Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

Mitochondrial DNA variants modulate N-formylmethionine, proteostasis and risk of late-onset human diseases

Nature Medicine volume 27, pages 1564–1575 (2021)Cite this article

Subjects

Abstract

Mitochondrial DNA (mtDNA) variants influence the risk of late-onset human diseases, but the reasons for this are poorly understood. Undertaking a hypothesis-free analysis of 5,689 blood-derived biomarkers with mtDNA variants in 16,220 healthy donors, here we show that variants defining mtDNA haplogroups Uk and H4 modulate the level of circulating N-formylmethionine (fMet), which initiates mitochondrial protein translation. In human cytoplasmic hybrid (cybrid) lines, fMet modulated both mitochondrial and cytosolic proteins on multiple levels, through transcription, post-translational modification and proteolysis by an N-degron pathway, abolishing known differences between mtDNA haplogroups. In a further 11,966 individuals, fMet levels contributed to all-cause mortality and the disease risk of several common cardiovascular disorders. Together, these findings indicate that fMet plays a key role in common age-related disease through pleiotropic effects on cell proteostasis.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of analysis and population structure of nuclear and mitochondrial genomes.
Fig. 2: Metabolites and their associations with mtDNA SNPs in the INTERVAL study.
Fig. 3: fMet-associated genes regulate mtDNA gene expression.
Fig. 4: fMet regulates mitochondrial protein synthesis and OSPHOS function.
Fig. 5: fMet modulates cytosolic protein homeostasis.
Fig. 6: fMet as a biomarker for IS and other late-onset disorders.

Similar content being viewed by others

Data availability

The GTEx project was supported by the Common Fund of the Office of the Director of the National Institutes of Health and by the NCI, the NHGRI, the NHLBI, the NIDA, the NIMH and the NINDS. Data used for analyses described in this study were obtained from the GTEx Portal (GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8) and dbGaP accession number phs000424.v7.p2. All data are available in the main text, the Supplementary Information or upon request to the authors. Source data are provided with this paper.

Code availability

We conducted our analyses using the following published and publicly available software: (1) for calling mtDNA variants: GATK version 4.0.3.0 HaplotypeCaller (https://gatk.broadinstitute.org/hc/en-us/articles/360037225632-HaplotypeCaller) and mtDNA-Server, local version (https://github.com/seppinho/mutserve), (2) for mtDNA association analysis using an LMM and variance decomposition analysis: LDAK version 5 (http://dougspeed.com/downloads2/); (3) for improving the power of mtDNA association: CMS version 1.0 (https://github.com/haschard/CMS); (4) for eQTL analyses: LIMIX version 3.0 (https://github.com/limix/limix); (5) for identifying pseudogenes in the nuclear genome with high sequence similarity to mtDNA: lastal 744 (http://last.cbrc.jp/doc/lastal.html); (6) for identifying PEER factors that capture unknown confounding in gene expression data: PEER version 1.3 (https://github.com/PMBio/peer); (7) for differential expression analysis: edgeR version 3.11 (http://bioconductor.org/packages/release/bioc/html/edgeR.html); (8) for GSEA: GSEA version 4.1.0 (https://www.gsea-msigdb.org/gsea/index.jsp); (9) for flow cytometry analysis: FlowJo version 10.2.

References

  1. Anderson, S. et al. Sequence and organization of the human mitochondrial genome. Nature 290, 457–465 (1981).

    Article  CAS  PubMed  Google Scholar 

  2. Giles, R. E., Blanc, H., Cann, H. M. & Wallace, D. C. Maternal inheritance of human mitochondrial DNA. Proc. Natl Acad. Sci. USA 77, 6715–6719 (1980).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Howell, N. Mutational analysis of the human mitochondrial genome branches into the realm of bacterial genetics. Am. J. Hum. Genet. 59, 749–755 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Lippold, S. et al. Human paternal and maternal demographic histories: insights from high-resolution Y chromosome and mtDNA sequences. Investig. Genet. 5, 13 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Stone, A. C. & Stoneking, M. mtDNA analysis of a prehistoric Oneota population: implications for the peopling of the New World. Am. J. Hum. Genet. 62, 1153–1170 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Vigilant, L., Stoneking, M., Harpending, H., Hawkes, K. & Wilson, A. C. African populations and the evolution of human mitochondrial DNA. Science 253, 1503–1507 (1991).

    Article  CAS  PubMed  Google Scholar 

  7. Ruiz-Pesini, E., Mishmar, D., Brandon, M., Procaccio, V. & Wallace, D. C. Effects of purifying and adaptive selection on regional variation in human mtDNA. Science 303, 223–226 (2004).

    Article  CAS  PubMed  Google Scholar 

  8. Lane, N. & Martin, W. The energetics of genome complexity. Nature 467, 929–934 (2010).

    Article  CAS  PubMed  Google Scholar 

  9. Ji, F. et al. Mitochondrial DNA variant associated with Leber hereditary optic neuropathy and high-altitude Tibetans. Proc. Natl Acad. Sci. USA 109, 7391–7396 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Gómez-Durán, A. et al. Unmasking the causes of multifactorial disorders: OXPHOS differences between mitochondrial haplogroups. Hum. Mol. Genet. 19, 3343–3353 (2010).

