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Healthspan and lifespan extension by fecal microbiota transplantation into progeroid mice

Nature Medicine volume 25, pages 1234–1242 (2019)Cite this article

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Abstract

The gut microbiome is emerging as a key regulator of several metabolic, immune and neuroendocrine pathways1,2. Gut microbiome deregulation has been implicated in major conditions such as obesity, type 2 diabetes, cardiovascular disease, non-alcoholic fatty acid liver disease and cancer3,4,5,6, but its precise role in aging remains to be elucidated. Here, we find that two different mouse models of progeria are characterized by intestinal dysbiosis with alterations that include an increase in the abundance of Proteobacteria and Cyanobacteria, and a decrease in the abundance of Verrucomicrobia. Consistent with these findings, we found that human progeria patients also display intestinal dysbiosis and that long-lived humans (that is, centenarians) exhibit a substantial increase in Verrucomicrobia and a reduction in Proteobacteria. Fecal microbiota transplantation from wild-type mice enhanced healthspan and lifespan in both progeroid mouse models, and transplantation with the verrucomicrobia Akkermansia muciniphila was sufficient to exert beneficial effects. Moreover, metabolomic analysis of ileal content points to the restoration of secondary bile acids as a possible mechanism for the beneficial effects of reestablishing a healthy microbiome. Our results demonstrate that correction of the accelerated aging-associated intestinal dysbiosis is beneficial, suggesting the existence of a link between aging and the gut microbiota that provides a rationale for microbiome-based interventions against age-related diseases.

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Fig. 1: Gut dysbiosis in LmnaG609G/G609G and Zmpste24−/− progeroid mice.
Fig. 2: Alterations in the gut microbiome in progeria patients and long-lived humans.
Fig. 3: Effects of fecal microbiota transplantation in progeroid and WT mice.
Fig. 4: A. muciniphila supplementation in progeroid mice and metabolomic analysis of ileal content.

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Data availability

Sequence data supporting these findings have been deposited in EGA (https://ega-archive.org/) under accession number EGAS00001003656. Metabolomics data are provided in Supplementary Table 5. Any additional data generated and analyzed in this study are available from the corresponding authors upon reasonable request.

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Acknowledgements

The authors thank M. Stamsnijder and the Progeria Family Circle, and all the participants that have kindly engaged in our study. The authors also thank G. Velasco, Y. Español, A.R. Folgueras, X.M. Caravia, J.M. Fraile, I. Varela, M. Mittelbrunn and K. Iribarren for helpful comments and advice; R. Feijoo, A. Moyano, D.A. Puente, S.A. Miranda, M.S. Pitiot, V. García de la Fuente and M.C. Muñiz for excellent technical assistance; M. Gueimonde and C.G. de los Reyes-Gavilán (IPLA-CSIC) for providing A. muciniphila; C. Mayolas for help with logistic organization. The authors acknowledge generous support by J.I. Cabrera. A.M.N. is a recipient of a FPI predoctoral contract and N.S. is supported by a Juan de la Cierva postdoctoral contract, both granted by Ministerio de Economia y Competividad (MINECO). A.L.'s research on aging is funded by Fondo de Investigaciones Sanitarias and Fondos FEDER (PI15/00558). The metabolomics platform is supported by H2020 European Union project OncoBiome. G.K. is supported by Ligue contre le Cancer (équipe labellisée); Agence National de la Recherche (ANR) – Projets blancs; ANR under the frame of E-Rare-2, the ERA-Net for Research on Rare Diseases; Association pour la recherche sur le cancer (ARC); Cancéropôle Ile-de-France; Chancelerie des universités de Paris (Legs Poix), Fondation pour la Recherche Médicale (FRM); a donation by Elior; European Research Area Network on Cardiovascular Diseases (ERA-CVD, MINOTAUR); Gustave Roussy Odyssea; the European Union Horizon 2020 Project Oncobiome; Fondation Carrefour; Institut National du Cancer (INCa); Inserm (HTE); Institut Universitaire de France; LeDucq Foundation; the LabEx Immuno-Oncology; the RHU Torino Lumière; the Seerave Foundation; the SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE); the SIRIC Cancer Research and Personalized Medicine (CARPEM). The Instituto Universitario de Oncología is supported by Fundación Bancaria Caja de Ahorros de Asturias. J.M.P.F. is supported by Ministerio de Economía y Competitividad-FEDER and Gobierno del Principado de Asturias. C.L.-O. is supported by grants from the European Research Council (DeAge, ERC Advanced Grant), Ministerio de Economía y Competitividad-FEDER, Instituto de Salud Carlos III (RTICC) and Progeria Research Foundation.

