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.

  • Resource
  • Published:

Skeletal muscle mitochondrial interactome remodeling is linked to functional decline in aged female mice

Nature Aging volume 3, pages 313–326 (2023)Cite this article

Subjects

Abstract

Genomic, transcriptomic and proteomic approaches have been used to gain insight into molecular underpinnings of aging in laboratory animals and in humans. However, protein function in biological systems is under complex regulation and includes factors besides abundance levels, such as modifications, localization, conformation and protein–protein interactions. By making use of quantitative chemical cross-linking technologies, we show that changes in the muscle mitochondrial interactome contribute to mitochondrial functional decline in aging in female mice. Specifically, we identify age-related changes in protein cross-links relating to assembly of electron transport system complexes I and IV, activity of glutamate dehydrogenase, and coenzyme-A binding in fatty acid β-oxidation and tricarboxylic acid cycle enzymes. These changes show a remarkable correlation with complex I respiration differences within the same young–old animal pairs. Each observed cross-link can serve as a protein conformational or protein–protein interaction probe in future studies, which will provide further molecular insights into commonly observed age-related phenotypic differences. Therefore, this data set could become a valuable resource for additional in-depth molecular studies that are needed to better understand complex age-related molecular changes.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Experimental workflow.
Fig. 2: Quantitative cross-linking enables detection of reproducible changes in the interactome of aging mitochondria.
Fig. 3: Assembly of CI and CIV integrity is affected in aging muscle.
Fig. 4: Cross-link levels associated with DHE3 activation are decreased in aged mitochondria.
Fig. 5: TCA cycle and FAO.
Fig. 6: Interactome remodeling associated with changes in muscle metabolism with aging.

Similar content being viewed by others

Data availability

Mass spectrometry data have been deposited to the ProteomeXchange Consortium via the PRIDE repository under identifiers PXD031643 and PXD031644.

Cross-linking data have been uploaded to the XLinkdb online database (http://xlinkdb.gs.washington.edu/xlinkdb/Interactome_of_aged_muscle_mitochondria.php).

Code availability

R markdown used for statistical analysis and figure generation is available at https://github.com/brucelab/aging_mito_interactome_data_analysis.

References

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

    Article  PubMed  PubMed Central  Google Scholar 

  2. Harman, D. Aging: a theory based on free radical and radiation chemistry. J. Gerontol. 11, 298–300 (1956).

    Article  CAS  PubMed  Google Scholar 

  3. Peterson, C. M., Johannsen, D. L. & Ravussin, E. Skeletal muscle mitochondria and aging: a review. J. Aging Res. 2012, 1–20 (2012).

    Article  Google Scholar 

  4. Sun, N., Youle, R. J. & Finkel, T. The mitochondrial basis of Aging. Mol. Cell 61, 654–666 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Short, K. R. et al. Decline in skeletal muscle mitochondrial function with aging in humans. Proc. Natl Acad. Sci. 102, 5618–5623 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hepple, R. T. Mitochondrial involvement and impact in aging skeletal muscle. Front. Aging Neurosci. 6, 211 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ubaida-Mohien, C. et al. Discovery proteomics in aging human skeletal muscle finds change in spliceosome, immunity, proteostasis and mitochondria. eLife 8, e49874 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Tumasian, R. A. et al. Skeletal muscle transcriptome in healthy aging. Nat. Commun. 12, 2014 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hwang, C. Y. et al. Quantitative proteome analysis of age-related changes in mouse gastrocnemius muscle using mTRAQ. Proteomics 14, 121–132 (2014).

    Article  CAS  PubMed  Google Scholar 

  10. Hunt, L. C. et al. Integrated genomic and proteomic analyses identify stimulus-dependent molecular changes associated with distinct modes of skeletal muscle atrophy. Cell Rep. 37, 109971 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Yu, Q. et al. Sample multiplexing for targeted pathway proteomics in aging mice. Proc. Natl Acad. Sci.117, 9723–9732 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Yeo, D., Kang, C. & Ji, L. L. Aging alters acetylation status in skeletal and cardiac muscles. GeroScience 42, 963–976 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wagner, G. R. & Payne, R. M. Mitochondrial acetylation and diseases of aging. J. Aging Res. 2011, 234875 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Hofer, A. & Wenz, T. Post-translational modification of mitochondria as a novel mode of regulation. Exp. Gerontol. 56, 202–220 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. Chavez, J. D., Keller, A., Mohr, J. P. & Bruce, J. E. Isobaric quantitative protein interaction reporter technology for comparative interactome studies. Anal. Chem. 92, 14094–14102 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Müller, F. & Rappsilber, J. A protocol for studying structural dynamics of proteins by quantitative crosslinking mass spectrometry and data-independent acquisition. J. Proteomics 218, 103721 (2020).