    Article  PubMed  CAS  Google Scholar 

  11. Suissa, S. et al. Ancient mtDNA genetic variants modulate mtDNA transcription and replication. PLoS Genet. 5, e1000474 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Tranah, G. J. et al. Mitochondrial DNA variation in human metabolic rate and energy expenditure. Mitochondrion 11, 855–861 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Gorman, G. S. et al. Prevalence of nuclear and mitochondrial DNA mutations related to adult mitochondrial disease. Ann. Neurol. 77, 753–759 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ruiz-Pesini, E. et al. An enhanced MITOMAP with a global mtDNA mutational phylogeny. Nucleic Acids Res. 35, D823–D828 (2007).

    Article  CAS  PubMed  Google Scholar 

  15. Stewart, J. B. & Chinnery, P. F. The dynamics of mitochondrial DNA heteroplasmy: implications for human health and disease. Nat. Rev. Genet. 16, 530–542 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Zong, W.-X., Rabinowitz, J. D. & White, E. Mitochondria and cancer. Mol. Cell 61, 667–676 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Larman, T. C. et al. Spectrum of somatic mitochondrial mutations in five cancers. Proc. Natl Acad. Sci. USA 109, 14087–14091 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Yuan, Y. et al. Comprehensive molecular characterization of mitochondrial genomes in human cancers. Nat. Genet. 52, 342–352 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).

    Article  CAS  Google Scholar 

  20. Marom, S., Friger, M. & Mishmar, D. MtDNA meta-analysis reveals both phenotype specificity and allele heterogeneity: a model for differential association. Sci. Rep. 7, 43449 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Bi, R. et al. Mitochondrial DNA haplogroup B5 confers genetic susceptibility to Alzheimer’s disease in Han Chinese. Neurobiol. Aging 36, 1604.e7–1604.e16 (2015).

    Article  CAS  Google Scholar 

  22. Hudson, G. et al. Two-stage association study and meta-analysis of mitochondrial DNA variants in Parkinson disease. Neurology 80, 2042–2048 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Chinnery, P. F., Elliott, H. R., Syed, A. & Rothwell, P. M. & Oxford Vascular Study. Mitochondrial DNA haplogroups and risk of transient ischaemic attack and ischaemic stroke: a genetic association study. Lancet Neurol. 9, 498–503 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Nishigaki, Y. et al. Mitochondrial haplogroup N9b is protective against myocardial infarction in Japanese males. Hum. Genet. 120, 827–836 (2007).

    Article  CAS  PubMed  Google Scholar 

  25. Kofler, B. et al. Mitochondrial DNA haplogroup T is associated with coronary artery disease and diabetic retinopathy: a case control study. BMC Med. Genet. 10, 35 (2009).

  26. Chinnery, P. F. et al. Mitochondrial DNA haplogroups and type 2 diabetes: a study of 897 cases and 1010 controls. J. Med. Genet. 44, e80 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Poulton, J. et al. Type 2 diabetes is associated with a common mitochondrial variant: evidence from a population-based case–control study. Hum. Mol. Genet. 11, 1581–1583 (2002).

    Article  CAS  PubMed  Google Scholar 

  28. Hudson, G., Gomez-Duran, A., Wilson, I. J. & Chinnery, P. F. Recent mitochondrial DNA mutations increase the risk of developing common late-onset human diseases. PLoS Genet. 10, e1004369 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Wallace, D. C. A mitochondrial paradigm of metabolic and degenerative diseases, aging, and cancer: a dawn for evolutionary medicine. Annu. Rev. Genet. 39, 359–407 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chinnery, P. F. & Gomez-Duran, A. Oldies but Goldies mtDNA population variants and neurodegenerative diseases. Front. Neurosci. 12, 682 (2018).

  31. Trounce, I., Neill, S. & Wallace, D. C. Cytoplasmic transfer of the mtDNA nt 8993 T→G (ATP6) point mutation associated with Leigh syndrome into mtDNA-less cells demonstrates cosegregation with a decrease in state III respiration and ADP/O ratio. Proc. Natl Acad. Sci. USA 91, 8334–8338 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Mattiazzi, M. The mtDNA T8993G (NARP) mutation results in an impairment of oxidative phosphorylation that can be improved by antioxidants. Hum. Mol. Genet. 13, 869–879 (2004).

    Article  CAS  PubMed  Google Scholar 

  33. Goto, Y., Nonaka, I. & Horai, S. A mutation in the tRNALeu(UUR) gene associated with the MELAS subgroup of mitochondrial encephalomyopathies. Nature 348, 651–653 (1990).

    Article  CAS  PubMed  Google Scholar 

  34. van den Ouweland, J. M. et al. Maternally inherited diabetes and deafness is a distinct subtype of diabetes and associates with a single point mutation in the mitochondrial tRNALeu(UUR) gene. Diabetes 43, 746–751 (1994).

