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.

Exercise plasma boosts memory and dampens brain inflammation via clusterin

Abstract

Physical exercise is generally beneficial to all aspects of human and animal health, slowing cognitive ageing and neurodegeneration1. The cognitive benefits of physical exercise are tied to an increased plasticity and reduced inflammation within the hippocampus2,3,4, yet little is known about the factors and mechanisms that mediate these effects. Here we show that ‘runner plasma’, collected from voluntarily running mice and infused into sedentary mice, reduces baseline neuroinflammatory gene expression and experimentally induced brain inflammation. Plasma proteomic analysis revealed a concerted increase in complement cascade inhibitors including clusterin (CLU). Intravenously injected CLU binds to brain endothelial cells and reduces neuroinflammatory gene expression in a mouse model of acute brain inflammation and a mouse model of Alzheimer’s disease. Patients with cognitive impairment who participated in structured exercise for 6 months had higher plasma levels of CLU. These findings demonstrate the existence of anti-inflammatory exercise factors that are transferrable, target the cerebrovasculature and benefit the brain, and are present in humans who engage in exercise.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: RP induces neuroplasticity, improves cognition and reduces inflammation.
Fig. 2: RP counteracts LPS-induced neuroinflammation.
Fig. 3: Running alters complement and coagulation proteins.
Fig. 4: Clusterin reduces hippocampal inflammation.

Data availability

The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE55 partner repository under dataset identifier PXD022262 and PXD027406 for male and female mouse plasma proteins, respectively. Single-cell data and bulk RNA-seq datasets have been deposited at the Gene Expression Omnibus under accession number GSE164401Source data are provided with this paper.

Code availability

Data analyses and graphing have been carried out using free available software packages. When appropriate, custom code from previous literature was cited in the text and is available from the corresponding authors on request.

References

  1. Prakash, R. S., Voss, M. W., Erickson, K. I. & Kramer, A. F. Physical activity and cognitive vitality. Annu. Rev. Psychol. 66, 769–797 (2015).

    PubMed  Google Scholar 

  2. Gleeson, M. et al. The anti-inflammatory effects of exercise: mechanisms and implications for the prevention and treatment of disease. Nat. Rev. Immunol. 11, 607–615 (2011).

    CAS  PubMed  Google Scholar 

  3. He, X. F. et al. Voluntary exercise promotes glymphatic clearance of amyloid beta and reduces the activation of astrocytes and microglia in aged mice. Front. Mol. Neurosci. 10, 144 (2017).

    PubMed  PubMed Central  Google Scholar 

  4. van Praag, H., Christie, B. R., Sejnowski, T. J. & Gage, F. H. Running enhances neurogenesis, learning, and long-term potentiation in mice. Proc. Natl Acad. Sci. USA 96, 13427–13431 (1999).

    ADS  PubMed  PubMed Central  Google Scholar 

  5. Hawley, J. A., Hargreaves, M., Joyner, M. J. & Zierath, J. R. Integrative biology of exercise. Cell 159, 738–749 (2014).

    CAS  PubMed  Google Scholar 

  6. Chin, L. M., Keyser, R. E., Dsurney, J. & Chan, L. Improved cognitive performance following aerobic exercise training in people with traumatic brain injury. Arch. Phys. Med. Rehabil. 96, 754–759 (2015).

    PubMed  Google Scholar 

  7. Horder, H. et al. Midlife cardiovascular fitness and dementia: a 44-year longitudinal population study in women. Neurology 90, e1298–e1305 (2018).

    PubMed  PubMed Central  Google Scholar 

  8. da Costa Daniele, T. M. et al. Exercise effects on brain and behavior in healthy mice, Alzheimer’s disease and Parkinson’s disease model—a systematic review and meta-analysis. Behav. Brain Res. 383, 112488 (2020).

    PubMed  Google Scholar 

  9. Trejo, J. L., Carro, E. & Torres-Aleman, I. Circulating insulin-like growth factor I mediates exercise-induced increases in the number of new neurons in the adult hippocampus. J. Neurosci. 21, 1628–1634 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Fabel, K. et al. VEGF is necessary for exercise-induced adult hippocampal neurogenesis. Eur. J. Neurosci. 18, 2803–2812 (2003).