    Article  PubMed  Google Scholar 

  17. Chavez, J. D. et al. A general method for targeted quantitative cross-linking mass spectrometry. PLoS One 11, e0167547 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Rath, S. et al. MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations. Nucleic Acids Res. 49, D1541–D1547 (2021).

    Article  CAS  PubMed  Google Scholar 

  20. Figueiredo, P. A., Ferreira, R. M., Appell, H. J. & Duarte, J. A. Age-induced morphological, biochemical, and functional alterations in isolated mitochondria from murine skeletal muscle. J. Gerontol. Biol. Sci. Med. Sci. 63, 350–359 (2008).

    Article  Google Scholar 

  21. Han, Y. et al. Transcriptome features of striated muscle aging and predictability of protein level changes. Mol. Omics 17, 796–808 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wiley, C. D. & Campisi, J. The metabolic roots of senescence: mechanisms and opportunities for intervention. Nat. Metab. 3, 1290–1301 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Sánchez-Caballero, L., Guerrero-Castillo, S. & Nijtmans, L. Unraveling the complexity of mitochondrial complex I assembly: a dynamic process. Biochim. Biophys. Acta 1857, 980–990 (2016).

    Article  PubMed  Google Scholar 

  24. Kruse, S. E. et al. Age modifies respiratory complex I and protein homeostasis in a muscle type-specific manner. Aging Cell 15, 89–99 (2016).

    Article  CAS  PubMed  Google Scholar 

  25. Park, J. et al. SIRT5-mediated lysine desuccinylation impacts diverse metabolic pathways. Mol. Cell 50, 919–930 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Yang, W. et al. Mitochondrial sirtuin network reveals dynamic SIRT3-dependent deacetylation in response to membrane depolarization. Cell 167, 985–1000.e1021 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Guerrero-Castillo, S. et al. The assembly pathway of mitochondrial respiratory chain complex I. Cell Metab. 25, 128–139 (2017).

    Article  CAS  PubMed  Google Scholar 

  28. Hirst, J. & Roessler, M. M. Energy conversion, redox catalysis and generation of reactive oxygen species by respiratory complex I. Biochim. Biophys. Acta 1857, 872–883 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Szczepanowska, K. et al. A salvage pathway maintains highly functional respiratory complex I. Nat. Commun. 11, 1643 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Miwa, S. et al. Low abundance of the matrix arm of complex I in mitochondria predicts longevity in mice. Nat. Commun. 5, 3837 (2014).

    Article  CAS  PubMed  Google Scholar 

  31. Protasoni, M. et al. Respiratory supercomplexes act as a platform for complex III-mediated maturation of human mitochondrial complexes I and IV. EMBO J. 39, e102817 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Balsa, E. et al. NDUFA4 is a subunit of complex IV of the mammalian electron transport chain. Cell Metab. 16, 378–386 (2012).