    Article  PubMed  Google Scholar 

  35. Brown, M. D., Trounce, I. A., Jun, A. S., Allen, J. C. & Wallace, D. C. Functional analysis of lymphoblast and cybrid mitochondria containing the 3460, 11778, or 14484 Leber’s hereditary optic neuropathy mitochondrial DNA mutation. J. Biol. Chem. 275, 39831–39836 (2000).

    Article  CAS  PubMed  Google Scholar 

  36. Moore, C. et al. The INTERVAL trial to determine whether intervals between blood donations can be safely and acceptably decreased to optimise blood supply: study protocol for a randomised controlled trial. Trials 15, 363 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  37. GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

  38. Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2018).

  39. Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Shin, S.-Y. et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46, 543–550 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Speed, D. et al. Reevaluation of SNP heritability in complex human traits. Nat. Genet. 49, 986–992 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Aschard, H. et al. Covariate selection for association screening in multiphenotype genetic studies. Nat. Genet. 49, 1789–1795 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Derenko, M. et al. Western Eurasian ancestry in modern Siberians based on mitogenomic data. BMC Evol. Biol. 14, 217 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Day, N. et al. EPIC-Norfolk: study design and characteristics of the cohort. European Prospective Investigation of Cancer. Br. J. Cancer 80, 95–103 (1999).

    PubMed  Google Scholar 

  45. Rajbhandary, U. L. & Ming Chow, C. Initiator tRNAs and initiation of protein synthesis. in tRNA: Structure, Biosynthesis, and Function (eds Söll, D. & RajBhandary, U. L.) Ch. 5, 511–528 (American Society for Microbiology, 1994).

  46. Tucker, E. J. et al. Mutations in MTFMT underlie a human disorder of formylation causing impaired mitochondrial translation. Cell Metab. 14, 428–434 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. King, M. P. & Attardi, G. Human cells lacking mtDNA: repopulation with exogenous mitochondria by complementation. Science 246, 500–503 (1989).

    Article  CAS  PubMed  Google Scholar 

  48. Picard, M. et al. Progressive increase in mtDNA 3243A>G heteroplasmy causes abrupt transcriptional reprogramming. Proc. Natl Acad. Sci. USA 111, E4033–E4042 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. King, M. P., Koga, Y., Davidson, M. & Schon, E. A. Defects in mitochondrial protein synthesis and respiratory chain activity segregate with the tRNALeu(UUR) mutation associated with mitochondrial myopathy, encephalopathy, lactic acidosis, and strokelike episodes. Mol. Cell. Biol. 12, 480–490 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Arguello, T., Köhrer, C., RajBhandary, U. L. & Moraes, C. T. Mitochondrial methionyl-formylation affects steady-state levels of oxidative phosphorylation complexes and their organization into supercomplexes. J. Biol. Chem. 293, 15021–15032 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Hinttala, R. et al. An N-terminal formyl methionine on COX 1 is required for the assembly of cytochrome c oxidase. Hum. Mol. Genet. 24, 4103–4113 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Neeve, V. C. M. et al. Clinical and functional characterisation of the combined respiratory chain defect in two sisters due to autosomal recessive mutations in MTFMT. Mitochondrion 13, 743–748 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Nijtmans, L. G. J., Henderson, N. S. & Holt, I. J. Blue Native electrophoresis to study mitochondrial and other protein complexes. Methods 26, 327–334 (2002).

    Article  CAS  PubMed  Google Scholar 

  54. Wek, R. C., Jiang, H.-Y. & Anthony, T. G. Coping with stress: eIF2 kinases and translational control. Biochem. Soc. Trans. 34, 7–11 (2006).

    Article  CAS  PubMed  Google Scholar 

  55. Ameri, K. & Harris, A. L. Activating transcription factor 4. Int. J. Biochem. Cell Biol. 40, 14–21 (2008).

    Article  CAS  PubMed  Google Scholar 

  56. Su, N. & Kilberg, M. S. C/EBP homology protein (CHOP) interacts with activating transcription factor 4 (ATF4) and negatively regulates the stress-dependent induction of the asparagine synthetase gene. J. Biol. Chem. 283, 35106–35117 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Quirós, P. M. et al. Multi-omics analysis identifies ATF4 as a key regulator of the mitochondrial stress response in mammals. J. Cell Biol. 216, 2027–2045 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Walter, P. & Ron, D. The unfolded protein response: from stress pathway to homeostatic regulation. Science 334, 1081–1086 (2011).

    Article  CAS  PubMed  Google Scholar 

  59. Kim, J.-M. et al. Formyl-methionine as an N-degron of a eukaryotic N-end rule pathway. Science 362, eaat0174 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Eldeeb, M. A., Fahlman, R. P., Esmaili, M. & Fon, E. A. Formylation of eukaryotic cytoplasmic proteins: linking stress to degradation. Trends Biochem. Sci. 44, 181–183 (2019).