    PubMed  Google Scholar 

  11. Leiter, O. et al. Exercise-induced activated platelets increase adult hippocampal precursor proliferation and promote neuronal differentiation. Stem Cell Rep. 12, 667–679 (2019).

    CAS  Google Scholar 

  12. Horowitz, A. M. et al. Blood factors transfer beneficial effects of exercise on neurogenesis and cognition to the aged brain. Science 369, 167–173 (2020).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  13. Chen, M. B. et al. Brain endothelial cells are exquisite sensors of age-related circulatory cues. Cell Rep. 30, 4418–4432 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Batista, C. R. A., Gomes, G. F., Candelario-Jalil, E., Fiebich, B. L. & de Oliveira, A. C. P. Lipopolysaccharide-induced neuroinflammation as a bridge to understand neurodegeneration. Int. J. Mol. Sci. 20, 2293 (2019).

    CAS  PubMed Central  Google Scholar 

  15. Conway, E. M. Complement-coagulation connections. Blood Coagul. Fibrinolysis 29, 243–251 (2018).

    CAS  PubMed  Google Scholar 

  16. Markiewski, M. M., Nilsson, B., Ekdahl, K. N., Mollnes, T. E. & Lambris, J. D. Complement and coagulation: strangers or partners in crime? Trends Immunol. 28, 184–192 (2007).

    CAS  PubMed  Google Scholar 

  17. Hicks, A. L., Kent-Braun, J. & Ditor, D. S. Sex differences in human skeletal muscle fatigue. Exerc. Sport Sci. Rev. 29, 109–112 (2001).

    CAS  PubMed  Google Scholar 

  18. Yip, K. S., Suvorov, A., Connerney, J., Lodato, N. J. & Waxman, D. J. Changes in mouse uterine transcriptome in estrus and proestrus. Biol. Reprod. 89, 13 (2013).

    PubMed  PubMed Central  Google Scholar 

  19. Harold, D. et al. Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat. Genet. 41, 1088–1093 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Lambert, J. C. et al. Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat. Genet. 41, 1094–1099 (2009).

    CAS  PubMed  Google Scholar 

  21. Pereira, R. M. et al. Protective molecular mechanisms of clusterin against apoptosis in cardiomyocytes. Heart Fail. Rev. 23, 123–129 (2018).

    CAS  PubMed  Google Scholar 

  22. Thangaraj, S. S. et al. Contact activation-induced complex formation between complement factor H and coagulation factor XIIa. J. Thromb. Haemost. 18, 876–884 (2020).

    CAS  PubMed  Google Scholar 

  23. Hunt, L. C., Upadhyay, A., Jazayeri, J. A., Tudor, E. M. & White, J. D. An anti-inflammatory role for leukemia inhibitory factor receptor signaling in regenerating skeletal muscle. Histochem. Cell Biol. 139, 13–34 (2013).

    CAS  PubMed  Google Scholar 

  24. Yamagishi, S. et al. Pigment epithelium-derived factor inhibits TNF-alpha-induced interleukin-6 expression in endothelial cells by suppressing NADPH oxidase-mediated reactive oxygen species generation. J. Mol. Cell. Cardiol. 37, 497–506 (2004).

    CAS  PubMed  Google Scholar 

  25. Pohlkamp, T., Wasser, C. R. & Herz, J. Functional roles of the interaction of APP and lipoprotein receptors. Front. Mol. Neurosci. 10, 54 (2017).

    PubMed  PubMed Central  Google Scholar 

  26. Yang, A. C. et al. Physiological blood-brain transport is impaired with age by a shift in transcytosis. Nature 583, 425–430 (2020).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  27. Middeldorp, J. et al. Preclinical assessment of young blood plasma for Alzheimer disease. JAMA Neurol. 73, 1325–1333 (2016).

    PubMed  PubMed Central  Google Scholar 

  28. Zhou, Z., Xu, M. J. & Gao, B. Hepatocytes: a key cell type for innate immunity. Cell. Mol. Immunol. 13, 301–315 (2016).

    CAS  PubMed  Google Scholar 

  29. Yin, C. et al. ApoE attenuates unresolvable inflammation by complex formation with activated C1q. Nat. Med. 25, 496–506 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Hong, S. et al. Complement and microglia mediate early synapse loss in Alzheimer mouse models. Science 352, 712–716 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kirszbaum, L., Bozas, S. E. & Walker, I. D. SP-40,40, a protein involved in the control of the complement pathway, possesses a unique array of disulphide bridges. FEBS Lett. 297, 70–76 (1992).