    Article  CAS  PubMed  Google Scholar 

  33. Zong, S. et al. Structure of the intact 14-subunit human cytochrome c oxidase. Cell Res. 28, 1026–1034 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Caudal, A. et al. Mitochondrial interactome quantitation reveals structural changes in metabolic machinery in the failing murine heart. Nat. Cardiovasc. Res. 1, 855–866 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Clayton, S. A. et al. Inflammation causes remodeling of mitochondrial cytochrome. Sci. Adv. 7, eabl5182 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Pitceathly, R. D. S. et al. NDUFA4 mutations underlie dysfunction of a cytochrome c oxidase subunit linked to human neurological disease. Cell Rep. 3, 1795–1805 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Yuan, R. et al. Genetic coregulation of age of female sexual maturation and lifespan through circulating IGF1 among inbred mouse strains. Proc. Natl. Acad. Sci. USA 109, 8224–8229 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ferrucci, L. & Kuchel, G. A. Heterogeneity of aging: individual risk factors, mechanisms, patient priorities, and outcomes. J. Am. Geriatr. Soc. 69, 610–612 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Smith, H. Q., Li, C., Stanley, C. A. & Smith, T. J. Glutamate dehydrogenase, a complex enzyme at a crucial metabolic branch point. Neurochem. Res. 44, 117–132 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. Hamelin, M., Mary, J., Vostry, M., Friguet, B. & Bakala, H. Glycation damage targets glutamate dehydrogenase in the rat liver mitochondrial matrix during aging: glycation of glutamate dehydrogenase with aging. FEBS J. 274, 5949–5961 (2007).

    Article  CAS  PubMed  Google Scholar 

  41. Li, M., Li, C., Allen, A., Stanley, C. A. & Smith, T. J. The structure and allosteric regulation of mammalian glutamate dehydrogenase. Arch. Biochem. Biophys. 519, 69–80 (2012).

    Article  CAS  PubMed  Google Scholar 

  42. Banerjee, S., Schmidt, T., Fang, J., Stanley, C. A. & Smith, T. J. Structural studies on ADP activation of mammalian glutamate dehydrogenase and the evolution of regulation. Biochemistry 42, 3446–3456 (2003).

    Article  CAS  PubMed  Google Scholar 

  43. Hyyti, O. M., Ledee, D., Ning, X.-H., Ge, M. & Portman, M. A. Aging impairs myocardial fatty acid and ketone oxidation and modifies cardiac functional and metabolic responses to insulin in mice. Am. J. Physiol. Heart Circ. Physiol. 299, H868–H875 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Houtkooper, R. H. et al. The metabolic footprint of aging in mice. Sci. Rep. 1, 134 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Zhang, X. et al. Impaired mitochondrial energetics characterize poor early recovery of muscle mass following hind limb unloading in old mice. J. Gerontol. Ser. A 73, 1313–1322 (2018).

    Article  CAS  Google Scholar 

  46. Fan, J. et al. Tetrameric acetyl-CoA acetyltransferase 1 is important for tumor growth. Mol. Cell 64, 859–874 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wu, B. et al. Succinyl-CoA ligase deficiency in pro-inflammatory and tissue-invasive T cells. Cell Metab. 32, 967–980.e965 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Hayflick, L. The greatest risk factor for the leading cause of death is ignored. Biogerontology 22, 133–141 (2021).

    Article  PubMed  Google Scholar 

  49. Asadi Shahmirzadi, A. et al. Alpha-ketoglutarate, an endogenous metabolite, extends lifespan and compresses morbidity in aging mice. Cell Metab. 32, 447–456.e446 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Su, Y. et al. Alpha-ketoglutarate extends Drosophila lifespan by inhibiting mTOR and activating AMPK. Aging 11, 4183–4197 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kaeberlein, M., Burtner, C. R. & Kennedy, B. K. Recent developments in yeast aging. PLoS Genet. 3, e84 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Chin, R. M. et al. The metabolite α-ketoglutarate extends lifespan by inhibiting ATP synthase and TOR. Nature 510, 397–401 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hagopian, K. Caloric restriction increases gluconeogenic and transaminase enzyme activities in mouse liver. Exp. Gerontol. 38, 267–278 (2003).

    Article  CAS  PubMed  Google Scholar 

  54. Lyu, Y. et al. Drosophila serotonin 2A receptor signaling coordinates central metabolic processes to modulate aging in response to nutrient choice. eLife 10, e59399 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Lombardi, A. A. et al. Mitochondrial calcium exchange links metabolism with the epigenome to control cellular differentiation. Nat. Commun. 10, 4509 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Rardin, M. J. et al. Label-free quantitative proteomics of the lysine acetylome in mitochondria identifies substrates of SIRT3 in metabolic pathways. Proc. Natl Acad. Sci. USA 110, 6601–6606 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Mimaki, M., Wang, X., McKenzie, M., Thorburn, D. R. & Ryan, M. T. Understanding mitochondrial complex I assembly in health and disease. Biochim. Biophys. Acta 1817, 851–862 (2012).

