Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Mitochondrial and nuclear DNA matching shapes metabolism and healthy ageing

A Corrigendum to this article was published on 07 December 2016


Human mitochondrial DNA (mtDNA) shows extensive within-population sequence variability1. Many studies suggest that mtDNA variants may be associated with ageing or diseases2,3,4, although mechanistic evidence at the molecular level is lacking5,6. 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 animals7,8,9 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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Conplastic animals differ in metabolism and ageing profiles.
Figure 2: Transcriptomic, metabolomics and proteomics adaptations to mtDNA haplotype.
Figure 3: Functional differences among conplastic strains.
Figure 4: Proteostasis, mtUPR and dynamics.

Accession codes

Primary accessions

Gene Expression Omnibus

Data deposits

RNA-seq data was deposited at GEO under accession number GSE56933.


  1. Wallace, D. C. Mitochondrial DNA variation in human radiation and disease. Cell 163, 33–38 (2015)

    Article  CAS  Google Scholar 

  2. Ruiz-Pesini, E. et al. Human mtDNA haplogroups associated with high or reduced spermatozoa motility. Am. J. Hum. Genet. 67, 682–696 (2000)

    Article  CAS  Google Scholar 

  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)

    Article  CAS  Google Scholar 

  4. Baudouin, S. V. et al. Mitochondrial DNA and survival after sepsis: a prospective study. Lancet 366, 2118–2121 (2005)

    Article  Google Scholar 

  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)

    Article  CAS  Google Scholar 

  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)

    Article  CAS  Google Scholar 

  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)

    Article  CAS  Google Scholar 

  8. Yu, X. et al. Dissecting the effects of mtDNA variations on complex traits using mouse conplastic strains. Genome Res. 19, 159–165 (2009)

    Article  CAS  Google Scholar 

  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)

    Article  CAS  Google Scholar 

  10. Ross, J. M. et al. Germline mitochondrial DNA mutations aggravate ageing and can impair brain development. Nature 501, 412–415 (2013)

    Article  ADS  CAS  Google Scholar 

  11. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013)

    Article  Google Scholar 

  12. 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)

    Article  CAS  Google Scholar 

  13. 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)

    Article  Google Scholar 

  14. 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)

    Article  CAS  Google Scholar 

  15. Lapuente-Brun, E. et al. Supercomplex assembly determines electron flux in the mitochondrial electron transport chain. Science 340, 1567–1570 (2013)

    Article  ADS  CAS  Google Scholar 

  16. Guarás, A. et al. The CoQH2/CoQ ratio serves as a sensor of respiratory chain efficiency. Cell Rep. 15, 197–209 (2016)

    Article  Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. Houtkooper, R. H. et al. Mitonuclear protein imbalance as a conserved longevity mechanism. Nature 497, 451–457 (2013)

    Article  ADS  CAS  Google Scholar 

  19. Kelso, G. F. et al. Selective targeting of a redox-active ubiquinone to mitochondria within cells: antioxidant and antiapoptotic properties. J. Biol. Chem. 276, 4588–4596 (2001)

    Article  CAS  Google Scholar 

  20. Tachibana, M. et al. Mitochondrial gene replacement in primate offspring and embryonic stem cells. Nature 461, 367–372 (2009)

    Article  ADS  CAS  Google Scholar 

  21. Craven, L. et al. Pronuclear transfer in human embryos to prevent transmission of mitochondrial DNA disease. Nature 465, 82–85 (2010)

    Article  ADS  CAS  Google Scholar 

  22. Chinnery, P. F. et al. The challenges of mitochondrial replacement. PLoS Genet. 10, e1004315 (2014)

    Article  Google Scholar 

  23. Logan, A. et al. Using exomarkers to assess mitochondrial reactive species in vivo. Biochim. Biophys. Acta 1840, 923–930 (2014)

    Article  CAS  Google Scholar 

  24. 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)

    Article  Google Scholar 

  25. Flores, I. et al. The longest telomeres: a general signature of adult stem cell compartments. Genes Dev. 22, 654–667 (2008)

    Article  CAS  Google Scholar 

  26. 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)

    Article  CAS  Google Scholar 

  27. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005)

    Article  ADS  CAS  Google Scholar 

  28. Martínez-Bartolomé, S. et al. Properties of average score distributions of SEQUEST: the probability ratio method. Mol. Cell. Proteomics 7, 1135–1145 (2008)

    Article  Google Scholar 

  29. 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)