    Article  CAS  PubMed  Google Scholar 

  61. 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  PubMed  Google Scholar 

  62. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Pacheu-Grau, D., Gómez-Durán, A., López-Pérez, M. J., Montoya, J. & Ruiz-Pesini, E. Mitochondrial pharmacogenomics: barcode for antibiotic therapy. Drug Discov. Today 15, 33–39 (2010).

    Article  CAS  PubMed  Google Scholar 

  64. Pello, R. et al. Mitochondrial DNA background modulates the assembly kinetics of OXPHOS complexes in a cellular model of mitochondrial disease. Hum. Mol. Genet. 17, 4001–4011 (2008).

    Article  CAS  PubMed  Google Scholar 

  65. Bianchetti, R., Lucchini, G., Crosti, P. & Tortora, P. Dependence of mitochondrial protein synthesis initiation on formylation of the initiator methionyl-tRNAf. J. Biol. Chem. 252, 2519–2523 (1977).

    Article  CAS  PubMed  Google Scholar 

  66. Sorrentino, V. et al. Enhancing mitochondrial proteostasis reduces amyloid-β proteotoxicity. Nature 552, 187–193 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Eldeeb, M. A., Fahlman, R. P., Esmaili, M. & Ragheb, M. A. Regulating apoptosis by degradation: the N-end rule-mediated regulation of apoptotic proteolytic fragments in mammalian cells. Int. J. Mol. Sci. 19, 3414 (2018).

  68. Shemorry, A., Hwang, C.-S. & Varshavsky, A. Control of protein quality and stoichiometries by N-terminal acetylation and the N-end rule pathway. Mol. Cell 50, 540–551 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Wrobel, L. et al. Mistargeted mitochondrial proteins activate a proteostatic response in the cytosol. Nature 524, 485–488 (2015).

    Article  CAS  PubMed  Google Scholar 

  70. Couvillion, M. T., Soto, I. C., Shipkovenska, G. & Churchman, L. S. Synchronized mitochondrial and cytosolic translation programs. Nature 533, 499–503 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Suhre, K. et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun. 8, 14357 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Yao, C. et al. Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease. Nat. Commun. 9, 3268 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Di Angelantonio, E. Efficiency and safety of varying the frequency of whole blood donation (INTERVAL): a randomised trial of 45 000 donors. Lancet 390, 2360–2371 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Weissensteiner, H. et al. mtDNA-Server: next-generation sequencing data analysis of human mitochondrial DNA in the cloud. Nucleic Acids Res. 44, W64–W69 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.1–11.10.33 (2013).

    Article  Google Scholar 

  80. 1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  CAS  Google Scholar 

  81. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Kiełbasa, S. M., Wan, R., Sato, K., Horton, P. & Frith, M. C. Adaptive seeds tame genomic sequence comparison. Genome Res. 21, 487–493 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Lippert, C., Casale, F. P., Rakitsch, B. & Stegle, O. LIMIX: genetic analysis of multiple traits. Preprint at bioRxiv https://doi.org/10.1101/003905 (2014).

  85. 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  CAS  PubMed  PubMed Central  Google Scholar 

  86. Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    Article  CAS  PubMed  Google Scholar 

  87. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Koboldt, D. C. et al. VarScan: variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics 25, 2283–2285 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Wilm, A. et al. LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets. Nucleic Acids Res. 40, 11189–11201 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Torroni, A. et al. Classification of European mtDNAs from an analysis of three European populations. Genetics 144, 1835–1850 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. van Oven, M. & Kayser, M. Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation. Hum. Mutat. 30, E386–E394 (2009).

    Article  PubMed  Google Scholar 

  95. Weissensteiner, H. et al. HaploGrep 2: mitochondrial haplogroup classification in the era of high-throughput sequencing. Nucleic Acids Res. 44, W58–W63 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Pereira, L., Soares, P., Radivojac, P., Li, B. & Samuels, D. C. Comparing phylogeny and the predicted pathogenicity of protein variations reveals equal purifying selection across the global human mtDNA diversity. Am. J. Hum. Genet. 88, 433–439 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Levin, L., Zhidkov, I., Gurman, Y., Hawlena, H. & Mishmar, D. Functional recurrent mutations in the human mitochondrial phylogeny: dual roles in evolution and disease. Genome Biol. Evol. 5, 876–890 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  98. Bustin, S. A. et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622 (2009).

    Article  CAS  PubMed  Google Scholar 

  99. Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein–dye binding. Anal. Biochem. 72, 248–254 (1976).

    Article  CAS  PubMed  Google Scholar 

  100. Wittig, I., Braun, H.-P. & Schägger, H. Blue native PAGE. Nat. Protoc. 1, 418–428 (2006).

    Article  CAS  PubMed  Google Scholar 

  101. Yarnall, A. J. et al. Characterizing mild cognitive impairment in incident Parkinson disease: the ICICLE-PD study. Neurology 82, 308–316 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Gibb, W. R. & Lees, A. J. The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 51, 745–752 (1988).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Rothwell, P. M. et al. Population-based study of event-rate, incidence, case fatality, and mortality for all acute vascular events in all arterial territories (Oxford Vascular Study). Lancet 366, 1773–1783 (2005).