    CAS  PubMed  Google Scholar 

  32. Hsu, J. L. et al. Plasma biomarkers are associated with agitation and regional brain atrophy in Alzheimer’s disease. Sci. Rep. 7, 5035 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  33. Yousef, H. et al. Aged blood impairs hippocampal neural precursor activity and activates microglia via brain endothelial cell VCAM1. Nat. Med. 25, 988–1000 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Lee, J. W. et al. Neuro-inflammation induced by lipopolysaccharide causes cognitive impairment through enhancement of beta-amyloid generation. J. Neuroinflammation 5, 37 (2008).

    PubMed  PubMed Central  Google Scholar 

  35. Monje, M. L., Toda, H. & Palmer, T. D. Inflammatory blockade restores adult hippocampal neurogenesis. Science 302, 1760–1765 (2003).

    ADS  CAS  PubMed  Google Scholar 

  36. Rockenstein, E., Mallory, M., Mante, M., Sisk, A. & Masliaha, E. Early formation of mature amyloid-β protein deposits in a mutant APP transgenic model depends on levels of Aβ1–42. J. Neurosci. Res. 66, 573–582 (2001).

    CAS  PubMed  Google Scholar 

  37. Villeda, S. A. et al. The ageing systemic milieu negatively regulates neurogenesis and cognitive function. Nature 477, 90–94 (2011).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  38. Liddelow, S. A. et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541, 481–487 (2017).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  39. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  40. Tabula Muris, C. et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    ADS  Google Scholar 

  41. Hahn, O. et al. A nutritional memory effect counteracts benefits of dietary restriction in old mice. Nat. Metab. 1, 1059–1073 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Brake, M. A. et al. Assessing blood clotting and coagulation factors in mice. Curr. Protoc. Mouse Biol. 9, e61 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  43. Zheng, Z. et al. An ATF6-tPA pathway in hepatocytes contributes to systemic fibrinolysis and is repressed by DACH1. Blood 133, 743–753 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Smith, A. A., Jacobson, L. J., Miller, B. I., Hathaway, W. E. & Manco-Johnson, M. J. A new euglobulin clot lysis assay for global fibrinolysis. Thromb. Res. 112, 329–337 (2003).

    CAS  PubMed  Google Scholar 

  45. Yousef, H., Czupalla, C. J., Lee, D., Butcher, E. C. & Wyss-Coray, T. Papain-based single cell isolation of primary murine brain endothelial cells using flow cytometry. Bio Protoc. 8, e3091 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Han, Y., Li, M., Qiu, F., Zhang, M. & Zhang, Y. H. Cell-permeable organic fluorescent probes for live-cell long-term super-resolution imaging reveal lysosome-mitochondrion interactions. Nat. Commun. 8, 1307 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  47. Dempsey, G. T., Vaughan, J. C., Chen, K. H., Bates, M. & Zhuang, X. Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging. Nat. Methods 8, 1027–1036 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Hughes, C. S. et al. Ultrasensitive proteome analysis using paramagnetic bead technology. Mol. Syst. Biol. 10, 757 (2014).

    PubMed  Google Scholar 

  49. Rappsilber, J., Ishihama, Y. & Mann, M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal. Chem. 75, 663–670 (2003).

    CAS  PubMed  Google Scholar 

  50. Rahnenfuhrer A, A. topGO: enrichment analysis for Gene Ontology. R package version 2.18.0 https://doi.org/10.18129/B9.bioc.topGO (2016).

  51. Supek, F., Bosnjak, M., Skunca, N. & Smuc, T. REVIGO summarizes and visualizes long lists of Gene Ontology terms. PLoS ONE 6, e21800 (2011).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  52. Albert, M. S. et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 270–279 (2011).

