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Regulation of aged skeletal muscle regeneration by circulating extracellular vesicles

Nature Aging volume 1, pages 1148–1161 (2021)Cite this article

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Abstract

Heterochronic blood exchange (HBE) has demonstrated that circulating factors restore youthful features to aged tissues. However, the systemic mediators of those rejuvenating effects remain poorly defined. We show here that the beneficial effect of young blood on aged muscle regeneration was diminished when serum was depleted of extracellular vesicles (EVs). Whereas EVs from young animals rejuvenate aged cell bioenergetics and skeletal muscle regeneration, aging shifts EV subpopulation heterogeneity and compromises downstream benefits on recipient cells. Machine learning classifiers revealed that aging shifts the nucleic acid, but not protein, fingerprint of circulating EVs. Alterations in subpopulation heterogeneity were accompanied by declines in transcript levels of the prolongevity protein α-Klotho (Klotho), and injection of EVs improved muscle regeneration in a Klotho mRNA-dependent manner. These studies demonstrate that EVs play a key role in the rejuvenating effects of HBE and that Klotho transcripts within EVs phenocopy the effects of young serum on aged skeletal muscle.

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Fig. 1: The beneficial effect of young serum on aged muscle progenitors is dependent on circulating EVs.
Fig. 2: The beneficial effect of young serum on aged muscle regeneration and mitochondrial function is dependent, at least in part, on circulating EVs.
Fig. 3: Aging shifts EV subpopulation heterogeneity and disrupts the biochemical fingerprint of EVs.
Fig. 4: Transcriptomic alterations in skeletal muscle with young serum treatment are dominated by EVs.
Fig. 5: Klotho transcripts in EVs decline over time and are preferentially contained within EVs with high expression of the CD81 surface marker.
Fig. 6: EV age impacts skeletal muscle regeneration and function.
Fig. 7: Klotho mRNA within EVs contributes to functional skeletal muscle regeneration.

Data availability

The raw data that support the experimental findings are included as Supplementary Information. The image files used for computational analysis are available at https://github.com/ankitbhatia/bioimage_aging. RNA-sequencing data have been deposited to the NCBI Gene Expression Omnibus database with the accession number GSE176478.

Code availability

The code used to perform machine learning-based analyses of EVs is available at https://github.com/ankitbhatia/bioimage_aging. For PC analyses, the OriginLab plugin ‘Principal Component Analysis for Spectroscopy’ was used. The code for RNA-sequencing analysis is available at https://github.com/sruthi-hub/Aging_EV/tree/main.

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Acknowledgements

These studies were supported by NIA grants R01AG052978 (F.A.), R01AG061005 (F.A.), R01AG066198-01 (F.A. and R.K.) and R33 ES025606-05 (B.V.H.) and UPMC Enterprises (F.A.). We thank the flow cytometry core at the Department of Immunology, University of Pittsburgh for providing resources and expertise to perform ImageStream analysis (National Institutes of Health grant 1S10OD019942-01), as well as the Center of Biologic Imaging, University of Pittsburgh for providing resources to perform confocal imaging (National Institutes of Health grant 1S10OD019973). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Physical Medicine & Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA

    Amrita Sahu, Sunita N. Shinde, Sruthi Sivakumar, Abish Pius & Fabrisia Ambrosio

  2. Department of Environmental and Occupational Health, University of Pittsburgh, Pittsburgh, PA, USA

    Amrita Sahu, Zachary J. Clemens, Nicholas F. Fitz, Iliya Lefterov, Aaron Barchowsky, Radosveta Koldamova & Fabrisia Ambrosio

  3. Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA

    Sruthi Sivakumar & Fabrisia Ambrosio

  4. Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Abish Pius

  5. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Ankit Bhatia

  6. Laboratory of Nanomedicine and Clinical Biophotonics (LABION), IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy

    Silvia Picciolini, Cristiano Carlomagno, Alice Gualerzi & Marzia Bedoni

  7. Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, USA

    Bennett Van Houten & Aaron Barchowsky

  8. Department of Sports Medicine and Nutrition, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA

    Mita Lovalekar

  9. McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Fabrisia Ambrosio

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  1. Amrita Sahu
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Contributions

F.A. and A.S. provided the concept, idea and experimental design for the studies. F.A. and A.S. wrote the manuscript. A.S., Z.J.C., S.S., S.N.S. and A.P. collected, analyzed and interpreted data and reviewed the manuscript. A. Bhatia provided computer vision- and machine learning-based analyses. S.P., C.C., and A.G. collected and analyzed data. M.B. interpreted data and reviewed the manuscript. B.V.H. analyzed and interpreted data and reviewed that manuscript. A. Barchowsky provided consultation for data interpretation and review of the manuscript. M.L. provided support for statistical analyses. N.F., I.L., and R.K. provided support for data interpretation. F.A. provided funding for the studies.

Corresponding author

Correspondence to Fabrisia Ambrosio.

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The authors declare no competing interests.

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Peer review information Nature Aging thanks Dan Lark, Tim Gavin, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Depletion of EVs eliminates the effect of young serum on Pax7 expression of muscle progenitors.

Quantification of Pax7 in aged MPCs treated with aged serum, young serum or EV-depleted aged or young serum. Scale bars, 50 µm (****P < 0.0001, two-tailed Mann–Whitney test comparing depleted young serum and young serum treatments). Data are presented as mean + s.e.m. Data from different cohorts or experimental groups performed on different days are presented within the same graph as black or red circles.

Source data

Extended Data Fig. 2 The ability of EVs to modulate target cell Klotho and MyoD protein levels is dependent on Klotho mRNAs.

a, Imaging and quantification of Klotho protein in aged MPCs following culture in the presence of young or aged EVs for 24 h. Scale bars, 50 µm (n = 6 wells per group performed over two independent experiments, **P < 0.01, two-tailed Welch’s t test). b, Representative violin plot of Klotho protein intensity per EV from young and aged serum, using imaging flow cytometry (n = 11,229–11,685 EVs per group for this experimental run. EVs pooled from four young and four aged serum samples, P > 0.05, two-tailed Mann–Whitney test, experiment repeated in triplicate). Violin plot minima, maxima, median, 25th percentile and 75th percentile are 0, 272915.9, 0, 0 and 26.36 (young serum) and 0, 272241.3, 0, 0, and 23.2 (aged serum). c,d, Quantification of MyoD-positive (%), desmin-positive (%) and ki67-positive (%) aged MPCs receiving young serum EVs treated with scramble or siRNA to Klotho (c) or aged serum EVs or aged serum EVs loaded with synthetic Klotho mRNA (d) (MyoD and desmin (scramble, siRNA) or desmin (aged EVs, synthetic Klotho): **P < 0.01, ***P < 0.001 and ****P < 0.0001, two-tailed t test with Welch’s correction, n = 5–6 wells per group; MyoD (aged EVs and synthetic Klotho): **P < 0.01, two-tailed Mann–Whitney test, n = 5 wells and group; ki67 (scramble, siRNA and aged EVs and synthetic Klotho): P > 0.05 (P = 0.1), two-tailed Mann–Whitney test).

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Sahu, A., Clemens, Z.J., Shinde, S.N. et al. Regulation of aged skeletal muscle regeneration by circulating extracellular vesicles. Nat Aging 1, 1148–1161 (2021). https://doi.org/10.1038/s43587-021-00143-2

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