CoChat
Sign Up
Cited by 377

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Mitochondrial and nuclear DNA matching shapes metabolism and healthy ageing

Latorre-Pellicer, Ana, Moreno-Loshuertos, Raquel, Lechuga-Vieco, Ana Victoria, Sánchez-Cabo, Fátima, Torroja, Carlos, Acín-Pérez, Rebeca, Calvo, Enrique, Aix, Esther, González-Guerra, Andrés, Logan, Angela, Bernad-Miana, María Luisa, Romanos, Eduardo, Cruz, Raquel, Cogliati, Sara, Sobrino, Beatriz, Carracedo, Ángel, Pérez-Martos, Acisclo, Fernández-Silva, Patricio, Ruíz-Cabello, Jesús, Murphy, Michael P., Flores, Ignacio, Vázquez, Jesús, Enríquez, José Antonio·2016·Nature
DOI 10.1038/nature18618
Open this paper in CoChat — free account

Abstract

Human mitochondrial DNA (mtDNA) shows extensive within population sequence variability. Many studies suggest that mtDNA variants may be associated with ageing or diseases, although mechanistic evidence at the molecular level is lacking. Mitochondrial replacement has the potential to prevent transmission of disease-causing oocyte mtDNA. However, extension of this technology requires a comprehensive understanding of the physiological relevance of mtDNA sequence variability and its match with the nuclear-encoded mitochondrial genes. Studies in conplastic animals allow comparison of individuals with the same nuclear genome but different mtDNA variants, and have provided both supporting and refuting evidence that mtDNA variation influences organismal physiology. However, most of these studies did not confirm the conplastic status, focused on younger animals, and did not investigate the full range of physiological and phenotypic variability likely to be influenced by mitochondria. Here we systematically characterized conplastic mice throughout their lifespan using transcriptomic, proteomic,metabolomic, biochemical, physiological and phenotyping studies. We show that mtDNA haplotype profoundly influences mitochondrial proteostasis and reactive oxygen species generation,insulin signalling, obesity, and ageing parameters including telomere shortening and mitochondrial dysfunction, resulting in profound differences in health longevity between conplastic strains.

Overview

Integrity signals

Registry

Crossref · 10.1038/nature18618

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Mitochondrial-nuclear crosstalk is well documented, and evidence suggests that mutations in the mitochondrial genome influence nuclear gene expression profiles even when phenotypes are mild.

    “mitochondrial-nuclear crosstalk is well documented, and evidence suggests that mutations in the mitochondrial genome also influence nuclear gene expression profiles, even when phenotypes are mild [94].”

    From Codon optimization is an essential parameter for the efficient allotopic expression of mtDNA genes (2020) · cites this paper as [94]