    PubMed  PubMed Central  Google Scholar 

  53. Gold, L. et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS ONE 5, e15004 (2010).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  54. SOMAscan Technical White Paper (Somalogic, 2015); http://www.somalogic.com/wp-content/uploads/2016/08/SSM-002-Rev-3-SOMAscan-Technical-White-Paper.pdf

  55. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    CAS  PubMed  Google Scholar 

  56. Schaum, N., et al. Ageing hallmarks exhibit organ-specific temporal signatures. Nature 583, 596–602 (2020).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  57. Bostrom, P. et al. A PGC1-α-dependent myokine that drives brown-fat-like development of white fat and thermogenesis. Nature 481, 463–468 (2012).

    ADS  PubMed  PubMed Central  Google Scholar 

  58. Cho, H. C. et al. The concentrations of serum, plasma and platelet BDNF are all increased by treadmill VO2max performance in healthy college men. Neurosci. Lett. 519, 78–83 (2012).

    CAS  PubMed  Google Scholar 

  59. Colt, E. W., Wardlaw, S. L. & Frantz, A. G. The effect of running on plasma β-endorphin. Life Sci. 28, 1637–1640 (1981).

    CAS  PubMed  Google Scholar 

  60. El Hayek, L. et al. Lactate mediates the effects of exercise on learning and memory through sirt1-dependent activation of hippocampal brain-derived neurotrophic factor (BDNF). J. Neurosci. 39, 2369–2382 (2019).

    PubMed  PubMed Central  Google Scholar 

  61. Eliakim, A. et al. Physical fitness, endurance training, and the growth hormone-insulin-like growth factor I system in adolescent females. J. Clin. Endocrinol. Metab. 81, 3986–3992 (1996).

    CAS  PubMed  Google Scholar 

  62. Eliakim, A., Brasel, J. A., Mohan, S., Wong, W. L. & Cooper, D. M. Increased physical activity and the growth hormone-IGF-I axis in adolescent males. Am. J. Physiol. 275, R308–R314 (1998).

    CAS  PubMed  Google Scholar 

  63. Ferreira, J. C. et al. Maximal lactate steady state in running mice: effect of exercise training. Clin. Exp. Pharmacol. Physiol. 34, 760–765 (2007).

    CAS  PubMed  Google Scholar 

  64. Koehl, M. et al. Exercise-induced promotion of hippocampal cell proliferation requires β-endorphin. FASEB J. 22, 2253–2262 (2008).

    CAS  PubMed  Google Scholar 

  65. Koziris, L. P. et al. Serum levels of total and free IGF-I and IGFBP-3 are increased and maintained in long-term training. J. Appl. Physiol. 86, 1436–1442 (1999).

    CAS  PubMed  Google Scholar 

  66. Moon, H. Y. et al. Running-induced systemic cathepsin B secretion is associated with memory function. Cell Metab. 24, 332–340 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Schabitz, W. R. et al. Intravenous brain-derived neurotrophic factor reduces infarct size and counterregulates Bax and Bcl-2 expression after temporary focal cerebral ischemia. Stroke 31, 2212–2217 (2000).

    ADS  CAS  PubMed  Google Scholar 

  68. Wrann, C. D. et al. Exercise induces hippocampal BDNF through a PGC-1α/FNDC5 pathway. Cell Metab. 18, 649–659 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank members of the Wyss-Coray laboratory for their support; D. Berdnik for technical assistance; H. du Bois and S. Shuken for sharing their protocols; H. Zhang and K. Dickey for laboratory management; and members of the Stanford Behavioral and Functional Neuroscience Laboratory for their work on the behavioural assays. This work was funded by the US National Institute on Aging (AG047820 to T.W.-C. and T.A.R. and 1F32AG067652 to N.K.) and the Stanford Alzheimer’s Disease Research Center (P30AG066515), the US Department of Veterans Affairs (Research Career Scientist Award IO1 BX001319 to T.W.-C.), a NOMIS Foundation award to T.W.-C., the Simons Foundation (award to T.W.-C.), the Wu Tsai Neurosciences Institutes’ Brain Rejuvenation Project with support from the Bertarelli Foundation (award to T.W.-C.), the Department of Defense (W81XWH-12-1-0584 to K.J.F.), the Alzheimer’s Association (NIRG-15-362171 to J.K.F.) and the Marie Curie Foundation n-273487 (GCs-CNS-IS) awarded to Z.D.M. The contents supported by this funding do not represent the views of the VA or the US Government. We acknowledge Servier Medical Art (https://smart.servier.com), MediaLab (https://medialab.biochem.wisc.edu/clip-art/) and Freepik (https://www.freepik.com) for providing images of mice and cartoon components.