    Article  CAS  Google Scholar 

  30. 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)

    Article  Google Scholar 

Download references


We thank C. López-Otín for critical reading; C. Jimenez for management; the Transgenesis Unit of the CNIC and M. Cueva for mouse work, A. Molina and R. Doohan for histology, the Genomics Unit of the CNIC for RNA-seq; ICTS-CNME (UCM) for technical support; J. Amigo and X. Bello for NGS bioinformatics support; and S. Bartlett for English editing. Supported by grants from the MEyC (SAF2015-65633-R, CSD2007-00020, SAF2012-38449, BIO2012-37926 and BIO2015-67580-P), the Madrid (CAM/P2010/BMD-2402), the EU (PCIG10-GA-2011-304217 and FP7-PEOPLE-2012-ITN-GA-317433), the ISC III (PI09-00946, PI12/01297, PI13-01136 and PI11-00078, PRB2 (IPT13/0001, ProteoRed), RIC ((RD12/0042/0045) and RETICS (RD12/0042/00056)). The CNIC is supported by the MEyC and the Pro-CNIC Foundation and is a SO-Center of Excellence (SEV-2015-0505).

Author information

Authors and Affiliations



A.L.-P., R.M.-L., A.V.L.-V., R.A.-P. and S.C. performed experimental work. F.S.-C. and C.T. analysed genomic and transcriptomic data. E.A. and I.F. performed the telomere analysis. A.G.-G., M.L.B.-M. and E.R. performed the mouse phenotyping. A.L. and M.P.M. did the assessment of ROS levels. R.C., B.S. and A.C. analysed the microarrays and mtDNA next-generation sequencing. A.V.L.-V. and J.R.-C. analysed the metabolomics. E.C. and J.V. performed the proteomics. J.A.E., A.L.-P., R.M.-L., A.P.-M. and P.F.-S. contributed to design and analysis. A.L.-P. and J.A.E. wrote the manuscript. J.A.E. directed and designed the research.

Corresponding author

Correspondence to José Antonio Enríquez.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Conplastic mouse generation and nuclear genome identity.

a, Schematic of the conplastic animal concept. b, Sequence differences between NZB and C57 mtDNAs by next-generation sequencing. See also Extended Data Table 1. c, Cluster dendrogram based on the expressed genome of the indicated mouse strain and the RNA-seq-derived expressed genome of conplastic animals. d, Comparison of nuclear single nucleotide polymorphism (SNP) differences between the indicated mouse lines by Affimetrix Mouse Diversity Genotyping Array analysis. e, Percentage of genotype differences between the mouse nuclear DNA SNPs identified by RNA-seq; the number of SNPs compared is indicated.

Extended Data Figure 2 Young conplastic mouse phenotyping.

a, Weight-gain during early life (BL/6NZB, BL/6C57, and NZBNZB separated by sex, n = 10 per group). b, Organ-weight to body-weight ratios separated by sex (n = 10 per group, ANOVA test). c, Activity profiles (BL/6NZB, BL/6C57, and NZBNZB separated by sex, 10-week-old mice, n = 10 per group). d, Area under the curve analysis of the activities shown in c, providing estimated activity intensities of the different strains. e, Ratios of activity to respiratory parameters in the different mouse strains. Data are means ± s.d., *P < 0.05, two-tailed t-test.

Extended Data Figure 3 Ageing phenotype of conplastic mice.

a, Two-year-old mice were scored for conspicuous signs of the indicated ageing parameters (0 (none) to 3 (high))10. b, Representative image of 2-year-old animals. c, Tumours incidence detected upon liver necropsy of animals dying from natural causes (BL/6NZB, n = 14; BL/6C57, n = 20 mice). d, Representative haematoxylin and eosin stained paraffin sections of liver adenoma, carcinoma, histiocytic sarcoma and hemangiosarcoma. e, Representative histological preparations of ovaries from animals of the indicated age and genotype. f, Lipofuscin accumulation over the reproductive period of conplastic females (n = 5 females per genotype and age) *P < 0.05, two-tailed t-test.

Extended Data Figure 4 Additional transcriptomic data.

a, Chord plot comparing liver expression of PPARα-regulated genes in conplastic mice. The chord plot shows gene changes (logFC) and relates them to their functional categories. b, Gene set enrichment analysis (GSEA), showing false discovery rate (FDR) estimates of significant and non-significant differences between conplastic mice for liver gene expression linked to the indicated processes.