    Article  CAS  PubMed  Google Scholar 

  58. Martínez-Reyes, I. & Chandel, N. S. Mitochondrial TCA cycle metabolites control physiology and disease. Nat. Commun. 11, 102 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Lee, S. H., Lee, S. K., Paik, D. & Min, K. J. Overexpression of fatty-acid-β-oxidation-related genes extends the lifespan of Drosophila melanogaster. Oxid. Med. Cell Longev. 2012, 854502 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Nguyen, D., Samson, S. L., Reddy, V. T., Gonzalez, E. V. & Sekhar, R. V. Impaired mitochondrial fatty acid oxidation and insulin resistance in aging: novel protective role of glutathione. Aging Cell 12, 415–425 (2013).

    Article  CAS  PubMed  Google Scholar 

  61. Keller, A., Chavez, J. D., Eng, J. K., Thornton, Z. & Bruce, J. E. Tools for 3D interactome visualization. J. Proteome Res. 18, 753–758 (2019).

    Article  CAS  PubMed  Google Scholar 

  62. Mänttäri, S. & Järvilehto, M. Comparative analysis of mouse skeletal muscle fibre type composition and contractile responses to calcium channel blocker. BMC Physiol. 5, 4 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Wallace, M. A. et al. The ketogenic diet preserves skeletal muscle with aging in mice. Aging Cell 20, e13322 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Hämäläinen, N. & Pette, D. The histochemical profiles of fast fiber types IIB, IID, and IIA in skeletal muscles of mouse, rat, and rabbit. J. Histochem. Cytochem. 41, 733–743 (1993).

    Article  PubMed  Google Scholar 

  65. Lark, D. S. et al. Direct real-time quantification of mitochondrial oxidative phosphorylation efficiency in permeabilized skeletal muscle myofibers. Am. J. Physiol. Cell Physiol. 311, C239–C245 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Figueiredo, P. A., Ferreira, R. M., Appell, H. J. & Duarte, J. A. Age-induced morphological, biochemical, and functional alterations in isolated mitochondria from murine skeletal muscle. J. Gerontol. Ser. Biol. Sci. Med. Sci. 63, 350–359 (2008).

    Article  Google Scholar 

  67. Crouch, M.-L. et al. Cyclophosphamide leads to persistent deficits in physical performance and in vivo mitochondria function in a mouse model of chemotherapy late effects. PLoS One 12, e0181086 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Wittig, I., Karas, M. & Schägger, H. High resolution clear native electrophoresis for in-gel functional assays and fluorescence studies of membrane protein complexes. Mol. Cell Proteomics 6, 1215–1225 (2007).

    Article  CAS  PubMed  Google Scholar 

  69. Mohr, J. P., Perumalla, P., Chavez, J. D., Eng, J. K. & Bruce, J. E. Mango: a general tool for collision induced dissociation-cleavable cross-linked peptide identification. Anal. Chem. 90, 6028–6034 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Eng, J. K., Jahan, T. A. & Hoopmann, M. R. Comet: an open-source MS/MS sequence database search tool. Proteomics 13, 22–24 (2013).

    Article  CAS  PubMed  Google Scholar 

  71. Keller, A., Chavez, J. D. & Bruce, J. E. Increased sensitivity with automated validation of XL-MS cleavable peptide crosslinks. Bioinformatics 35, 895–897 (2019).

    Article  CAS  PubMed  Google Scholar 

  72. Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Software 4, 1686 (2019).

    Article  Google Scholar 

  73. Szklarczyk, D. et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 49, D605–D612 (2021).

    Article  CAS  PubMed  Google Scholar 

  74. Bullock, J. M. A., Thalassinos, K. & Topf, M. Jwalk and MNXL web server: model validation using restraints from crosslinking mass spectrometry. Bioinformatics 34, 3584–3585 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Figures were created with BioRender.com. We thank members of the Bruce laboratory for helpful discussion. This work was funded by National Institutes of Health grants P01-AG001751 (D.J.M. and M.D.C.), R56-AG070096 (D.J.M. and J.E.B.), T32 AG066574 (G.A.P.), R35GM136255 (J.E.B.) and R01HL144778 (J.E.B.).