Author information

Authors and Affiliations

Authors

Contributions

T.W.-C., T.A.R., Z.D.M. and M.B. designed and conceived the experiments. Z.D.M. and M.B. developed an initial paradigm of plasma transfer from runner to non-runner mice and studied the effect of plasma on neural stem cell activity. Z.D.M. and D.W. performed experiments to generate plasma pools, carried out animal treatments and processed brain tissue and plasma samples for molecular and protein analyses. D.W. performed and analysed behavioural experiments under the supervision of Z.D.M.; L.B., N.K., O.H. and Z.D.M. performed and analysed sequencing experiments. Z.D.M., N.L.S. and M.S. designed and performed behavioural experiments. N.K., A.Y., R.V., N.L. and Z.D.M. designed and performed experiments on BECs. N.K. performed statistical analysis and visualization of the single-cell datasets. H.Z. generated and provided the APP mice. D.L. performed retro-orbital injections of rCLU. L.Y., N.K. and Z.D.M. designed and performed experiments to assess the complement and coagulation cascades. B.L. performed statistical analyses and visualization of protein and gene datasets. J.K.F. performed experiments with humans and collected plasma samples. N.O., J.E.E., L.Z., P.L.M. and K.C. carried out MS analyses. Z.D.M., D.W. and T.W.-C. wrote the manuscript with input from T.A.R.; T.W.-C. and Z.D.M. supervised the study.

Corresponding author

Correspondence to Tony Wyss-Coray.

Ethics declarations

Competing interests

N.O. is affiliated with Calico Life Sciences and has no financial interests to declare. The other authors declare no competing interests.

Additional information

Peer review information Nature thanks the anonymous reviewers 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 figures and tables

Extended Data Fig. 1 Changes in cell proliferation and survival with running or runner plasma infusions.

a, Male mice at 3-4 months of age had access to a running wheel for 3, 7, 14 or 28 days while controls remain without access to running wheels. EdU was administered 24 h before sacrifice. Plots show total number of cells per dentate gyrus (DG) of fluorescent immunolabelled EdU+ cells (n = 6-8 per group) and DCX+ cells (n = 6-7 per group). b, Running distance per day by male mice (n = 5) at 3-4 months of age with free access to a running wheel. c, Male mice at 3, 6, 9, 12 and 15 months of age had access to a running wheel for 28 days. Graphs show total number of DCX+ cells per dentate gyrus (n=4-8 per group). Means ± s.e.m; unpaired Student’s two-tailed t test; * P < 0.05, ** P < 0.01, *** P < 0.01. d, Male mice at 3 months of age had access to a running wheel for 28 days. BrdU was administered 3 days before the exercise and EdU 24 h before sacrifice. Graphs show total number of cells per dentate gyrus (DG) of fluorescent immunolabelled cells (n=4-6 per group). Means ± s.e.m; unpaired Student’s two-tailed t test; * P < 0.05. e, Control injections of saline via retro-orbital vein in combination with Isoflurane do not impair neural stem activity. Mice at 3-4 months of age were injected with saline via the tail vein or the retro orbital vein with 200 µl of saline, every 3 days for 28 days. BrdU was administered 3 days before saline administration and EdU 24 h before sacrifice. The hippocampus was dissected and processed for immunohistochemistry. Graphs show total number of cells per dentate gyrus (DG) of fluorescent immunolabelled cells (n=7-8 per group). Means ± s.e.m; Unpaired Student’s two-tailed t test. f, Runner plasma infusions from 28 days runners upregulate proliferation and survival of hippocampal new born cells. Plasma from running mice (3-4 months of age) that run for 7, 14 or 28 days was collected and transferred to matched aged non-running mice, once every 3 days 28 days. BrdU was administered 3 days before plasma administration and EdU 24 h before sacrifice. Graphs show fold change of total number of cells per dentate gyrus (DG) of fluorescent immunolabelled EdU+ cells, BrdU+ cells and DCX+ cells (n = 6-9 per group). Means ± s.e.m; unpaired Student’s two-tailed t test; * P < 0.05, ** P < 0.01 and *** P < 0.001. The images in a, c, d and e were generated using Servier Medical Art (https://smart.servier.com) and MediaLab (https://medialab.biochem.wisc.edu/clip-art/).

