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.

Microbiota from young mice counteracts selective age-associated behavioral deficits

Abstract

The gut microbiota is increasingly recognized as an important regulator of host immunity and brain health. The aging process yields dramatic alterations in the microbiota, which is linked to poorer health and frailty in elderly populations. However, there is limited evidence for a mechanistic role of the gut microbiota in brain health and neuroimmunity during aging processes. Therefore, we conducted fecal microbiota transplantation from either young (3–4 months) or old (19–20 months) donor mice into aged recipient mice (19–20 months). Transplant of a microbiota from young donors reversed aging-associated differences in peripheral and brain immunity, as well as the hippocampal metabolome and transcriptome of aging recipient mice. Finally, the young donor-derived microbiota attenuated selective age-associated impairments in cognitive behavior when transplanted into an aged host. Our results reveal that the microbiome may be a suitable therapeutic target to promote healthy aging.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: FMT alters the microbiota on a taxonomic and functional level.
Fig. 2: FMT from young to aged mice modulate peripheral immunity and hippocampal neuroimmunity.
Fig. 3: FMT from young to aged mice shapes the hippocampal metabolome.
Fig. 4: FMT from young to aged mice influence behavior.

Data availability

Sequencing data have been deposited in the Sequence Read Archive. Microbiota sequencing data are available at http://www.ncbi.nlm.nih.gov/bioproject/737691 (accession PRJNA737691) and hippocampal RNA-seq data are available at http://www.ncbi.nlm.nih.gov/bioproject/738160 (accession PRJNA738160). Source data are provided with this paper. Other data that support the findings of this study are available from the corresponding author upon request.

Code availability

Custom R scripts are available online at https://github.com/thomazbastiaanssen/Tjazi. All R scripts are available online at https://github.com/thomazbastiaanssen/FMT_Aging.

References

  1. 1.

    Bettio, L. E. B., Rajendran, L. & Gil-Mohapel, J. The effects of aging in the hippocampus and cognitive decline. Neurosci. Biobehav. Rev. 79, 66–86 (2017).

    PubMed  Article  Google Scholar 

  2. 2.

    Cryan, J. F. et al. The microbiota-gut-brain axis. Physiol. Rev. 99, 1877–2013 (2019).

    PubMed  Article  CAS  Google Scholar 

  3. 3.

    Claesson, M. J. et al. Gut microbiota composition correlates with diet and health in the elderly. Nature 488, 178–184 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  4. 4.

    Stebegg, M. et al. Heterochronic faecal transplantation boosts gut germinal centres in aged mice. Nat. Commun. 10, 2443 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  5. 5.

    Donaldson, D. S., Pollock, J., Vohra, P., Stevens, M. P. & Mabbott, N. A. Microbial stimulation reverses the age-related decline in M cells in aged mice. iScience 23, 101147 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  6. 6.

    Li, Y. et al. Age-related shifts in gut microbiota contribute to cognitive decline in aged rats. Aging 12, 7801–7817 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. 7.

    Kundu, P. et al. Neurogenesis and prolongevity signaling in young germ-free mice transplanted with the gut microbiota of old mice. Sci. Transl. Med. 11, eaau4760 (2019).

    PubMed  Article  CAS  Google Scholar 

  8. 8.

    Fransen, F. et al. Aged gut microbiota contributes to systemical inflammaging after transfer to germ-free mice. Front. Immunol. 8, 1385 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  9. 9.

    D’Amato, A. et al. Faecal microbiota transplant from aged donor mice affects spatial learning and memory via modulating hippocampal synaptic plasticity- and neurotransmission-related proteins in young recipients. Microbiome 8, 140 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Smith, P. et al. Regulation of life span by the gut microbiota in the short-lived African turquoise killifish. eLife 6, e27014 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Iwai, S. et al. Piphillin: improved prediction of metagenomic content by direct inference from human microbiomes. PLoS ONE 11, e0166104 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. 12.

    Valles-Colomer, M. et al. The neuroactive potential of the human gut microbiota in quality of life and depression. Nat. Microbiol. 4, 623–632 (2019).

    PubMed  Article  CAS  Google Scholar 

  13. 13.

    Dalile, B., Van Oudenhove, L., Vervliet, B. & Verbeke, K. The role of short-chain fatty acids in microbiota–gut-brain communication. Nat. Rev. Gastroenterol. Hepatol. 16, 461–478 (2019).

    PubMed  Article  Google Scholar 

  14. 14.

    Thevaranjan, N. et al. Age-associated microbial dysbiosis promotes intestinal permeability, systemic inflammation, and macrophage dysfunction. Cell Host Microbe 21, 455–466 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  15. 15.

    Mohle, L. et al. Ly6C(hi) monocytes provide a link between antibiotic-induced changes in gut microbiota and adult hippocampal neurogenesis. Cell Rep. 15, 1945–1956 (2016).

    PubMed  Article  CAS  Google Scholar 

  16. 16.

    Baliu-Pique, M. et al. Short lifespans of memory T-cells in bone marrow, blood, and lymph nodes suggest that T-cell memory is maintained by continuous self-renewal of recirculating cells. Front. Immunol. 9, 2054 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  17. 17.

    Tan, J. et al. Dietary fiber and bacterial SCFA enhance oral tolerance and protect against food allergy through diverse cellular pathways. Cell Rep. 15, 2809–2824 (2016).

    PubMed  Article  CAS  Google Scholar 

  18. 18.

    Uhl, B. et al. Aged neutrophils contribute to the first line of defense in the acute inflammatory response. Blood 128, 2327–2337 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  19. 19.

    Erny, D. et al. Host microbiota constantly control maturation and function of microglia in the CNS. Nat. Neurosci. 18, 965–977 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  20. 20.

    van de Wouw, M., Boehme, M., Dinan, T. G. & Cryan, J. F. Monocyte mobilisation, microbiota & mental illness. Brain Behav. Immun. 81, 74–91 (2019).

    PubMed  Article  CAS  Google Scholar 

  21. 21.

    Elmore, M. R. P. et al. Replacement of microglia in the aged brain reverses cognitive, synaptic, and neuronal deficits in mice. Aging Cell 17, e12832 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  22. 22.

    Kozareva, D. A. et al. Absence of the neurogenesis-dependent nuclear receptor TLX induces inflammation in the hippocampus. J. Neuroimmunol. 331, 87–96 (2019).

    PubMed  Article  CAS  Google Scholar 

  23. 23.

    Hickman, S. E. et al. The microglial sensome revealed by direct RNA sequencing. Nat. Neurosci. 16, 1896–1905 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  24. 24.

    Mecca, C., Giambanco, I., Donato, R. & Arcuri, C. Microglia and aging: the role of the TREM2-DAP12 and CX3CL1-CX3CR1 axes. Int. J. Mol. Sci. 19, 318 (2018).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  25. 25.

    Schafer, D. P. et al. Microglia sculpt postnatal neural circuits in an activity and complement-dependent manner. Neuron 74, 691–705 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  26. 26.

    Stephan, A. H. et al. A dramatic increase of C1q protein in the CNS during normal aging. J. Neurosci. 33, 13460–13474 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  27. 27.

    Recio, C. et al. Activation of the immune-metabolic receptor GPR84 enhances inflammation and phagocytosis in macrophages. Front. Immunol. 9, 1419 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  28. 28.

    Kam, T. I. et al. FcγRIIb mediates amyloid-β neurotoxicity and memory impairment in Alzheimer’s disease. J. Clin. Invest. 123, 2791–2802 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  29. 29.

    Gemma, C., Bachstetter, A. D. & Bickford, P. C. Neuron-microglia dialogue and hippocampal neurogenesis in the aged brain. Aging Dis. 1, 232–244 (2010).

    PubMed  Google Scholar 

  30. 30.

    Ogbonnaya, E. S. et al. Adult hippocampal neurogenesis is regulated by the microbiome. Biol. Psychiatry 78, e7–e9 (2015).

    PubMed  Article  Google Scholar 

  31. 31.

    Canfield, C.-A. & Bradshaw, P. C. Amino acids in the regulation of aging and aging-related diseases. Transl. Med. Aging 3, 70–89 (2019).

    Article  Google Scholar 

  32. 32.

    Biyong, E. F. et al. Dietary vitamin A supplementation prevents early obesogenic diet-induced microbiota, neuronal and cognitive alterations. Int. J. Obes. 45, 588–598 (2021).

    Article  CAS  Google Scholar 

  33. 33.

    Provensi, G. et al. Preventing adolescent stress-induced cognitive and microbiome changes by diet. Proc. Natl Acad. Sci. USA 116, 9644–9651 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  34. 34.

    Touyarot, K. et al. A mid-life vitamin A supplementation prevents age-related spatial memory deficits and hippocampal neurogenesis alterations through CRABP-I. PLoS ONE 8, e72101 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. 35.

    McQuail, J. A., Frazier, C. J. & Bizon, J. L. Molecular aspects of age-related cognitive decline: the role of GABA signaling. Trends Mol. Med. 21, 450–460 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  36. 36.

    Puigdellivol, M., Allendorf, D. H. & Brown, G. C. Sialylation and galectin-3 in microglia-mediated neuroinflammation and neurodegeneration. Front. Cell Neurosci. 14, 162 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  37. 37.

    Hunsberger, H. C. et al. Divergence in the metabolome between natural aging and Alzheimer’s disease. Sci. Rep. 10, 12171 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  38. 38.

    Malinski, T. Nitric oxide and nitroxidative stress in Alzheimer’s disease. J. Alzheimers Dis. 11, 207–218 (2007).

    PubMed  Article  CAS  Google Scholar 

  39. 39.

    Schaffer, A. E., Pinkard, O. & Coller, J. M. tRNA metabolism and neurodevelopmental disorders. Annu. Rev. Genomics Hum. Genet. 20, 359–387 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  40. 40.

    Palmieri, E. M. et al. Blockade of glutamine synthetase enhances inflammatory response in microglial cells. Antioxid. Redox Signal. 26, 351–363 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  41. 41.

    Olson, C. A. et al. The gut microbiota mediates the anti-seizure effects of the ketogenic diet. Cell 173, 1728–1741(2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  42. 42.

    Jaggar, M., Rea, K., Spichak, S., Dinan, T. G. & Cryan, J. F. You’ve got male: sex and the microbiota-gut–brain axis across the lifespan. Front. Neuroendocrinol. 56, 100815 (2020).

    PubMed  Article  Google Scholar 

  43. 43.

    Darch, H. T., Collins, M. K., O’Riordan, K. J. & Cryan, J. F. Microbial memories: sex-dependent impact of the gut microbiome on hippocampal plasticity. Eur. J. Neurosci. https://doi.org/10.1111/ejn.15119 (2021).

  44. 44.

    Bruce-Keller, A. J. et al. Obese-type gut microbiota induce neurobehavioral changes in the absence of obesity. Biol. Psychiatry 77, 607–615 (2015).

    PubMed  Article  Google Scholar 

  45. 45.

    Roux, S., Sable, E. & Porsolt, R. D. Primary observation (Irwin) test in rodents for assessing acute toxicity of a test agent and its effects on behavior and physiological function. Curr. Protoc. Pharmacol. Chapter 10, Unit 10.10 (2005).

    PubMed  Google Scholar 

  46. 46.

    O’Leary, J. D. et al. The nuclear receptor Tlx regulates motor, cognitive and anxiety-related behaviours during adolescence and adulthood. Behav. Brain Res. 306, 36–47 (2016).

    PubMed  Article  CAS  Google Scholar 

  47. 47.

    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. 48.

    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

    Article  CAS  Google Scholar 

  49. 49.

    Fernandes, A. D. et al. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2, 15 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).

    Article  Google Scholar 

  51. 51.

    Hamidi, B., Wallace, K., Vasu, C. & Alekseyenko, A. V. Wd$W_{d}^{*}$-test: robust distance-based multivariate analysis of variance. Microbiome 7, 51 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Bastiaanssen, T. F. S. et al. Volatility as a concept to understand the impact of stress on the microbiome. Psychoneuroendocrinology 124, 105047 (2021).

    PubMed  Article  CAS  Google Scholar 

  53. 53.

    van de Wouw, M. et al. The role of the microbiota in acute stress-induced myeloid immune cell trafficking. Brain Behav. Immun. 84, 209–217 (2020).

    PubMed  Article  CAS  Google Scholar 

  54. 54.

    Scott, K. A. et al. Revisiting Metchnikoff: age-related alterations in microbiota–gut–brain axis in the mouse. Brain Behav. Immun. 65, 20–32 (2017).

    PubMed  Article  Google Scholar 

  55. 55.

    Golubeva, A. V. et al. Microbiota-related changes in bile acid & tryptophan metabolism are associated with gastrointestinal dysfunction in a mouse model of autism. EBioMedicine 24, 166–178 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    van de Wouw, M. et al. Distinct actions of the fermented beverage kefir on host behaviour, immunity and microbiome gut-brain modules in the mouse. Microbiome 8, 67 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Friard, O. & Gamba, M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330 (2016).

    Article  Google Scholar 

  58. 58.

    Vorhees, C. V. & Williams, M. T. Morris water maze: procedures for assessing spatial and related forms of learning and memory. Nat. Protoc. 1, 848–858 (2006).

    PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Boehme, M. et al. Mid-life microbiota crises: middle age is associated with pervasive neuroimmune alterations that are reversed by targeting the gut microbiome. Mol. Psychiatry 25, 2567–2583 (2019).

    PubMed  Article  Google Scholar 

  60. 60.

    Higaki, A. et al. Recognition of early stage thigmotaxis in Morris water maze test with convolutional neural network. PLoS ONE 13, e0197003 (2018).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  61. 61.

    Boehme, M. et al. Impact of indomethacin on neuroinflammation and hippocampal neurogenesis in aged mice. Neurosci. Lett. 572, 7–12 (2014).

    PubMed  Article  CAS  Google Scholar 

  62. 62.

    Ito, D. et al. Microglia-specific localisation of a novel calcium binding protein, Iba1. Brain Res. Mol. Brain Res. 57, 1–9 (1998).

    PubMed  Article  CAS  Google Scholar 

  63. 63.

    Tsien, J. Z., Huerta, P. T. & Tonegawa, S. The essential role of hippocampal CA1 NMDA receptor-dependent synaptic plasticity in spatial memory. Cell 87, 1327–1338 (1996).

    PubMed  Article  CAS  Google Scholar 

  64. 64.

    Ferreira, T. A. et al. Neuronal morphometry directly from bitmap images. Nat. Methods 11, 982–984 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  65. 65.

    Gonzalez Ibanez, F. et al. Immunofluorescence staining using IBA1 and TMEM119 for microglial density, morphology and peripheral myeloid cell infiltration analysis in mouse brain. J. Vis. Exp. https://doi.org/10.3791/60510 (2019).

  66. 66.

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  67. 67.

    Motulsky, H. J. & Brown, R. E. Detecting outliers when fitting data with nonlinear regression - a new method based on robust nonlinear regression and the false discovery rate. BMC Bioinf. 7, 123 (2006).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank Metabolon Inc. for performing untargeted metabolomics of the hippocampal samples and Edinburgh Genomics for sequencing and their help in analyzing the RNA-seq dataset. Flow cytometry analysis was performed at the APC Microbiome Ireland Flow Cytometry Platform (led by P. Stamou and K. Nally). The authors are grateful for the invaluable technical assistance of C. Manley, V. Caputi, S. Boscaini, P. Ventura-Silva, S. Cussotto, K. Rea, K. J. O’Riordan, M. G. Codagnone, J. S. Cruz-Pereira and M. Taruc. We also thank K. Sandhu for her artistic assistance. This work was supported by research grants from Science Foundation Ireland (SFI) to APC Microbiome lreland through the Irish Government’s National Development Plan (grants SFI/12/RC/2273 and SFI/12/RC/2273_P2) and through the Joint Programming Initiative: a healthy diet for a healthy life; investigating Nutrition and Cognitive Function (NutriCog) by SFI Grant ‘A Menu for Brain Responses Opposing Stress-Induced Alternations in Cognition’ (AMBROSIAC) 15/JPHDHL/3270.

Author information

Affiliations

Authors

Contributions

M.B. and J.F.C. have contributed to the conception and design of the work. M.B., K.E.G., T.F.S.B. and J.F.C. wrote the manuscript. M.B., K.E.G., T.F.S.B., G.M.M., P.D.C., O.F.O., T.G.D. and J.F.C. critically revised the manuscript. All animal work, including behaviors was performed and analyzed by M.B., K.E.G., E.M. and C.S.M.C. FMT preparation was performed by M.B., K.E.G., L.O.R., M.C.N. and E.M. Tissue collection was performed by K.E.G., M.J., E.H., M.B. and A.V.G. F.C. performed DNA extractions and ran 16S sequencing. Microbiota analysis was performed by T.F.S.B. and interpreted by T.F.S.B., A.V.G., M.B. and K.E.G. Peripheral immune analysis was designed and performed by M.B., M.v.d.W., K.E.G., A.G.G., G.M.M., N.R., M.S. and F.D. and interpreted by M.B., M.v.d.W., K.E.G., G.M.M. and A.G.G. Neuroimmunity analysis was performed by K.E.G. and M.B. Hippocampal metabolomics were analyzed by T.F.S.B. and interpreted by T.F.S.B., M.B., K.E.G., J.F.C. and G.M.M. Hippocampal transcriptomics were analyzed T.F.S.B. and interpreted by T.F.S.B., M.B., K.E.G., J.F.C. and G.M.M. Hippocampal neurogenesis analysis was performed by K.E.G. Figures were constructed by T.F.S.B., K.E.G., M.B. and J.F.C. All other acquisition, analysis and interpretation of data not previously indicated were performed by M.B., K.E.G., T.F.S.B., M.v.d.W., A.V.G., G.M.M., S.S., R.S.F., A.V.G., P.F., M.J.C., P.D.C., O.F.O., T.G.D. and J.F.C. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work.

Corresponding author

Correspondence to John F. Cryan.

Ethics declarations

Competing interests

J.F.C. has been an invited speaker at meetings organized Precision Biotics, Alkermes and Janssen and has received research funding from Cremo, Nutricia, DuPont (IFF) and a consultant for Alkermes and Nestlé. T.G.D. has been an invited speaker at meetings organized by Servier, Lundbeck, Janssen and AstraZeneca and has received research funding from Cremo, Nutricia. O.F.O. has received funding from Alkermes and been an invited speaker by Janssen. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Aging 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

Extended Data Fig. 1 Aging Impacts on Physiological Parameters.

a, No differences were found in telomere length amongst groups. n = 10 (young yFMT), 12 (aged oFMT), 12 (aged yFMT). b, Aged mice exhibited a decrease in intestinal motility independent of the donor FMT (F(2,50)=9.055, p < 0.001). Post-hoc analysis indicated decreased intestinal transit time in aged oFMT (p < 0.001) and aged yFMT (p = 0.002) compared to young yFMT. Aging decreased fecal water content independent of the donor FMT, respectively (F(2,50)=5.464, p = 0.007) with decreased fecal water content in both aged oFMT (p = 0.007) and aged yFMT (p = 0.041) compared to young yFMT. n = 16 (young yFMT), 16 (aged oFMT), 21 (aged yFMT). c, Aged mice exhibited decreased cecum weight (F(2,48)=22.78, p < 0.001; post-hoc: p < 0.001) which was unaltered by FMT from young to aged mice. n = 15 (young yFMT), 15 (aged oFMT), 20 (aged yFMT). d, Aged mice showed decreased plasma corticosterone levels independent of the donor FMT (F(2,48)=28.78, p < 0.001) with young yFMT compared to aged oFMT (p < 0.001) and aged yFMT (p < 0.001), respectively. n = 15 (young yFMT), 15 (aged oFMT), 20 (aged yFMT). e, Concomitant with the decreased corticosterone levels, both left and right adrenal weight were decreased by age. Organ weight is depicted as % relative to body weight. n = 14 (young yFMT), 12 (aged oFMT), 18 (aged yFMT). f, Treatment significantly altered spleen weight (H(3)=12.93, p = 0.016). Post-hoc analysis (Dunn’s) indicates reduced spleen weight in aged yFMT compared to young yFMT (p = 0.001). n = 16 (young yFMT), 15 (aged oFMT), 19 (aged yFMT). g, Aged mice, independent of the donor FMT, showed an accumulation of fat depots, subcutaneous (H(3)=12.84, p = 0.002, Kruskal-Wallis post-hoc Dunn’s) and retroperitoneal (H(3)=21.24, p < 0.001, Kruskal-Wallis post-hoc Dunn’s) while inter scapular brown fat (iBAT) remained unaltered. n = 15 (young yFMT), 16 (aged oFMT), 21 (aged yFMT). (a-g) If not otherwise indicated: data was analyzed by one-way ANOVA post-hoc Holm–Sidak. All statistical tests were conducted two-sided. Each datapoint represents one independent mouse. Mean ± s.e.m., *p < 0.05, **p < 0.01, ***p < 0.001.

Source data

Extended Data Fig. 2 yFMT did not Improve the Aging-Associated Reduction in Survival of Newborn Hippocampal Neurons or alter Microglia Morphology.

a, Aging induced dramatic declines in the number of surviving new-born neurons, labelled with BrdU + /NeuN + (F(2,12)=55.23, p < 0.001; young yFMT vs aged oFMT (p < 0.001). However, there were no differences in neuron survival between aged oFMT and aged yFMT mice (p = 0.832). One-way ANOVA post-hoc Holm–Sidak. Mean ± s.e.m. n = 5 independent mice per group. b, There were no significant differences in microglia branching morphology revealed by Sholl analysis. Mean (number of intersections per µm radius from center) ± s.e.m. n = 5 independent mice per group. Scale bar = 200 µm.

Source data

Extended Data Fig. 3 Impact of Aging on Peripheral Innate and Adaptive Immune System.

Adaptive and innate immunity was assessed by Flow Cytometry in both the mesenteric lymph nodes (at the end of the study) and the systemic circulation (before mice were assessed for behavior). a, Adaptive immunity in MLNs: A profound age effect was noted in the overall population of CD4+ T-cells (H(3)=30.49, p < 0.001, Kruskal-Wallis post-hoc Dunn’s) while CD8+ T-cells were not affected by age nor FMT. Subsequently, we assessed subpopulations of both, CD4+ and CD8+ T-cells, which both, early activated (F(2,47)=31.79, p < 0.001) and memory CD4+ T-cells (F(2,48)=45.47, p < 0.001), and memory CD8+ T-cells (F(2,44)=68.14, p < 0.001) showed an age-associated increase which was unaffected by yFMT into aged mice. n = 15-16 (young yFMT), 16 (aged oFMT), 19-21 (aged yFMT). b, Adaptive immunity in the circulation: A decrease of T-regulatory cells and their transcription factor, foxp3, was observed in both age groups (F(2,50)=6.789, p = 0.003 and F(2,50)=12.90, p < 0.001, respectively). In addition, CD4+, CD25+ T-cells were decreased in both age groups (F(2,50)=6.554, p = 0.003) while CD25 expression on CD4+, CD25+ T-cells were increased in aged yFMT compared to young yFMT (H(3)=6.179, p = 0.046, Kruskal-Wallis post-hoc Dunn’s: p = 0.041). Similarly to MLNs, the overall population of CD4+ T-cells was strongly decreased by age (F(2,50)=89.4, p < 0.001) while CD8+ T-cells remained unchanged. Subsequent assessment of the respective subpopulations revealed a strong increase in both age groups in early activated (H(3)=20.74, p < 0.001, Kruskal-Wallis post-hoc Dunn’s) and memory CD4+ T-cells (H(3)=23.05, p < 0.001, Kruskal-Wallis post-hoc Dunn’s), and memory CD8+ T-cells (F(2,49)=51.81, p < 0.001) while early activated CD8+ T-cells remained unchanged. Further, we assessed the expression of the adhesion marker, CD62L, on both overall CD4+ and CD8+ T-cells. A decrease of CD62L expression in both age groups were found in CD4+ T-cells (F(2,50)=4.875, p = 0.012). In contrast, aged mice exhibited an increase in CD62L expression in CD8+ T-cells (F(2,50)=4.684, p = 0.014) from which Aged yFMT was significantly different to Young yFMT (p = 0.013) while aged oFMT showed a trend (p = 0.068). n = 14-16 (young yFMT), 15-16 (aged oFMT), 20-21 (aged yFMT). (c) Innate immunity in circulation: assessment of three subpopulations of monocytes; Ly6Chi, Ly6Cint and Ly6Clow monocytes revealed a strong age impact on the overall population and subsequent protein expression levels of activation and trafficking markers. An age-related increase was found in the overall population of Ly6Chi (F(2,46)=5.489, p = 0.007), Ly6Cint (F(2,45)=3.316, p = 0.045) and Ly6Clow monocytes (H(3)=14.21, p < 0.001, Kruskal-Wallis post-hoc Dunn’s). Similarly, all three populations exhibited an age-related increased expression in the trafficking receptor CX3CR1 (Ly6Chi: F(2,44)=4.22, p = 0.021, Ly6Cint: F(2,44)=14.07, p < 0.001, and Ly6Clow monocytes: F(2,46)=5.832, p = 0.006) which remained unaffected by yFMT into aged mice. Moreover, aging triggered an increase in the activation marker CD11b on both Ly6Cint and Ly6Chi monocytes F(2,45)=29.51, p < 0.001, post-hoc: p < 0.001, and F(2,46)=3.872, p = 0.028, post-hoc: p = 0.032, respectively), and while these aging-related effects persisted in the Ly6Cint monocytes of aged yFMT mice (p < 0.001), there was no significant difference apparent in the CD11b marker on Ly6Chi monocytes in aged yFMT compared to young yFMT mice. Further, both aged groups showed a decreased expression of CD62L on Ly6Cint monocytes (F(2,43)=16.43, p < 0.001) while both, Ly6Chi and Ly6Clow monocytes remained unaffected. In addition, Aged oFMT showed an increase in Ly6C expression on Ly6Cint monocytes ((F(2,46)=6.819, p = 0.003; post-hoc: p = 0.002) which tended to be rescued in aged yFMT (p = 0.071). Assessment of CD11b+, CD11c+ dendritic cells revealed an age-related increase in both age groups regarding the overall population of CD11c+ dendritic cells in the circulation (F(2,46)=8.221, p < 0.001), the trafficking receptor CX3CR1 (F(2,46)=6.467, p = 0.003), and the activation marker CD11b (F(2,46)=6.867, p = 0.003). Notably, no differences were found in Ly6G+ neutrophils while CD49+ NK cells showed a strong increase in both aged groups (H(3)=15.86, p < 0.001, Kruskal-Wallis post-hoc Dunn’s). n = 12-15 (young yFMT), 15-16 (aged oFMT), 19-21 (aged yFMT). Mean ± s.e.m. *p < 0.05, **p < 0.01, ***p < 0.001, & p < 0.1. (a-c) If not otherwise indicated: data was analyzed by one-way ANOVA post-hoc Holm–Sidak. All statistical tests were conducted two-sided. Each datapoint represents one individual mouse.

Source data

Extended Data Fig. 4 Gating Strategy.

For MLNs, PBMCs were selected based on SSC-H and FSC-H using the FACSCalibur. F4/80+ Macrophages were further selected using CD11b+ and F4/80+. T-cells were gated through CD4+ and CD8+. To assess sub-populations of T-cells, each CD4+ and CD8+ T-cells were investigated with the markers for early activation (CD69) and memory cells (CD44hi). In addition, we assessed monocytes / neutrophils by gating on CD11b+ and Ly6C+, followed by differentiation with Ly6G for monocytes (CD11b+, Ly6C+, Ly6G) and neutrophils (CD11b+, Ly6C+, Ly6G+). Monocytes were further determined for the proportion of Ly6Chi monocytes which express Ly6C on a high level (CD11b+, Ly6G, Ly6Chi). Dendritic cells were investigated through gating for CD11c+ and MHCII+, and subsequent CD103+ marker expression. For the circulation assessment, we used a more comprehensive panel and utilized the FACSCelesta, which enabled initial gating for singlets, followed by selection of the PBMCs and live cells. We gated for CD4+ and CD8+ T-cells followed by subsequent assessment of each population for markers for early activation (CD69) and memory cells (CD44hi). We also investigated T-regulatory cells (CD4+, CD25+, Foxp3+) through gating for CD4+, CD25+ T-cell subpopulation followed by Foxp3+. In addition, we gated for CD49+ Natural killer (NK) cells through the CD4, CD8 population. Neutrophils were selected based on SSChi, Ly6G+ and CD11b+. Monocytes were selected through SSChi and Ly6G. Based on CD11b+ expression and their respective Ly6C expression, we differentiated the monocyte population into Ly6Cint monocytes and Ly6Chi monocytes. Dendritic cells were assessed through Ly6Clow and CD11c+. Ly6Clow monocytes were selected through Ly6Clow followed by CX3CR1+.

Extended Data Fig. 5 Additional behavioral readouts.

a, Short-term memory assessed by spontaneous alternation behavior (Y-maze). No significant differences were observed between groups. n = 16 (young yFMT), 15 (aged oFMT), 21 (aged yFMT). b, Short-term memory and exploration in the Novel Object Recognition (NOR) test. While long-term memory in the NOR was not significantly different across groups, there were significant differences in exploration of the objects (F(2,44)=3.388, p = 0.028), with aged mice receiving yFMT (aged yFMT) showing an increased exploration activity compared to aged oFMT (p = 0.043). n = 14 (young yFMT), 12 (aged oFMT), 19 (aged yFMT). c, Learning in Morris Water Maze: to better visualize the change in learning, readouts were further assessed by determining the % change from training day one (statistical analysis via RM One-Way ANOVA post-hoc Holm-Sidak followed by assessing differences per training day via one-way ANOVA; and AUC). Overall, there was a trend towards significant differences in learning in % change in mean distance from platform, path length, and average velocity, though there were no significance differences between groups following post-hoc corrections. Further assessment at each training day revealed significant age effects when comparing aged oFMT to young yFMT mice. Interestingly, aged yFMT mice showed no statistical difference compared to young yFMT mice, but significantly improved compared to aged oFMT mice in several parameters. First, aged oFMT mice displayed a reduced improvement in % change in mean distance from platform on Training Days 4 and 5 (F(2, 50)=4.349, p = 0.018, post-hoc p = 0.021; F(2, 50)=4.031, p = 0.024, post-hoc p = 0.046) as well as a trend on Day 3 (F(2, 50)=2.395, p = 0.102, post-hoc p = 0.097). Aged yFMT mice showed significant improvement compared to aged oFMT mice on Training Day 5 (p = 0.046) and trended towards significant difference on Day 4 (p = 0.056). This recovery in the effect of aging by yFMT was also evident in AUC, where aged oFMT mice had significantly higher AUC than young yFMT mice (F(2, 50)=3.842, p = 0.028, post-hoc p = 0.047) which was ameliorated by yFMT into aged mice. While there was no overall difference in % change in latency across training days, a significant age impairment is present on Training Day 4 (F(2, 50)=3.814, p = 0.029, post-hoc p = 0.031) where aged yFMT mice also trended towards significantly improving compared to aged oFMT (p = 0.089). Significant effects of age were apparent in % change in average path length on Training Days 3, 4, and 5 (Day 3: F(2, 50)=4.481, p = 0.016, post-hoc p = 0.022; Day 4: F(2, 50)=6.165, p = 0.004, post-hoc p = 0.004; and Day 5: F(2, 50)=5.580, p = 0.007, post-hoc p = 0.007). Furthermore, aged yFMT significantly improved compared to aged oFMT on these three training days (p = 0.039; p = 0.027; p = 0.030, respectively). This clear age effect was further evident when looking at AUC, where aged oFMT mice showed significantly increased AUC compared to young yFMT and aged yFMT mice (F(2, 50)=5.277, p = 0.008, post-hoc p = 0.016 and p = 0.016, respectively). Aged oFMT mice had significantly different % change in average velocity compared to both young yFMT and aged yFMT mice on Training Day 3 (F(2, 50)=4.351, p = 0.018, post-hoc p = 0.026 and p = 0.04, respectively), though this significance was not apparent in AUC. n = 16 (young yFMT), 16 (aged oFMT), 21 (aged yFMT). d, Social behavior and memory: Treatment affected social behavior with respect to the time the test mouse interacted with a con-specific mouse (over an object) in the second phase of the three-chamber social interaction test (F(2,49)=4.168, p = 0.021) while interaction with a novel mouse (over the now familiar mouse) in the third phase of the three-chamber social interaction test remained unaffected by the treatment. Post-hoc analysis indicated decreased interaction with a con-specific mouse in aged oFMT compared to young yFMT (p = 0.022), which trended towards being rescued by yFMT into aged mice (p = 0.077). n = 15 (young yFMT), 16 (aged oFMT), 21 (aged yFMT). e, Observation Battery: Bar plots show observation battery score prior to FMT compared to end of study. Both aged groups showed a decreased score prior to FMT compared to young yFMT (H(3)=13.33, p = 0.001; post-hoc: aged oFMT p = 0.001, and aged yFMT p = 0.038, respectively). In contrast, aged yFMT did not show a significant difference (p = 0.46) at the end of the study while aged oFMT retained a decreased score compared to young yFMT (H(3)=6.146, p = 0.046; Kruskal-Wallis post-hoc Dunn’s: p = 0.04). n = 16 (young yFMT), 16 (aged oFMT), 21 (Aged yFMT). f, Locomotion: Locomotion was unaltered by age (Aged oFMT) or FMT from young to aged mice (Aged yFMT). g, Anxiety-like behavior: neither age nor FMT affected time in center and number of entries into the center zone in the open field. n = 12-16 (young yFMT), 16 (aged oFMT), 20-21 (aged yFMT). h, No significant differences were observed in the Novelty-Induced Hypophagia behavioral test. n = 9 (young yFMT), 9 (aged oFMT), 7 (aged yFMT). Mean ± s.e.m. For MWM Training Day figures, young yFMT vs aged oFMT: #p < 0.10, *p < 0.05, **p < 0.01; aged yFMT vs aged oFMT: &p < 0.10, $p < 0.05; For all other figures: & p < 0.10, *p < 0.05, **p < 0.01. (a-i) If not otherwise indicated: data was analyzed by one-way ANOVA post-hoc Holm-Sidak. All statistical tests were conducted two-sided. Each datapoint represents one independent mouse.

Source data

Extended Data Fig. 6 FMT samples have a high diversity and cluster with the fecal microbiome of their donors.

a, Principal component analysis showing the compositions of the fecal microbiome in young and aged mice as measured in Aitchison distance, as well as the donor samples in triplicates. Ellipses represent the 95% confidence intervals per group. b, Violin plots showing that pooled FMT samples have a higher alpha diversity compared to those of the murine microbiome. Black horizontal lines in violin plots represent the medians. c, Violin plots showing the number of genera gained, lost and unchanged per mouse after FMT. No differences in the amounts of genera gained, lost or unchanged were found. d, Stacked bar plots showing the composition of the microbiome per treatment group per time point, including both types of FMT donor material. Genera that were never detected above 1% reads abundance were aggregated and labeled as ‘Rare Taxa’ for this figure. No statistical testing was done for the FMT donor samples due to the low sample number (n = 3 per group). n = 25 (young yFMT), 23 (aged oFMT), 27 (aged yFMT) independent mice.

Source data

Extended Data Fig. 7 yFMT influences hippocampal transcriptomics and yFMT-induced microbiome alterations functionally correlate with hippocampal metabolomics.

a, Pearson correlation coefficients for the hippocampal metabolites that were found to be altered in aging and restored towards young levels after yFMT. The vertical dashed lines depict a Pearson correlation coefficient of 0. Colors depict the treatment groups, whereas grey points represent the correlation coefficients after pooling all three groups. Opaque points with thick black borders display significant correlations after Benjamini-Hochberg correction at q < 0.1. Non-opaque points display the correlation coefficient for the other treatment groups. Full statistical information is available in Supplementary Table 4. b, Transcriptomics analysis revealed three hippocampal genes altered in aging and reversed by yFMT. Relative gene expression is depicted as violin plots. All three genes, Glul and mt-Rnr1 and mt-Rnr2 were significantly altered by aging (aged oFMT vs. young yFMT: p < 0.001, p = 0.001, p < 0.001, and q = 0.004, q = 0.016, and q = 0.007, respectively), and restored by yFMT (aged yFMT vs. aged oFMT: p = 0.003, p = 0.006, p = 0.003, and q = 0.125, q = 0.159, and q = 0.125, respectively). Mann-Whitney U-test followed by Storey’s q-value post-hoc tests: **p < 0.01, ***p < 0.001. n = 12 per group. Heatmap showing top 50 most differentially expressed genes between groups by FDR. Microglia sensome-related genes are indicated with a box.

Source data

Extended Data Fig. 8 Graphical Summary.

Microbiota from young mice rejuvenated aspects of aging-related alterations in the gut microbiome, peripheral and brain immunity, and restored metabolites and behavior.

Supplementary information

Supplementary Information

This contains additional information for the methodology (flow cytometry, metabolomics, RNA-seq pre-processing and immunohistochemistry).

Reporting Summary

Supplementary Table

The Excel table contains all nine Supplementary Tables (each supplementary table in one separate tab) combined into one Excel file.

Source data

Source Data Fig. 1

Statistical Source Data.

Source Data Fig. 2

Statistical Source Data.

Source Data Fig. 3

Statistical Source Data.

Source Data Fig. 4

Statistical Source Data.

Source Data Extended Data Fig. 1

Statistical Source Data.

Source Data Extended Data Fig. 2

Statistical Source Data.

Source Data Extended Data Fig. 3

Statistical Source Data.

Source Data Extended Data Fig. 5

Statistical Source Data.

Source Data Extended Data Fig. 6

Statistical Source Data.

Source Data Extended Data Fig. 7

Statistical Source Data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Boehme, M., Guzzetta, K.E., Bastiaanssen, T.F.S. et al. Microbiota from young mice counteracts selective age-associated behavioral deficits. Nat Aging 1, 666–676 (2021). https://doi.org/10.1038/s43587-021-00093-9

Download citation

Further reading

Search

Quick links