    Article  CAS  PubMed  Google Scholar 

  104. Floros, V. I. et al. Segregation of mitochondrial DNA heteroplasmy through a developmental genetic bottleneck in human embryos. Nat. Cell Biol. 20, 144–151 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Calabrese, C. et al. MToolBox: a highly automated pipeline for heteroplasmy annotation and prioritization analysis of human mitochondrial variants in high-throughput sequencing. Bioinformatics 30, 3115–3117 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Xu, H. et al. FastUniq: a fast de novo duplicates removal tool for paired short reads. PLoS ONE 7, e52249 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Participants in the INTERVAL randomized controlled trial were recruited with the active collaboration of NHS Blood and Transplant England (https://www.nhsbt.nhs.uk/), which has supported field work and other elements of the trial. DNA extraction and genotyping was co-funded by the NIHR, the NIHR BioResource (http://bioresource.nihr.ac.uk) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014)*. The academic coordinating center for INTERVAL was supported by core funding from the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR, BTRU-2014-10024), the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (SP/09/002, RG/13/13/30194, RG/18/13/33946) and the NIHR Cambridge BRC (BRC-1215-20014)*. A complete list of investigators for and contributors to the INTERVAL trial is provided in ref. 73. The academic coordinating center thanks blood donor center staff and blood donors for participating in the INTERVAL trial. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division (Welsh Government), the Public Health Agency (Northern Ireland), the British Heart Foundation and Wellcome. N.C. is supported by an EBI–Sanger Postdoctoral Fellowship. A.G.-D. receives funding from the NIHR Biomedical Research Centre based at Cambridge University Hospitals NHS Foundation Trust. E.Y.-D. was funded by the Isaac Newton Trust/Wellcome Trust ISSF/University of Cambridge Joint Research Grants Scheme. J.M.M.H. was funded by the NIHR Cambridge BRC (BRC-1215-20014)*. P.F.C. is a Wellcome Trust Principal Research Fellow (212219/Z/18/Z) and a UK NIHR Senior Investigator, who receives support from the Medical Research Council Mitochondrial Biology Unit (MC_UU_00015/9), the Evelyn Trust and the NIHR Biomedical Research Centre based at Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge. C.H.W.-G. is supported by a RCUK/UKRI Research Innovation Fellowship awarded by the Medical Research Council (MR/R007446/1) and by the Cambridge Centre for Parkinson-Plus. N.S. is supported by the Wellcome Trust (grant number 098051 until 30 September 2016 and 206194 from 1 October 2016), the Cambridge BHF Centre of Research Excellence (RE/18/1/34212) and the NIHR Cambridge Biomedical Research Centre Biomedical Resources Grant, University of Cambridge, Cardiovascular Theme (RG64226). J.D. holds a British Heart Foundation Professorship and an NIHR Senior Investigator Award*. R.A.L. is supported by grants from Parkinson’s UK. Sequencing of INTERVAL samples was supported by the Wellcome Trust (grant number 206194). Metabolon metabolomics assays were funded by the NIHR BioResource, the Wellcome Trust (grant number 206194) and the NIHR Cambridge BRC (BRC-1215-20014)*. Nightingale Health NMR assays were funded by the European Commission Framework Programme 7 (HEALTH-F2-2012-279233). SomaLogic assays were funded by Merck and the NIHR Cambridge BRC (BRC-1215-20014)*. ICICLE-PD was funded by Parkinson’s UK (J-0802, G-1301, G-1507) and the Lockhart Parkinson’s Disease Research Fund and supported by the NIHR Newcastle Biomedical Research Unit and the NIHR Cambridge Biomedical Research Centre (146281). The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1 and MC-UU_12015/1) and Cancer Research UK (C864/A14136). Genetic work in the EPIC-Norfolk study was funded by the Medical Research Council (MC_PC_13048). Metabolite measurements in the EPIC-Norfolk study were supported by the MRC Cambridge Initiative in Metabolic Science (MR/L00002/1) and the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372. We are grateful to all participants who have been part of the project and to the many members of the study teams at the University of Cambridge who have enabled this research. The collection of samples and assay work performed on the Oxford Vascular Study cohort was funded by the NIHR Biomedical Research Centre, Oxford, and the Wellcome Trust. *The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. We thank all members of the ICICLE-PD consortium for their support of this work.