Author information

Author notes

  1. These authors contributed equally: A. A. Bakhtina, G. A. Pharaoh.

Authors and Affiliations

  1. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Anna A. Bakhtina, Andrew Keller & James E. Bruce

  2. Department of Radiology, University of Washington, Seattle, WA, USA

    Gavin A. Pharaoh, Matthew D. Campbell, Rudolph S. Stuppard & David J. Marcinek

Authors
  1. Anna A. Bakhtina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gavin A. Pharaoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthew D. Campbell
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrew Keller
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rudolph S. Stuppard
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David J. Marcinek
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. James E. Bruce
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.A.B., G.A.P., D.J.M. and J.E.B. designed the experiments. A.A.B., G.A.P., D.J.M. and J.E.B. wrote and edited the manuscript. G.A.P., M.D.C. and R.S.S. performed animal experiments. A.A.B. performed cross-linking experiments, mass spectrometry raw data acquisition and processing. A.K. developed computational tools to support structural protein analysis and cross-linking quantitation. D.J.M. and J.E.B. supervised the project.

Corresponding authors

Correspondence to David J. Marcinek or James E. Bruce.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Aging thanks Luca Scorrano, Nicolas Demaurex and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Global quantitative cross-linking measurements.

a. Histogram of calculated Euclidean distances for all intraprotein cross-links mapped to AlphaFold predicted structures. b. Boxplots of number of ions used for each log2 ratio in P1, P2, P3, and P4. X-axis is capped at 40 for readability but there are ratios in each sample with more than 40 ions. c. Histogram of differences between a mean log2 ratios for cross-linked residue pairs based on multiple cross-linked peptides (cross-links that connect the same lysines, but can be identified in differently cleaved or modified peptides) and each cross-linked peptide pair d. Submitochondrial localization of significantly changed interlinks (left) and intralinks (right). e. STRING network of proteins with significantly changed cross-links. f. Isolated gastrocnemius mitochondria oxphos capacity normalized by amount of protein from the 4 young (6 months) and 4 old (30 months) female mice. Mean ± SD.

Source data

Extended Data Fig. 2 Complex I and Complex IV cross-linking analysis.

a. Heatmap of log2 ratios of all NDUS1 and NDUV1 cross-links and interprotein cross-links to other CI subunits. b. Boxplots of all intralinks in CI subunits by a biological replicate. c. Boxplots of crosslinks downregulated in aging and non-changing intralinks based on all 4 biological replicates for CI (NDUV1XLs n = 34; other links n = 112; visualized as median and 25th and 75th percentiles, with whiskers indicating minima and maxima) and CIV (f) Interlinks n = 30; other links n = 14; visualized as median and 25th and 75th percentiles, with whiskers indicating minima and maxima). P values are from Welch two-sided t-test. d. Structure of supercomplex (PDB 5GUP) with cross-linked CI and CIII subunits highlighted (top) and specific CI–CIII cross-links mapped to the subunits (bottom); decreased cross-linked are in green. e. CIII cross-links mapped to a bovine structure. Subunits with decreased intralinks highlighted and zoomed in (right). g. Correlation plots of CI and CIV cross-links changing in aging mitochondria and CII OXPHOS with multiple R-squared displayed.

Source data

Extended Data Fig. 3 Complex I and Complex IV in gel activity measurements show decrease in aged muscle mitochondria.

a. In-gel CI activity assay blot with identified bands. b. Quantification of total CI activity (left; statistical significance determined by unpaired two-tailed student’s t-test; p = 0.1726) and CI activity for each identified band (right; statistical significance determined by Ordinary Two-Way ANOVA with Sidak’s post hoc test; p = 0.0011 age main effect). c. In-gel CIV activity assay blot with identified bands. d. Quantification of total CIV activity (left; statistical significance determined by unpaired two-tailed student’s t-test; p = 0.1219) and CIV activity for each identified band (right; statistical significance determined by Ordinary Two-Way ANOVA with Sidak’s post hoc test; p = 0.0045 age main effect; p = 0.0059 for Band 1 Sidak’s post hoc test). Both activity assays were performed using isolated gastrocnemius mitochondria from young (4–6 month, n = 4) and old (27–29 month, n = 5) NIA C57BL/6 J female mice. Mean ± SD. ns, not significant, **p < 0.01 by Sidak’s post hoc test.