Extended Data Fig. 2 Changes in behavioural and hippocampal biological processes in response to runner plasma infusions.

a, Plasma from running mice (3-4 months of age) was collected and transferred to matched aged non-running mice, once every 3 days for 28 days. Mice were then tested for cued memory on the fear conditioning test. Graphs show percentage of freezing behaviour in response to the light/tone cues associated with the fear stimulus in CP and RP recipient mice (n = 18-20 per group). Means ± s.e.m; Unpaired Student’s two-tailed t test, N.S. not significant. b, CP and RP infused mice show comparable activity and velocity in the activity chamber (n = 13-14 per group) and percentage of time spent in the light arena (n = 12 per group). Means ± s.e.m; unpaired Student’s two-tailed t test; N.S., not significant. c, Graphs show distance traveled, thigmotaxis and velocity displayed by mice infused with RP or CP in the water maze test (n = 12 per group). d, Hierarchical networks of the abundance of gene ontology (GO) terms (Fisher’s exact test, P < 0.05) related to biological processes using REVIGO (Resnik measurement, 0.7 distance). GO terms correspond to the DEGs with treatment of CP versus RP shown in Fig. 1h.

Extended Data Fig. 3 Validation of changes in gene expression in response to runner plasma infusions and validation of changes in proteins with running.

a, PCA analysis of common DEGs (Wald test, P < 0.05) induced by LPS (SAL-SAL (blue) vs. SAL-LPS (red) and by RP treatment (LPS-CP (grey) vs. LPS-RP (green)) (n = 7-8 per group). b, Graphs show fold changes of relative gene expression of indicated genes measured by qPCR (n = 7-8 per group). Means ± s.e.m; One-way ANOVA and Bonferroni post-hoc; * P < 0.05, ** P < 0.01, *** P < 0.001 and **** P < 0.0001. c, Validation of proteins captured with shotgun-LC MS1 using TMT-LC MS3 detection and analysis. Heat map depicting the relative levels of the top differentially expressed plasma proteins detected. Unpaired Student’s two-tailed t test; * P < 0.05, ** P < 0.01 and *** P < 0.001. d, Classification by PCA analysis using plasma proteins detected by Shotgun-LC-MS1 and significantly changed with running (unpaired Student’s two-tailed t test, P < 0.05). Data are Log2 transformed and missing values imputed by using the mean of each group. Control plasma (grey); runner plasma (orange). (n = 8 per group). e, Hierarchical networks of the abundance of gene ontology (GO) terms (Fisher’s exact test, P < 0.05) related to biological processes using REVIGO (Resnik measurement, 0.7 distance). GO terms correspond to the proteins significantly change with running when comparing CP with RP (unpaired Student’s two-tailed t test, P < 0.05) and shown in Fig. 3b.

Extended Data Fig. 4 Assessment of fibrinolysis and measurement of clot formation in runner plasma.

a, The formation and lysis of clots in control and runner’s plasma as measured by the Euglobulin Clot Lysis Time [ECLT] assay. Clot formation and lysis is measured under continuous spectrophotometric absorbance readings at 405nm. (Maximum clot formation is defined as the maximum absorbance; Clot lysis time is defined as the as the time at which the curve reaches an absorbance of 0.05 or less; Time to half lysis is defined as the time at which the curve reaches 50% of clot lysis) (n=6-7 per group). Means ± s.e.m; unpaired Student’s two-tailed t test; n.s., not significant, * P < 0.05. b, Amount of blood lost in runner and control mice after tail clipping as measured by the haemoglobin concentration (n = 7 per group). Means ± s.e.m; unpaired Student’s two-tailed t test; n.s., not significant. c, Measuring the activity of the classical, alternative, and lectin complement pathways in control versus runner’s plasma. Plots show absorbance at 450nm indicative of levels of membrane C5b-9 attack complex formation induced by IgM, LPS, or Mannose Binding Lectin (MBL) respectively (n = 10–13 per group). Means ± s.e.m; Unpaired Student’s two-tailed t test; n.s., not significant.

