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Gut microbiota drives age-related oxidative stress and mitochondrial damage in microglia via the metabolite N6-carboxymethyllysine

Nature Neuroscience volume 25, pages 295–305 (2022)Cite this article

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Abstract

Microglial function declines during aging. The interaction of microglia with the gut microbiota has been well characterized during development and adulthood but not in aging. Here, we compared microglial transcriptomes from young-adult and aged mice housed under germ-free and specific pathogen-free conditions and found that the microbiota influenced aging associated-changes in microglial gene expression. The absence of gut microbiota diminished oxidative stress and ameliorated mitochondrial dysfunction in microglia from the brains of aged mice. Unbiased metabolomic analyses of serum and brain tissue revealed the accumulation of N6-carboxymethyllysine (CML) in the microglia of the aging brain. CML mediated a burst of reactive oxygen species and impeded mitochondrial activity and ATP reservoirs in microglia. We validated the age-dependent rise in CML levels in the sera and brains of humans. Finally, a microbiota-dependent increase in intestinal permeability in aged mice mediated the elevated levels of CML. This study adds insight into how specific features of microglia from aged mice are regulated by the gut microbiota.

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Fig. 1: Microbiota orchestrates microglial transcriptome in young and aged mice.
Fig. 2: Microbiota contributes to age-related oxidative stress and mitochondrial dysfunction in microglia.
Fig. 3: Microbiota- and age-associated regulation of serum and brain metabolites.
Fig. 4: CML contributes to microbiota-mediated microglial aging.
Fig. 5: Disruption of gut–blood barrier in aging instigates CML surge.

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Data availability

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 (http://twinsuk.ac.uk/resources-for-researchers/access-our-data/) and requests are going to be reviewed by the TwinsUK Resource Executive Committee regularly. Microglia RNA-seq data are available at the Gene Expression Omnibus under accession no. GSE182719. The mouse genome version mm10 (University of California Santa Cruz) can be viewed at https://www.ncbi.nlm.nih.gov/assembly/GCF_000001635.20/. For microbiome profiling, the FASTA files of two runs with their corresponding mappings are available at https://doi.org/10.6084/m9.figshare.15179775.v1 (ref. 64). 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.

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Acknowledgements

We thank E. Barleon and K. Seidel for helping with IHC and electron microscopy. J. Bodinek-Wersing (Core Facility, University of Freiburg) was responsible for cell sorting. We thank the Metabolomics Core Facility (University of Freiburg) and in particular D. Pan for technical assistance. TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, National Institute for Health Research-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St. Thomas’ NHS Foundation Trust in partnership with King’s College London. We thank C. Moehle and T. Stempfl (University of Regensburg) for technical assistance with the RNA-seq experiments. We thank M. Havermans for providing the BMDMs. We thank A. Dumas for proofreading the manuscript. This study was funded by the Deutsche Forschungsgemeinschaft (DFG) SFB/TRR 167 to T.B., T.L., D.E., R.B. and M.P. D.E. was further supported by the Berta Ottenstein Program for Advanced Clinician Scientists. M.P. was further supported by the Sobek Foundation, Ernst-Jung Foundation, DFG (SFB 992, SFB1160, Reinhart Koselleck Grant, Gottfried Wilhelm Leibniz-Prize), Alzheimer Forschung Initiative and the Ministry of Science, Research and Arts, Baden-Württemberg (‘Neuroinflammation’ specialization). This study was supported by the DFG under Germany’s Excellence Strategy (CIBSS, EXC-2189; project ID 390939984).

Author information

Authors and Affiliations

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

    Omar Mossad, Nikolaos Dokalis, Charlotte Mezö, Lara Susann Nabavi, Melanie Mayer, Feres José Mocayar Maron, Daniel Erny, Marco Prinz & Thomas Blank

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

    Omar Mossad, Nikolaos Dokalis & Charlotte Mezö

  3. Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany

    Bérénice Batut & Rolf Backofen

  4. Maurice Müller Laboratories, Department for Biomedical Research, University Clinic of Visceral Surgery and Medicine, Inselspital, University of Bern, Bern, Switzerland

    Bahtiyar Yilmaz, Mercedes Gomez de Agüero, Andrew J. Macpherson & Stephanie C. Ganal-Vonarburg