Author information

Author notes

  1. These authors contributed equally: Na Cai, Aurora Gomez-Duran.

  2. These authors jointly supervised this work: Joanna M. M. Howson, Oliver Stegle, Patrick F. Chinnery, Nicole Soranzo.

Authors and Affiliations

  1. Human Genetics Department, Wellcome Sanger Institute (WT), Hinxton, UK

    Na Cai, Kousik Kundu, Willem H. Ouwehand, John Danesh, Klaudia Walter & Nicole Soranzo

  2. European Bioinformatics Institute (EMBL-EBI), Hinxton, UK

    Na Cai, Marc J. Bonder & Oliver Stegle

  3. Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany

    Na Cai

  4. Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK

    Aurora Gomez-Duran, Zoe J. Golder, Claudia Calabrese, Marta Camacho, Caroline H. Williams-Gray & Patrick F. Chinnery

  5. Medical Research Council Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK

    Aurora Gomez-Duran, Zoe J. Golder, Claudia Calabrese & Patrick F. Chinnery

  6. Centro de Investigaciones Biológicas Margarita Salas, Consejo Superior de Investigaciones Científicas (CIB-CSIC), Madrid, Spain

    Aurora Gomez-Duran

  7. British Heart Foundation Cardiovascular Epidemiology Unit, Department of Primary Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Ekaterina Yonova-Doing, Emanuele Di Angelantonio, Adam S. Butterworth, John Danesh & Joanna M. M. Howson

  8. Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK

    Ekaterina Yonova-Doing & Joanna M. M. Howson

  9. Wolfson Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK

    Annette I. Burgess, Lixin Li & Peter M. Rothwell

  10. Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Marc J. Bonder & Oliver Stegle

  11. Translational and Clinical Research Institute, Newcastle University, Newcastle, UK

    Rachael A. Lawson

  12. British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK

    Emanuele Di Angelantonio, Willem H. Ouwehand, Adam S. Butterworth, John Danesh & Nicole Soranzo

  13. National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK

    Emanuele Di Angelantonio, David J. Roberts, Adam S. Butterworth, John Danesh & Nicole Soranzo

  14. Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK

    Emanuele Di Angelantonio, Adam S. Butterworth & John Danesh

  15. NHS Blood and Transplant—Oxford Centre, John Radcliffe Hospital, Oxford, UK

    David J. Roberts

  16. Radcliffe Department of Medicine, University of Oxford, Oxford, UK

    David J. Roberts

  17. NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge, UK

    Nick A. Watkins & Willem H. Ouwehand

  18. Department of Haematology, University of Cambridge, Cambridge, UK

    Willem H. Ouwehand & Nicole Soranzo

  19. MRC Epidemiology Unit, University of Cambridge, Cambridge, UK

    Isobel D. Stewart, Maik Pietzner, Nick J. Wareham & Claudia Langenberg

  20. European Molecular Biology Laboratory, Heidelberg, Germany

    Oliver Stegle

  21. Genomics Research Centre, Human Technopole, Milan, Italy

    Nicole Soranzo

Authors
  1. Na Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aurora Gomez-Duran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ekaterina Yonova-Doing
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kousik Kundu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Annette I. Burgess
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zoe J. Golder
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Claudia Calabrese
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Marc J. Bonder
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Marta Camacho
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Rachael A. Lawson
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Lixin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Caroline H. Williams-Gray
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Emanuele Di Angelantonio
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. David J. Roberts
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Nick A. Watkins
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Willem H. Ouwehand
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Adam S. Butterworth
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Isobel D. Stewart
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Maik Pietzner
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Nick J. Wareham
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Claudia Langenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. John Danesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Klaudia Walter
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Peter M. Rothwell
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Joanna M. M. Howson
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Oliver Stegle
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Patrick F. Chinnery
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Nicole Soranzo
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

ICICLE-PD Study Group

  • Caroline H. Williams-Gray

Contributions

N.C., A.G.-D., J.M.M.H., O.S., N.S. and P.F.C. conceived the study; N.C., A.G.-D., O.S., N.S. and P.F.C. designed the analyses and experiments; N.C. performed the quantitative genetics analyses; A.G.-D. performed the wet lab experiments; E.Y.-D. and J.M.M.H. obtained replication data and performed the replication of quantitative genetic analyses; A.G.-D., A.I.B., L.L. and Z.G. performed the wet lab experiments on cell lines and patient cohorts; N.C. and C.C. performed the analysis on the patient cohorts; I.D.S. and M.P. performed the analysis on the prospective cohort; N.C., A.G.-D., O.S., N.S. and P.F.C. interpreted the results; N.S., P.F.C., O.S., J.M.M.H., E.D.A., D.J.R., W.H.O., A.S.B., J.D., P.M.R., A.I.B., M.C., R.A.L., C.H.W.-G., N.J.W. and C.L. acquired the resources and datasets; N.C., K.K., M.J.B, and K.W. curated the data and wrote the bioinformatics methods; N.S., P.F.C., J.M.M.H. and O.S. supervised the project; N.S. and P.F.C. acquired the funding and administered the project; N.C., A.G.-D., O.S., N.S., and P.F.C. wrote the paper with input from all authors, who approved the final version of the manuscript. N.C. and A.G.-D. contributed equally as first authors; E.Y.-D., K.K., A.I.B. and Z.J.G. contributed equally as second authors; J.M.M.H., O.S., N.S. and P.F.C. jointly supervised the work.

Corresponding authors

Correspondence to Oliver Stegle, Patrick F. Chinnery or Nicole Soranzo.