Extended Data Fig. 5 Changes in plasma proteins in female and male mice in response to running .

a - b. Volcano plot showing proteins significantly changed (P < 0.05) in RP versus CP in female or male mice. Downregulated proteins (blue); upregulated proteins (red). Unpaired Student’s two-tailed t test without FDR correction. c – d. Representative plasma proteins of the complement and coagulation pathways in male or female mice with 28 days of running (n = 8 per group). Means ± s.e.m, Unpaired Student’s two-tailed t test.

Extended Data Fig. 6 Immunodepletion of clusterin in runner plasma abrogates its anti-inflammatory properties on the hippocampus.

a. Male mice (3-4 months of age) were injected with LPS and treated with saline (LPS – SAL, n=10), runner plasma (LPS – RP, n=10), runner plasma without CLU (LPS – RP – CLU, n=8), runner plasma without FH (LPS – RP – FH, n=8), runner plasma without LIFR (LPS – RP – LIFR, n=8)) or runner plasma without PDEF (LPS – RP – PDEF, n=8). Heat map representing the relative differences between the mean of each group (zscore of means) on selected inflammatory gene markers in the hippocampus. b. Western blotting shows that CLU removal from a plasma sample after depletion via CLU antibody coupled to superparamagnetic beads using a Dynabeads® Antibody Coupling Kit bind to CLU.

Extended Data Fig. 7 Expression of Clu and its receptor Lrp8 across different cell types and organs of the adult mouse.

a. Clu mRNA levels across organs quantified from the bulk RNA-seq dataset conducted on different organs of the adult mouse56. b. Bar chart showing the top 20 Clu expressing cells at the mRNA level among >100 different cell types of the adult mouse quantified from the Tabula Muris Atlas dataset40 (n=4 male and n = 4 female biological replicates). c. The mRNA levels of the Clu receptor LRP8/ApoER2 across organs quantified from the bulk RNA-seq dataset conducted on different organs of the adult mouse56. d. Bar chart showing the top 20 Lrp8 expressing cells at the mRNA level among >100 different cell types of the adult mouse quantified from the Tabula Muris Atlas dataset40.

Extended Data Fig. 8 Experimental design, cell population consistency, and pathways analysis of the scRNA-seq experiment conducted on hippocampal BEC isolated from LPS and CLU treated mice.

a, Confocal representative images show peripherally injected rCLU tagged with Atto-647N or control PBS containing Atto-647N colocalized with cells of the cerebrovasculature, arteries (SMA), capillaries and veins (Tfrc). Scale bars, 10 µm. b, Schematic depicting the experimental paradigm followed for the injections of the three groups. 3- to 4-month-old male mice received Saline only injections (Sal), LPS plus saline treatments (LPS), or LPS plus recombinant CLU (LPS+Clu). BECs were isolated from n = 4-5 per group. c, tSNE plots showing the cellular proportions, numbers, and distributions in the three experimental groups (Sal, LPS, LPS+Clu). d, tSNE plots show distribution of BECs among arterial, capillary, and venous cells by group. Combined tSNE plot for BECs sorted from 3- to 4-month-old male mice (n = 4-5 mice per group) treated with Saline, LPS, and LPS+CLU. (Cells labelled as BEC are of low quality and were excluded from differential expression analysis.). e, Scatter plots show a list of selected genes altered in BECs (arterial, capillary, and venous) by acute inflammation (LPS) and reversed by CLU treatment. Coloured genes represent genes that pass the cutoff fold change of 1.1. Green: Genes increased by inflammation and reversed by CLU. Blue: Genes reduced by inflammation and reversed by CLU. (Log FC: natural logarithm of fold change). f-g, Dotplot showing Gene Ontology (GO) Biological Processes terms for BEC genes (Benjamini–Hochberg adjustment test, FDR < 0.05) that decrease or increase by LPS treatment and are reversed by CLU. Genes were selected based on the cutoff fold change of 1.1. The images in b were generated using MediaLab (https://medialab.biochem.wisc.edu/clip-art/).