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 23

  1. Wallace, D. C. Mitochondrial DNA variation in human radiation and disease. Cell 163, 33–38 (2015) · DOI
  2. Ruiz-Pesini, E. et al. Human mtDNA haplogroups associated with high or reduced spermatozoa motility. Am. J. Hum. Genet. 67, 682–696 (2000) · DOI
  3. Brown, M. D. et al. The role of mtDNA background in disease expression: a new primary LHON mutation associated with Western Eurasian haplogroup J. Hum. Genet. 110, 130–138 (2002) · DOI
  4. Baudouin, S. V. et al. Mitochondrial DNA and survival after sepsis: a prospective study. Lancet 366, 2118–2121 (2005) · DOI
  5. Amo, T. & Brand, M. D. Were inefficient mitochondrial haplogroups selected during migrations of modern humans? A test using modular kinetic analysis of coupling in mitochondria from cybrid cell lines. Biochem. J. 404, 345–351 (2007) · DOI
  6. Amo, T., Yadava, N., Oh, R., Nicholls, D. G. & Brand, M. D. Experimental assessment of bioenergetic differences caused by the common European mitochondrial DNA haplogroups H and T. Gene 411, 69–76 (2008) · DOI
  7. Roubertoux, P. L. et al. Mitochondrial DNA modifies cognition in interaction with the nuclear genome and age in mice. Nature Genet. 35, 65–69 (2003) · DOI
  8. Yu, X. et al. Dissecting the effects of mtDNA variations on complex traits using mouse conplastic strains. Genome Res. 19, 159–165 (2009) · DOI · Paper page
  9. Sharpley, M. S. et al. Heteroplasmy of mouse mtDNA is genetically unstable and results in altered behavior and cognition. Cell 151, 333–343 (2012) · DOI
  10. Soga, T. et al. Differential metabolomics reveals ophthalmic acid as an oxidative stress biomarker indicating hepatic glutathione consumption. J. Biol. Chem. 281, 16768–16776 (2006) · DOI
  11. Cochemé, H. M. et al. Using the mitochondria-targeted ratiometric mass spectrometry probe MitoB to measure H2O2 in living Drosophila. Nature Protocols 7, 946–958 (2012) · DOI
  12. Moreno-Loshuertos, R. et al. Differences in reactive oxygen species production explain the phenotypes associated with common mouse mitochondrial DNA variants. Nature Genet. 38, 1261–1268 (2006) · DOI
  13. Guarás, A. et al. The CoQH2/CoQ ratio serves as a sensor of respiratory chain efficiency. Cell Rep. 15, 197–209 (2016) · DOI
  14. Quirós, P. M., Langer, T. & López-Otín, C. New roles for mitochondrial proteases in health, ageing and disease. Nature Rev. Mol. Cell Biol. 16, 345–359 (2015) · DOI
  15. Tachibana, M. et al. Mitochondrial gene replacement in primate offspring and embryonic stem cells. Nature 461, 367–372 (2009) · DOI
  16. Craven, L. et al. Pronuclear transfer in human embryos to prevent transmission of mitochondrial DNA disease. Nature 465, 82–85 (2010) · DOI
  17. Chinnery, P. F. et al. The challenges of mitochondrial replacement. PLoS Genet. 10, e1004315 (2014) · DOI
  18. Logan, A. et al. Using exomarkers to assess mitochondrial reactive species in vivo. Biochim. Biophys. Acta 1840, 923–930 (2014) · DOI
  19. Acín-Pérez, R., Fernández-Silva, P., Peleato, M. L., Pérez-Martos, A. & Enriquez, J. A. Respiratory active mitochondrial supercomplexes. Mol. Cell 32, 529–539 (2008) · DOI · Paper page
  20. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010) · DOI · Paper page
  21. Martínez-Bartolomé, S. et al. Properties of average score distributions of SEQUEST: the probability ratio method. Mol. Cell. Proteomics 7, 1135–1145 (2008) · DOI
  22. Navarro, P. & Vázquez, J. A refined method to calculate false discovery rates for peptide identification using decoy databases. J. Proteome Res. 8, 1792–1796 (2009) · DOI
  23. García-Marqués, F. et al. A novel systems-biology algorithm for the analysis of coordinated protein responses using quantitative proteomics. Mol. Cell. Proteomics 15, 1740–1760 (2016) · DOI

Cited by

Sorted by citations

Metadata

Authors
Latorre-Pellicer, A., Moreno-Loshuertos, R., Lechuga-Vieco, A. V., Sánchez-Cabo, F., Torroja, C., Acín-Pérez, R., Calvo, E., Aix, E., González-Guerra, A., Logan, A., Bernad-Miana, M. L., Romanos, E., Cruz, R., Cogliati, S., Sobrino, B., Carracedo, Á., Pérez-Martos, A., Fernández-Silva, P., Ruíz-Cabello, J., Murphy, M. P., Flores, I., Vázquez, J., & Enríquez, J. A.
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2016-07-06
Volume
535
Issue
7613
Pages
561-565
ISSN
  • 0028-0836
  • 1476-4687
DOI
10.1038/nature18618
Cited by
377
References
7
Language
en
Update notices
erratum (2016-12-07)
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review