Source data

Extended Data Fig. 4 Antenna specific DHE3 cross-links decreased with aging.

a. Decreased and non-changing cross-link levels in glutamate dehydrogenase highlighted on the volcano plot with Bonferroni corrected p value=0.05. b. Heatmap of all DHE3 cross-linked peptide pairs with each individual peptide sequence shown. Cross-linked lysine residues are in red. c. Correlation plots of DHE3 antenna cross-links changing in aged mitochondria and CII OXPHOS. d. Normalized glutamate stimulated respiration across a range of glutamate concentrations (left; statistical significance determined by Two-Way RM ANOVA with Sidak’s post hoc test; p = 0.0058 age main effect) and maximum respiration capacity with glutamate stimulation (right; statistical significance determined by unpaired two-tailed student’s t-test; p = 0.0009) in young (4–6 mo, n = 4) and old (27–29 mo, n = 4) female NIA C57BL/6 J isolated gastrocnemius mitochondria. e. The kinetics of glutamate stimulated respiration are altered with age (left; nonlinear regression determined by [Agonist] vs. normalized response–Variable slope) in young (4–6 mo, n = 4) and old (27–29 mo, n = 4) female NIA C57BL/6 J isolated gastrocnemius mitochondria. The amount of glutamate required to stimulate 50% respiration calculated from the nonlinear regression is increased in old (right; statistical significance determined by unpaired two-tailed student’s t-test; p = 0.0253). Mean ± SD. *p < 0.05, **p < 0.01.

Source data

Extended Data Fig. 5 TCA cycle and FAO enzymes show decrease in cross-links proximal to CoA binding sites.

a. Fumarate hydratase cross-links mapped to a E. Coli structure (PDB 4HGV). Decreased cross-link levels are shown in the zoomed in square. b. Heatmap of log2 ratios of fumarate hydratase cross-links. c. Boxplots for Acadvl cross-links based on all 4 biological replicates. CoA proximal XLs n = 21; other links n = 46; visualized as median and 25th and 75th percentiles, with whiskers indicating minima and maxima; P-values are from Welch’s two-sided t test.

Source data

Extended Data Table 1 Pairwise combinations of old and young mitochondria to create 4 biological replicates for qXL-MS.
Full size table

Supplementary information

Supplementary Information

Supplementary results with Fig. 1 and discussion on limitations.

Reporting Summary

Supplementary Data 1

Calculations of log_2 changes for each young/old pair in mito yield, respiration and CS activity.

Source data

Source Data Fig. 2

Quantitative cross-linking data and functional measurements.

Source Data Fig. 3

Quantitative cross-linking data and functional measurements of complex I and complex IV.

Source Data Fig. 4

Quantitative cross-linking data and functional measurements of glutamate dehydrogenase.

Source Data Fig. 5

Quantitative cross-linking data for ACADVL, THIL and SUCA/SUCB.

Source Data Fig. 6

Correlation between quantitative cross-linking and functional measurements; XL clustering.

Source Data Extended Data Fig. 1

Global analysis of quantitative cross-linking measurements.

Source Data Extended Data Fig. 2

Quantitative cross-linking data and functional measurements of complex I and complex IV.

Source Data Extended Data Fig. 4a

Uncropped complex I activity gel.

Source Data Extended Data Fig. 4c

Uncropped complex IV activity gel.

Source Data Extended Data Fig. 3

Quantitation and statistical analysis of in gel CI and CIV activity for young and old mice.

Source Data Extended Data Fig. 4

Quantitative cross-linking data and functional measurements of DHE3.

Source Data Extended Data Fig. 5

Quantitative cross-linking data and functional measurements of ACADVL, THIL and SUCA/SUCB.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bakhtina, A.A., Pharaoh, G.A., Campbell, M.D. et al. Skeletal muscle mitochondrial interactome remodeling is linked to functional decline in aged female mice. Nat Aging 3, 313–326 (2023). https://doi.org/10.1038/s43587-023-00366-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43587-023-00366-5

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