Extended Data Fig. 9 Experimental design, cell population consistency, and pathways analysis of the scRNA-seq experiment conducted on hippocampal BEC isolated from APP and CLU treated mice.

a, Schematic depicting the experimental paradigm followed for the injections of the four groups. 14-months-old male Wild Type (WT) and APP transgenic mice that received no treatment, 17-months-old male APP mice that received saline only injections (APP+Sal) or repetitive CLU injections (APP+Clu) (BECs were isolated from n = 4-5 per group). b, tSNE plots showing the cellular proportions, numbers, and distributions in the four experimental groups (WT, APP, APP+Sal, APP+Clu). c, tSNE plots show distribution of BECs among arterial, capillary, and venous cells by group. Combined tSNE plot for BECs sorted from 14-month-old male wild-type and APP transgenic mice and 17-month-old male APP mice treated with Saline or CLU (BECs sorted from n = 4-5 mice per group). (Cells labelled as BEC are of low quality and were excluded from differential expression analysis.). d, Scatter plots show a list of selected genes altered in BECs (arterial, capillary, and venous) by chronic (APP) inflammation and reversed by CLU treatment. Coloured genes represent genes that pass the cutoff fold change of 1.1. Green: Genes increased by inflammation and reversed by CLU. Blue: Genes reduced by inflammation and reversed by CLU. (Log FC: natural logarithm of fold change). e, Dotplot showing Gene Ontology (GO) Biological Processes terms for BEC genes (Benjamini–Hochberg adjustment test, FDR < 0.05) that decrease or increased in APP mice compared to wild types and are reversed by CLU. Genes were selected based on the cutoff fold change of 1.1. f, Venn Diagram showing the number of unique and common genes that are increased by LPS and APP and reversed by CLU and reduced by LPS and APP and reversed by CLU (Extended Data Fig. 8f,g and 9e). g, Heatmap showing the common 20 genes increased with acute (LPS) inflammation and reversed by rCLU treatment (from Extended Data Fig. 8e panel). h, Dot plot showing Gene Ontology (GO) Biological Processes terms for BEC genes (Benjamini–Hochberg adjustment test, FDR < 0.05) of the common genes (from panel f, left) that increased with acute (LPS) and chronic (APP) inflammation and are reversed by Clu treatment. The images in a were generated using MediaLab (https://medialab.biochem.wisc.edu/clip-art/).

Extended Data Fig. 10 Changes in cortical gene expression in response to LPS inoculation and treatment with CLU.

a. Number of DEGs (Wald test; P < 0.1) between Saline-treated mice (n = 4) and either LPS (n =8) or LPS+rCLU (n = 7) injected animals. b. Venn diagrams depicting the overlap of DEGs (shown in panel a) in whole cortex of mice inoculated with LPS or LPS+rCLU, relative to the Saline-injected controls. c – d. Boxplot representation of scaled expression levels of 200 differentially up- and down-regulated genes under LPS as opposed to Saline controls. Whiskers represent the first and fifth quartiles, box edges represent the second and fourth quartiles and the centre line represents the third quartile/median. Two-sided Wilcoxon rank-sum test. e – f. Representative GO enrichment categories of LPS significantly regulated genes. Lengths of bars represent negative ln-transformed P using two-sided Fisher’s exact test. Colours indicate gene-wise log2 fold-changes (log2(FC)) between (e) LPS and Saline treatment or (f) LPS+rCLU and LPS treatment. Numbers beside bars indicate DEGs induced by LPS in that GO category.

Extended Data Fig. 11 Changes in plasma proteins of the complement and coagulation pathways in humans and male mice in response to exercise.

Schematic representation of the significantly changed (P < 0.05) plasma proteins of complement and coagulation cascades in humans and mice after exercise. Dotted black arrows indicate relationship between factors of the complement and the coagulation system. The diagram was generated using Servier Medical Art (https://smart.servier.com), MediaLab (https://medialab.biochem.wisc.edu/clip-art/) and Freepik (https://www.freepik.com).

Extended Data Table 1 Exercise-induced factors that affect the brain

Supplementary information

Supplementary Information

A guide to Supplementary Tables 1–20.

Reporting Summary

Supplementary Tables

This file contains Supplementary Tables 1–20 – see Supplementary Information document for full descriptions..

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

De Miguel, Z., Khoury, N., Betley, M.J. et al. Exercise plasma boosts memory and dampens brain inflammation via clusterin. Nature 600, 494–499 (2021). https://doi.org/10.1038/s41586-021-04183-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-021-04183-x

Further reading

Comments

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.

Search

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