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

    Elisa Nent, Joerg M. Buescher & Tim Lämmermann

  6. Ultimate Medicine AG, Dübendorf, Switzerland

    Antal Szalay

  7. Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany

    Rolf Backofen

  8. Berta Ottenstein Program, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Daniel Erny

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

    Marco Prinz

  10. Signalling Research Centers BIOSS and CIBSS, University of Freiburg, Freiburg, Germany

    Marco Prinz

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  1. Omar Mossad
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Contributions

O.M. performed most of the experiments, helped develop the overall concept and wrote the manuscript. B.Y. and S.C.G.V. analyzed the microbiome profile within the fecal samples. M.G.D.A. bred and prepared the mice of the different age groups. B.B. and R.B. performed and designed the analyses of the RNA-seq data. E.N. and J.M.B. performed the targeted metabolomics. L.S.N., M.M., F.J.M.M., C.M. and N.D. provided help with the experiments and experimental design. T.L., A.S., A.J.M., D.E. and M.P. contributed to critical analysis of the data, developed the concepts and provided the reagents. T.B. developed the overall concept of the project, supervised the experiments and wrote the manuscript.

Corresponding author

Correspondence to Thomas Blank.

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Nature Neuroscience thanks John Cryan, Sarkis Mazmanian, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Microglial transcriptional profile from GF and SPF mice of both sexes.

(a) Heatmap of genes (normalized gene counts) specific to different types of immune cells in order to show purity of sorted cells. (b) Heatmap showing sample−to-sample Ward clustering. (c) Heatmap of all genes in the modules eigengenes. Each row is a biological replicate (d) Heatmap of genes in metabolism-associated module eigengene ME10. Z-scores were calculated from normalized counts. Each row is a gene, and each column is a biological replicate; microglia isolated from young-adult and aged SPF (n = 6, 16) and GF (n = 6, 8) mice.

Source data

Extended Data Fig. 2 Gating strategy for flow cytometry and purity of MACS separation.

(a) Cell sorting strategy for RT-qPCR and RNA-seq. (1) Myeloid cells were gated by size and granularity then (2 and 3) only single cells were included. (4) Live and Lineage- cells were gated negative for Fixable Viability Dye eFluor® 780 and CD3, CD19, CD45R, Ly6C, and Ly6G to exclude T cells, B cells, monocytes and granulocytes, respectively. Microglia were gated on CD45Int and CD11b+. (b) Purity of cells used for for cellular ATP assay. Microglial cells were enriched using the CD11b MACS cell separation system (Miltenyi Biotec, USA). (b, left panel) Each dot represents one mouse. Data are presented as mean values + /- SEM.

Source data

Extended Data Fig. 3 Microbiota drives age-related differences in microglial morphology but not in cell density.

(a) Immunohistological detection of Iba-1+ microglia in the cortex of young-adult and aged SPF and GF mice. Scale bar, 20 μm. (b) Diagram summarizing microglia densities in the cortex. SPF (n = 9, 8) and GF (n = 9, 8). (c) Representative three-dimensional reconstruction of cortical microglia of all groups. Scale bar, 10 µm. (d-h) Imaris-based semi-automatic quantification of cell morphology. (d) Total branch length (µm), (e) total branch area (µm2), (f) number of branch points, (g) cell body volume (µm3) and (h) cell body sphericity. Each symbol represents an average of at least four cells measured per mouse. Data represent two independent experiments including young-adult and aged mice. SPF (n = 8, 8) and GF (n = 8, 8). Statistical analysis (b-h) two-way ANOVA followed by Tukey’s post-hoc test (*p < 0.05, **p < 0.01, ***p < 0.001, ns = not significant). Data are presented as mean values + SEM. Exact p-values are reported in the source data.

Extended Data Fig. 4 Age-related mitochondrial physiology in microglia of male and female SPF and GF mice.

(a) Representative electron micrographs of abnormal vs healthy mitochondria in cortical microglia. Scale bar, 600 nm. (b) Quantification of mitochondrial area per microglia. (c) Number of mitochondria per microglia. (b and c) Data were generated from aged SPF and GF mice (n = 8). (d) Hif1a mRNA expression in microglia based on RNA-seq analysis (normalized gene counts). (e) Hif1a mRNA expression by RT-qPCR in microglia of young-adult and aged SPF (n = 8, 8) and GF (n = 7, 10) mice. (f) Mitochondrial mass (MitoTracker Green MFI). (g) Mitochondrial membrane potential (Δψm) (TMRM dye MFI). (h) Quantification of cellular ATP relative to young-adult SPF males. Data were generated from young-adult and aged mice. (f and g) SPF (n = 17, 14) and GF (n = 9, 13). (h) SPF (n = 23, 17) and GF (n = 14, 11). (b-h) Data are presented as mean values + SEM. Statistical analysis (b and c) Mann−Whitney U test (two-sided), and (e-h) two-way ANOVA followed by Tukey’s 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 CML modulates macrophage metabolism.

