United Kingdom.ORCID 0000-0003-1968-1726
Lindsey Van HauteMRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, CB2 0XY, United Kingdom.
Michal MinczukMRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, CB2 0XY, United Kingdom.ORCID 0000-0001-8242-1420
Iain G JohnstonFaculty of Mathematics and Natural Sciences, University of Bergen, Bergen, Norway.ORCID 0000-0001-8559-3519
Nick S JonesDepartment of Mathematics, Imperial College London, London, SW7 2AZ, United Kingdom.ORCID 0000-0002-4083-972X

Funding

Medical Research Council MC_UU_00015/4
6 · The paper itself

Abstract

The 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.

Indexed as

Cell Physiological PhenomenaComputational BiologyDNA, MitochondrialEnergy MetabolismHumansModels, BiologicalMutationStochastic ProcessesDNA, Mitochondrial

Identifiers

PMID31242175
PMCPMC6615642

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.