Ethics declarations

Competing interests

J.M.M.H. and E.Y.-D. are full-time employees of Novo Nordisk Ltd. A.S.B. has received grants outside of this work from AstraZeneca, Biogen, BioMarin, Bioverativ, Merck and Sanofi and personal fees from Novartis outside of this work. J.D. sits on the International Cardiovascular and Metabolic Advisory Board for Novartis (since 2010); the Steering Committee of the UK Biobank (since 2011); the Scientific Advisory Committee for Sanofi (since 2013); the International Cardiovascular and Metabolism Research and Development Portfolio Committee for Novartis; the AstraZeneca Genomics Advisory Board (2018); and is an MRC International Advisory Group member, London (since 2013); and an MRC High Throughput Science ‘Omics Panel Member, London (since 2013).

Additional information

Peer review information Nature Medicine thanks Wolfram Kunz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Joao Monteiro was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 fMet regulatory pathways showed no differences between the haplogroups.

A. Representation of metabolic pathway of synthesis and degradation of fMet. B. One Carbon metabolism proteins and MTFMT. Immunoblot showing the effects of the haplogroup on the expression of fMet regulatory protein MTFMT and one-carbon metabolism. The blots were immuno-detected using an anti-MTFMT, anti-MTFHD1, anti- MTF2D1, anti-SHMT1 and B-actin as loading control. Quantification of the bands by loading control (Vinculin and/or B/Actin) in each cell line. C. Effect of mitochondrial haplogroup on the expression of fMet regulatory genes. D. Quantification of formate levels. Statistical testing was performed by unpaired t-test. Normality was assessed using the Kolmogorov–Smirnov test. Bars/dots represent the mean ± SD of the biological replicates (n=4 cell lines with independent mtDNAs for each haplogroup). Error bars represent the mean ± SD of the biological replicates (n=4 cell lines with independent mtDNAs for each haplogroup) of – (Control) and + (fMet treated) cell lines. Colors red and blue represent haplogroup H and Uk respectively. The values are represented as relative to the average of untreated samples from haplogroup H unless indicated. Statistical testing was performed by using a 2-way-ANOVA test followed by Holm-Sidak’s multiple comparisons test unless stated otherwise. Exact P-values corrected for multiple comparisons are indicated. E. fMet determination in serum from controls and patients carrying the mtDNA variant m.3243G. The values are represented as relative to control serums compared to the m.3243G ones; the line in the box plot represents the mean ± SD. Box plots representing minimum, the maximum, the sample median, and the first and third quartiles including n= 29 and n=10 samples from controls and carrying the m.3243G, respectively. Statistical testing was performed by using non-parametric Mann-Whitney test. Unprocessed Western Blots can be found in Source Data Extended Data Figure 1.

Source data

Extended Data Fig. 2 fMet supplementation is similar to the differences between mitochondrial haplogroups in INTERVAL.

A. Relative increase of fMet after supplementation on cybrids (143B) and primary fibroblasts n=3-8 independent cell lines were analysed in 3 independent technical replicates per sample. The values are represented as relative to untreated and the error bars represent the mean ± SD of the biological replicates. The line represents untreated cells in each cell line. B. Boxplot of fMet levels in INTERVAL participants between individuals from the haplogroup H and Uk. (P= 1.7x10−7). The centre of the boxplots show the median fMet levels, upper and lower limits of boxplots show the interquartile range, while the whiskers show values within 1.5 times the interquartile range. Outliers show values beyond 1.5 times the interquartile range.

Extended Data Fig. 3 mtDNA variant effects on expression of MT-CO3 in 41 GTEx tissues.

This Fig. shows the standardized effect size (BETA) of the mtDNA SNPs on expression levels log10(TPM+1) of mtDNA encoded MT-CO3 in Complex IV in 41 GTEx tissues. The colour of the boxes corresponds to the broad tissue type. The centre of the boxplot shows the median BETA value from all mtDNA SNPs; upper and lower limits of boxplots show the interquartile range, while the whiskers show values within 1.5 times the interquartile range. Outliers show values beyond 1.5 times the interquartile range. The upper panel shows the results for the 15 fMet-associated SNPs, while the bottom panel shows the results from the non-fMet associated mtDNA SNPs. The fMet-associated SNPs have negative BETAs across all 41 tissues in GTEx.

Extended Data Fig. 4 Formyl-methionine regulates mitochondrial protein synthesis and oxidative phosphorylation.