(a, b) Pathway enrichment analysis for significantly abundant metabolites in serum and brain of aged mice (plotted are the top 15 enriched pathways). Color scale (blue to red), ratio between the number of significant metabolites to the total number of metabolites detected in each pathway. Dot size reflects number of significant metabolites in each pathway. Pathway enrichment analysis was performed automatically using the Metabolon’s client portal. (c) Percentage of healthy vs abnormal mitochondria from total mitochondrial number in cortical microglia of young-adult mice treated with vehicle or CML i.p. (n = 5). (d and e) Bone marrow derived macrophages (BMDMs) were cultured in serum-free medium 6 h before the experiment. Cells were incubated with increasing concentrations of CML for 48 h, before harvesting for measurements. Each dot is a biological replicate (n = 3). (d) Quantification of relative MFI of CellROX probe signals. (e) Mitochondrial activity depicted as mitochondrial membrane potential (Δψm) (TMRM dye MFI) normalized to mitochondrial mass (MitoTracker Green MFI). (f) PCA on transcriptome (normalized gene counts) of microglia isolated from young-adult mice treated with vehicle or CML i.p.. (c-f) Data are presented as mean values + SEM. Each dot represents one mouse. Statistical analysis (c) two-way ANOVA followed by Sidak’s multiple comparisons test, (d and e) one-way ANOVA followed by Dunnett’s post-hoc test (***p < 0.001, ns = not significant). Exact p-values are reported in the source data.

Source data

Extended Data Fig. 6 Age-dependent shift in gut microbiota composition.

(a) PCA plot (beta-diversity) and (b) Shannon and Simpson alpha-diversity indices of gut microbiota. Non-parametric Mann-Whitney U-tests (two-sided) to compare alpha diversity between samples and Adonis from vegan package to assess the effects of groups for beta diversity. (c) Relative abundance of gut microbiota composition profiles at the phylum level in male mice at different ages (each color represents one bacterial phylum). (d) The average Firmicutes/Bacteroidetes ratio (F/R) in the cecal samples. (e) Relative abundance of the family Lachnospiraceae. (a-f) Data from young-adult and aged male mice, housed under SPF conditions (n = 5, 10). Each dot represents data from one animal. (d and e) Data are presented as mean values + SEM. (b and f) 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. (d and e) Statistics with Mann-Whitney U test (two-sided) (f) Relative abundance of differentially abundant genera in aging. Taxonomic differences at phylum and genus levels between tested groups were identified using the “multivariate analysis by linear models” (MaAsLin) R package.

Extended Data Fig. 7 Age-related microglial CML accumulation is gut-mediated.

(a) Targeted metabolomics (LC/MS) on CML translocated into the circulation 4 h post oral gavage in young-adult and aged mice housed under SPF or GF (n = 5). Light purple; before gavage, green; 4 h post gavage. Each dot represents an individual measurement for one mouse. (b-e) Data from young-adult and aged SPF mice injected with vehicle or CML (i.p. or o.g.) (n = 4 each). Each dot represents one mouse. (b) Percentage of CML+ Iba-1+ cells quantified in the cortex. (c) Immunofluorescent labelling of CML (Red), Iba-1 (green) and DAPI (blue) in mouse cortex. Scale bars, 50 µm (overview) and 10 µm (inset). (d) Quantification of relative CellROX probe signal by determining MFI and (e) quantification of relative cellular ATP. (a, b, d and e) Each dot represents one mouse. Data are presented as mean values + SEM. Statistical analysis (b, d and e) two-way ANOVA followed by Tukey’s post-hoc test (*p < 0.05, **p < 0.01, ***p < 0.001, ns = not significant). Exact p-values are reported in the source data.

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Mossad, O., Batut, B., Yilmaz, B. et al. Gut microbiota drives age-related oxidative stress and mitochondrial damage in microglia via the metabolite N6-carboxymethyllysine. Nat Neurosci 25, 295–305 (2022). https://doi.org/10.1038/s41593-022-01027-3

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