Extended Data Figure 5 Differentially regulated metabolites and metabolic pathways in liver and plasma.

a, PLS-DA-identified metabolites responsible for differential metabolic signatures (P, plasma; L, liver). The metabolite list was cross-validated, and only metabolites present in 50% or more iterations were considered. Colour highlighting indicates metabolites as potential markers of the indicated metabolic route. b, Glutathione metabolic pathway and heat map showing MSEA assessment of all detected metabolites implicated in GSH metabolism, regardless of whether they were identified as relevant in PLS-DA (red text indicates PLS-DA relevant), and of selected oxidative stress metabolic biomarkers. These data are also shown in Fig. 2d. c, Heat map of detected metabolites implicated in bile acid and xenobiotic metabolism. d, Heat map of detected metabolites implicated in phospholipid metabolism. Heat maps are accompanied by MSEA assessment of significant (red) or nonsignificant differences (FDR). Colour-coding in all heat maps represents the log fold difference between BL/6NZB and BL/6C57 mice (black, not detected; white, no change (log fc = 1); red, trend for enrichment in BL/6NZB samples (log fc >1); yellow, trend for depletion in BL/6NZB samples (log fc <1).

Extended Data Figure 6 Quantitative proteomics analysis of mitochondrial complexes and functional categories.

a, Blue native electrophoresis of liver samples from 8-, 12-, 16- and 50-week-old conplastic mice. Top: complex I, (Ndufa9); middle: complex III (core-2); bottom: complex IV (Cox5b) and Tom20. b, Ratio of supercomplexed to free complex I, determined from the CI western blot in a. c, Relative abundance of component proteins of CI, CIV and CIII detected by iTRAQ quantitative proteomics in heart and liver of the indicated mouse strain at 12 weeks and 2 years of age. Each dot represents a single protein; red dots represent mtDNA-encoded proteins. d, Relative abundance of proteins of the TCA cycle, glycolysis, or β-oxidation detected by iTRAQ quantitative proteomics in heart and liver of the indicated mouse strain at 12 weeks old and 2 years of age. The four succinate dehydrogenase (complex II) subunits are highlighted in green. The iTRAQ-detected proteins and Zq values represented in c and d are compiled in Supplementary Data 4. Data are means ± s.d., ANOVA test. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.00001.

Extended Data Figure 7 Lipid metabolism phenotype.

a, Serum cholesterol levels (ChoT), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) in young (2 months old, n = 8 per genotype) and adult (12 months old, n = 12 per genotype). b, Three-month body-weight gain in males fed chow (n = 4 per genotype) or a HFD (n = 8 per genotype). c, Adipocyte size in epididymal fat pads of HFD-fed conplastic males (n = = 3 per genotype and diet: BL/6C57 chow diet = 368 cells, HFD = 60 cells; BL/6NZB chow diet = 353 cells, HFD = 209 cells).

Extended Data Figure 8 Mitochondrial cristae and density.

a, Representative masks of individual heart mitochondria from 12-week-old mice (n = 3 per genotype). b, The cristae area per mitochondria estimated from masks. Significance was assessed by nonparametric Mann–Whitney U-test. Bars and whiskers represent means ± s.d.; each point represents the percentage of cristae area in an individual mitochondria; (BL/6NZB, n = 33; BL/6C57, n = 31) c, d, Aspect and electro-density of subsarcolemmal (SBS) or intermyofibrillar (IMF) mitochondria from left heart ventricular apex of 12-week-old mice.

Extended Data Table 1 Sequence differences between NZB and C57 mtDNA
Extended Data Table 2 Analysis of the mtDNA mutation load

Supplementary information

Supplementary Data 1

Data from the Next generation sequencing of mtDNA from both conplastic strains at three different ages. (XLSX 116 kb)

Supplementary Data 2

Data compiling the transcripts differentially expressed in liver and heart between conplastic mouse strains. (XLSX 165 kb)

Supplementary Data 3

Compilation of the metabolomic analysis of plasma and liver from complastic mice strains. (XLSX 234 kb)

Supplementary Data 4

Compilation of the iTRAQ proteomic analysis. (XLSX 71 kb)

Supplementary Information

This zipped file contains the original gel data for Extended Data Figure 5a. (ZIP 2111 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Latorre-Pellicer, A., Moreno-Loshuertos, R., Lechuga-Vieco, A. et al. Mitochondrial and nuclear DNA matching shapes metabolism and healthy ageing. Nature 535, 561–565 (2016).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing