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Microbiota-dependent increase in δ-valerobetaine alters neuronal function and is responsible for age-related cognitive decline

Nature Aging volume 1, pages 1127–1136 (2021)Cite this article

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Abstract

Understanding the physiological origins of age-related cognitive decline is of critical importance given the rising age of the world’s population1. Previous work in animal models has established a strong link between cognitive performance and the microbiota2,3,4,5, and it is known that the microbiome undergoes profound remodeling in older adults6. Despite growing evidence for the association between age-related cognitive decline and changes in the gut microbiome, the mechanisms underlying such interactions between the brain and the gut are poorly understood. Here, using fecal microbiota transplantation (FMT), we demonstrate that age-related remodeling of the gut microbiota leads to decline in cognitive function in mice and that this impairment can be rescued by transplantation of microbiota from young animals. Moreover, using a metabolomic approach, we found elevated concentrations of δ-valerobetaine, a gut microbiota-derived metabolite, in the blood and brain of aged mice and older adults. We then demonstrated that δ-valerobetaine is deleterious to learning and memory processes in mice. At the neuronal level, we showed that δ-valerobetaine modulates inhibitory synaptic transmission and neuronal network activity. Finally, we identified specific bacterial taxa that significantly correlate with δ-valerobetaine levels in the brain. Based on our findings, we propose that δ-valerobetaine contributes to microbiota-driven brain aging and that the associated mechanisms represent a promising target for countering age-related cognitive decline.

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Fig. 1: Microbiota transplant from young mice reinstates microbiota composition and rescues memory deficits in aged mice.
Fig. 2: δ-valerobetaine, an age-associated microbiota-derived metabolite, is modulated in serum and brain by microbiota transplantations.
Fig. 3: Specific bacterial taxa correlate with δ-valerobetaine levels in the brain.
Fig. 4: Injection of δ-valerobetaine recapitulates age-associated memory deficits and diminishes the effects of microbiota from young mice.

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Data availability

Microbiome profiling (16S rRNA-seq) raw data are accessible at the European Nucleotide Archive under accession number PRJEB46041. Data on TwinsUK twin participants are available to bona fide researchers under managed access due to governance and ethical constraints. Raw data should be requested via the website (http://twinsuk.ac.uk/resources-for-researchers/access-our-data/), and requests are reviewed by the TwinsUK Resource Executive Committee regularly. Other data that support the findings of this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

R scripts used for microbiome profiling are available online at https://github.com/OmarMossad/Mossad_FMT_NatAging.

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Acknowledgements

T.B., O.M., D.E., T.L., E.N. and M.P. were supported by the DFG (SFB/TRR167 ‘NeuroMac’). M.P. was further supported by the Sobek Foundation, the Ernst-Jung Foundation, the DFG (SFB 992, SFB1160, Reinhart Koselleck grant, Gottfried Wilhelm Leibniz prize), the Alzheimer Forschung Initiative e.V. (AFI) and the Ministry of Science, Research and Arts, Baden-Wuerttemberg (Sonderlinie ‘Neuroinflammation’). This study was supported by the DFG under Germany’s Excellence Strategy (CIBSS, EXC-2189, project ID-390939984). We are grateful to M. Gomez De Agüero and S.C. Ganal-Vonarburg (Macpherson laboratory, Bern, Switzerland) for providing us with GF mice. TwinsUK is funded by the Wellcome Trust, the Medical Research Council, the European Union, the National Institute for Health Research-funded BioResource, the Clinical Research Facility and the Biomedical Research Centre based at Guy’s and St. Thomas’ NHS Foundation Trust in partnership with King’s College London. We are grateful to Plexxikon for providing the CSF1R inhibitor PLX5622. We thank A. Dumas for proofreading the article.

Author information

Authors and Affiliations

  1. Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Omar Mossad, Daniel Erny, Marco Prinz & Thomas Blank

  2. Faculty of Biology, University of Freiburg, Freiburg, Germany

    Omar Mossad & Shani Folschweiller

  3. Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany

    Elisa Nent, Joerg M. Buescher & Tim Lämmermann

  4. Institute for Medical Microbiology and Hospital Epidemiology, Hannover Medical School, Hannover, Germany

    Sabrina Woltemate & Marius Vital

  5. Institute of Physiology I, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Shani Folschweiller, Marlene Bartos & Jonas F. Sauer

  6. Institute of Virology, Medical Center University of Freiburg, Freiburg, Germany

    Daniel Schnepf & Peter Staeheli

  7. Spemann Graduate School of Biology and Medicine (SGBM), Albert-Ludwigs University Freiburg, Freiburg, Germany

    Daniel Schnepf

  8. Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Daniel Erny & Peter Staeheli

  9. Ultimate Medicine AG, Dübendorf, Switzerland

    Antal Szalay

  10. Max-von-Pettenkofer Institute, LMU Munich, Munich, Germany

    Bärbel Stecher

  11. German Center for Infection Research (DZIF), partner site LMU Munich, Munich, Germany

    Bärbel Stecher

  12. Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany

    Marco Prinz

  13. Center for NeuroModulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Marco Prinz

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Contributions

O.M. conducted the majority of experiments and data analysis; E.N., T.L. and J.M.B. performed targeted metabolomics; S.W. and M.V. performed microbiome profiling; S.F. and J.F.S. performed electrophysiological recordings; D.S. and P.S. contributed to the FMT experimental paradigm; J.F.S., D.E., T.L., B.S., A.S., M.B. and M.P. designed experiments and interpreted results; T.B. initiated and conceived the project and designed experiments; O.M. and T.B. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Thomas Blank.

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Competing interests

T.B. is an author on a patent application, which is pending. All authors declare no other competing interests.

Additional information

Peer review information Nature Aging thanks Sven Pettersson, Pieter Dorrestein 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 Effects of antibiotics and fecal microbiota transplantation.

(a) Gating on DAPI- Syto9+ live bacteria of fecal samples from SPF and ABX-treated mice. Representative plot for the flow cytometry gating. (b) Percentage of live Gram+ and Gram- bacteria and (c) Quantification of live bacteria per mg fecal sample. (d) NMDS plot of Beta diversity (Bray-Curtis distance matrix) of microbiota in fecal pellets. Samples collected from start and end of the experiments are shown as triangles and circles, respectively. (e) Abundance of cecal microbiota composition profiles at the phylum level (each color represents one bacterial phylum). (f, g) Alpha diversity in cecum measured by (f) Shannon and (g) Simpson indices. Box plots; centre = median, upper and lower “hinges” correspond to the first and third quartiles (the 25th and 75th percentiles), upper whisker extends from the hinge to the highest value that is within 1.5 * IQR of the hinge, and lower whisker extends from the hinge to the lowest value within 1.5 * IQR of the hinge, where IQR is the inter-quartile range, or distance between the first and third quartiles. Statistical analysis with (b, c) Mann–Whitney U test (two-tailed) (f, g) Wilcoxon test followed by Holm post-hoc test (two-tailed) (*p < 0.05, **p < 0.01, ***p < 0.001, ns = not significant). Exact p-values are reported in the source data.

Source data

Extended Data Fig. 2 Age-associated microbiota alterations on genus level.

(a,b) Differentially abundant taxa in (a) aged vs young animals, and (b) oFMTiAged vs yFMTiAged animals with Padj<0.05. Each dot represents one ASV. Genus classification is depicted on y-axis, and log2FoldChange on x-axis. Taxa with unclassified genus were filtered out. Color represents different phyla.

Extended Data Fig. 3 δ-Valerobetaine enhances inhibitory postsynaptic currents and population synchronization in the medial prefrontal cortex.

(a) Dose-dependent increase in δ-valerobetainein the brain represented by area under the curve (AUC) of the mass-spec peaks.Error bars represent SEM. (b) T-maze (c,d) Familiarization (c) and Testing sessions (d) with a 6 h gap for the Novel object recognition test (NOR) (n=5). Statistical analysis with one-way ANOVA compared to the control group (0 mg/kg) followed by Bonferroni post-hoctest (*p < 0.05, **p < 0.01, ***p < 0.001). (e) Examples of spontaneous excitatory postsynaptic currents (EPSCs, left) and spontaneous inhibitory postsynaptic currents (IPSCs, right) of prefrontal pyramidal cells under control conditions (black) and after pre-incubation with 10 µM δ-valerobetaine (green). Data arefrom patch-clamp recordings in acute brain slices. (f) δ-valerobetaine increased the frequency (p=0.006, unpairedt-test) but not the amplitude of spontaneous IPSCs (p=0.174, unpaired t-test) while EPSC properties were unaltered (p=0.089 and p=0.641 for frequency andamplitude, respectively). n=10 control and 9 δ-valerobetaine-treated neurons. (a-d,f) Error bars represent SEM (g) Single unit recordings from them PFC to assess the effect of δ-valerobetaine on population activity in vivo. Example traces show a shortsegment of the summed population firing rate from all units of one recordingsession (black, binned in 100 ms time epochs, see Methods) and the binned spikerate of one neuron (bottom) before (left) and after (right) δ-valerobetaineinjection. Note that the unit showed increased coupling to the populationactivity upon δ-valerobetaine injection, indicating enhanced synchronization inthe prefrontal network. (h) Quantification of spike rates and population coupling of all neurons revealedno effect of δ-valerobetaine on average spike frequency but a significant increase in population coupling of the recorded cells (control: n=77 units from 4 mice, δ-valerobetaine: n=56 units from 4, Welch’s tests). Black bars indicatemeans. Statistical analysis (f,h) with Mann–Whitney U test (two-tailed)(**p < 0.01, ns = not significant).

Source data

Extended Data Fig. 4 Beneficial cognitive effect of young FMT is independent of microglia.

(a) Schematic diagram of the experimental setting: as in (Fig.1a) with the addition of the mice receiving either control AIN-76A standard chow (NC) or supplemented with PLX5622 (PLX, 1200 mg/kg) to deplete CSF1R-dependent cells, including microglia. (b) Representative immunofluorescence images of Iba-1+ (red), DAPI (blue) in cortex and hippocampus of PLX5622-treated mice upon sacrifice after behavioral testing. Scale bar, 150μm. (c) Quantification of Iba1+ cell density in cortex and hippocampus as one ROI upon PLX5622 treatment. Error bars represent SEM. (d)T-maze (e,f) Familiarization (e) and Testing sessions (f) with a 6 h gap for Novel object recognition test (NOR) (young (NC), aged (NC), aged (PLX), yFMTiAged (NC), yFMTiAged (PLX), oFMTiAged; n=4, 7, 8, 7, 8, 7, respectively). (a-f) Data represent two independent experiments. Errorbars represent SEM. Statistical analysis with (c-f) one-way ANOVA followed by Tukey post-hoc test (*p < 0.05, **p < 0.01, ***p < 0.001, ns = not significant). Exact p-values are reported in the source data.

Source data

Extended Data Fig. 5 Graphical abstract.

Heterochronic microbiota transplantation ameliorates the age-related cognitive decline through modulating the levels of the microbiota-dependent metabolite, δ-valerobetaine.

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Mossad, O., Nent, E., Woltemate, S. et al. Microbiota-dependent increase in δ-valerobetaine alters neuronal function and is responsible for age-related cognitive decline. Nat Aging 1, 1127–1136 (2021). https://doi.org/10.1038/s43587-021-00141-4

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