A. Immunoblot showing the effects of decrease on the levels of MTFMT using siRNA against MTFMT. The blots were immuno-detected using an anti-MTFMT antibody, anti-MT-CO1 and B-actin as loading control. B. Effects of decreasing the levels of MTFMT on the levels of MT-CO3 expression. Bars represent the mean ± SD of 4 technical replicates corrected by the Negative siRNA. Statistical testing was performed by using a 2-way-ANOVA test followed by Holm-Sidak’s multiple comparisons test. Exact p-values are indicated. C. Effect of fMet on mitochondrial proteins. Immunoblot showing the effects of the increase on the levels of fMet in cell lines from the haplogroup H and Uk. The blots were immuno-detected using an anti-NDUFS1, anti-NDUFS2, anti-antibody, anti-SDHA, anti-MT-CO1, anti-UQCRQ2, anti-ATP51, anti-ATP5O and B-actin as loading control. D. Effect of fMet on the formylated protein MT-CO1. Quantification of the immunoblot shown in C. Quantification of the bands by loading control (B-actin) in each cell line. E. Effect of fMet on the non-formylated mitochondrial proteins. Quantification of the immunoblot shown in C. Quantification of the bands by loading control (B-actin) in each cell line and corrected by the average of untreated samples from haplogroup H are shown. FM indicated treatment with fMet. F. 1D-BNGE and western blot analysis of digitonin treated cybrid cell lines with and without fMet treatment. The blots were immuno-detected using an anti-NDUFS1 antibody for complex I (CI, left gel) and anti-MT-CO1 for complex IV (CIV, right gel) detection. Loading control was performed by immunodetection of anti-SDHA for complex II (CII). Super-complexes (SC) composition is indicated in each case. G. 1D-BNGE and western blot analysis of digitonin treated cybrid cell lines with and without fMet treatment. The blots were immuno-detected using an anti-UQCRQ2 antibody for complex III (CIII, left gel) and anti-ATP5A1 for complex V (CV, right gel) detection. Loading control was performed by immunodetection of anti-SDH70 for complex II (CII). Super-complexes (SC) composition is indicated in each case. H. Quantification of the bands by loading control (CII) in each cell line. I. Maximal respiration with 1μM FCCP. J. Effects of increase of fMet on mitochondrial ATP. K. Effects of increase of fMet on membrane potential, mitochondrial volume and mitochondrial ROS levels. Error bars represent the mean ± standard deviation (SD) of the biological replicates (n=4 cell lines with independent mtDNAs for each haplogroup) of – (Control) and + (fMet treated) cell lines that were measured in 3-5 independent technical replicates each. Colors red and blue represent haplogroup H and Uk respectively. The values are represented as relative to the average of untreated samples from haplogroup H unless indicated. Statistical testing was performed by using a 2-way-ANOVA test followed by Holm-Sidak’s multiple comparisons test unless stated otherwise. Exact P-values corrected for multiple comparisons are indicated. Unprocessed Western Blots can be found in Source Data Extended Data Figure 4.

Source data

Extended Data Fig. 5 fMet regulation of protein metabolism is independent of mtUPR and mTORC1.

A. Effect of fMet on EIF2A and 4EBP1 activation. Immunoblot showing the effects of the increase on the levels of fMet in cell lines from the haplogroup H and Uk. The blots were immuno-detected using an anti-EIF2A, anti-p.EIF2ASer51, anti-4EBP1, anti-p.4EBP1Thr37&46 and anti-Vinculin as loading control. B. Effect of fMet and mitochondrial haplogroup on the expression of mtUPR targets. Transcript values corrected by the average of untreated samples from haplogroup H are shown. C. Effect of fMet and mitochondrial haplogroup on 4EBP1 activation. Quantification of the immunoblot shown in A. Quantification of the bands for p. 4EBP1Thr37&46 and 4EBP1 corrected by loading control (Vinculin) in each cell line. D. Effect of fMet in doubling time (DT). E. Effect of fMet on growth. Fold change growth corrected by day 0 is shown. Error bars represent the mean ± SD of the biological replicates (n=4 cell lines with independent mtDNAs for each haplogroup) of – (Control) and + (fMet treated) cell lines that were measured in 3-5 independent technical replicates each. F. Effect of fMet on protein ubiquitination. Immunoblot detection with anti-ubiquitin and anti-B-actin as a loading control for untreated (-) and fMet treatment (+). The quantification of the bands for ubiquitin smear corrected by loading control (B-actin) is shown. Bars/lines represent the mean ± SD of the biological replicates (n= 4 cell lines with independent mtDNAs for each haplogroup) of – (Control) and + (fMet treated) cell lines. Colors red and blue represent haplogroup H and Uk respectively. The values are represented as relative to the average of untreated samples from haplogroup H unless indicated. Statistical testing was performed by using a 2-way-ANOVA test followed by Holm-Sidak’s multiple comparisons test unless stated otherwise. Exact P-values corrected for multiple comparisons are indicated. Unprocessed Western Blots can be found in Source Data Extended Data Figure 5.

Source data

Supplementary information

Supplementary Information

Members of the ICICLE-PD Study Group, Supplementary Discussion, Supplementary Figs. 1–7 and legends for Supplementary Tables 1–18.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–18.

Source data

Source Data Fig. 4

Unprocessed S-35 blots and loading.

Source Data Fig. 5

Unprocessed S-35 and western blots and loading.

Source Data Extended Data Fig. 1

Unprocessed western blots.

Source Data Extended Data Fig. 4

Unprocessed western blots.

Source Data Extended Data Fig. 5

Unprocessed western blots.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cai, N., Gomez-Duran, A., Yonova-Doing, E. et al. Mitochondrial DNA variants modulate N-formylmethionine, proteostasis and risk of late-onset human diseases. Nat Med 27, 1564–1575 (2021). https://doi.org/10.1038/s41591-021-01441-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-021-01441-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research