Author information

Authors and Affiliations

  1. Departamento de Bioquímica y Biología Molecular, Facultad de Medicina, Instituto Universitario de Oncología (IUOPA), Universidad de Oviedo, Oviedo, Spain

    Clea Bárcena, Rafael Valdés-Mas, Pablo Mayoral, Cecilia Garabaya, Francisco Rodríguez, José M. P. Freije, Pedro M. Quirós & Carlos López-Otín

  2. Cell Biology and Metabolomics Platforms, Gustave Roussy Cancer Campus, Villejuif, France

    Sylvère Durand, Noélie Bossut, Fanny Aprahamian & Guido Kroemer

  3. Equipe 11 labellisée par la Ligue contre le Cancer, Centre de Recherche des Cordeliers, Paris, France

    Sylvère Durand, Noélie Bossut, Fanny Aprahamian & Guido Kroemer

  4. INSERM, U1138, Paris, France

    Sylvère Durand, Noélie Bossut, Fanny Aprahamian & Guido Kroemer

  5. Université Paris Descartes, Sorbonne Paris Cité, Paris, France

    Sylvère Durand, Noélie Bossut, Fanny Aprahamian & Guido Kroemer

  6. Unidad de histopatología molecular, IUOPA, Universidad de Oviedo, Oviedo, Spain

    María Teresa Fernández-García

  7. Department of Microbiology and Biochemistry of Dairy Products, Instituto de Productos Lácteos de Asturias, Consejo Superior de Investigaciones Científicas (IPLA-CSIC), Villaviciosa, Spain

    Nuria Salazar & Alicja M. Nogacka

  8. Diet, Microbiota and Health Group, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain

    Nuria Salazar & Alicja M. Nogacka

  9. Faculty of Health and Sport Sciences, Department of Physiatry and Nursing, University of Zaragoza, Huesca, Spain

    Nuria Garatachea

  10. GENUD (Growth, Exercise, NUtrition and Development) Research Group, University of Zaragoza, Zaragoza, Spain

    Nuria Garatachea

  11. Faculty of Sport Science, Universidad Europea de Madrid, Madrid, Spain

    Alejandro Lucia

  12. Instituto de Investigación Hospital 12 de Octubre (i+12) y Centro de Investigación Biomédica en Red de Fragilidad y Enjevecimiento Saludable (CIBERFES), Madrid, Spain

    Alejandro Lucia

  13. Université Pierre et Marie Curie, Paris, France

    Guido Kroemer

  14. Pôle de Biologie, Hôpital Européen Georges Pompidou, AP-HP, Paris, France

    Guido Kroemer

  15. Karolinska Institute, Department of Women’s and Children’s Health, Karolinska University Hospital, Stockholm, Sweden

    Guido Kroemer

  16. Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain

    José M. P. Freije, Pedro M. Quirós & Carlos López-Otín

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  1. Clea Bárcena
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Contributions

C.B., J.M.P.F., P.M.Q. and C.L.-O. conceived and designed the experiments. R.V.-M., P.M.Q. and C.B. performed bioinformatics analysis and results interpretation. C.B., P.M., C.G., F.R. and P.M.Q. performed experiments and analyzed data. M.T.F.-G. performed histopathological analysis. N.S. and A.M.N. performed the culturing and bacterial solutions of A. muciniphila. N.G. and A.L. organized and carried out pickup of human centenarian samples. S.D., N.B., F.A. and G.K. performed LC-MS-based metabolomics. C.B., J.M.P.F., P.M.Q. and C.L.-O. wrote the manuscript. A.L. and G.K. assisted with manuscript editing and all authors revised the manuscript and provided input.

Corresponding authors

Correspondence to Pedro M. Quirós or Carlos López-Otín.

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Competing interests

G.K. is one of the scientific co-founders of everImmune.

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Peer review information: Michael Basson and Randy Levinson were the primary editors on this Article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Biodiversity in wild-type (WT) and LmnaG609G/G609G mice at different ages.

a, Scheme representing the microbiome profiling experiment in LmnaG609G/G609G mice. b,c, Comparison of alpha-diversity of gut microbiota among all groups of mice using Shannon’s index (diversity) (b) and the Chao1 index (richness) (c). Analyses were performed using Kruskal–Wallis test (Supplementary Table 1). In the box plots, upper and lower hinges correspond to the first and third quartiles, the center line represents the median, whiskers indicate the highest and lowest values that are within 1.5 × IQR and data beyond the end of the whiskers are outliers and plotted as points. d, Principal coordinates analysis (PCoA) of beta-diversity using the Jaccard distance metric among samples of the five groups analyzed (P < 0.001; PERMANOVA) (Supplementary Table 1). e,f, UPGMA trees representing the beta-diversity analysis of all groups analyzed using the Bray–Curtis dissimilarity (e) and the Jaccard distance metrics (f). For a–e, WT 1mo, n = 6; G609G 1mo, n = 5; WT 4mo, n = 8; G609G 4mo, n = 9; WT 22mo, n = 8.

Extended Data Fig. 2 Statistically significant differences in the gut microbiome of LmnaG609G/G609G versus wild-type (WT) mice at different ages.

a, Total relative abundance of prevalent microbiota at the class level in the five groups studied: 1-month-old WT (n = 6), 1-month-old LmnaG609G/G609G (n = 5), 4-month-old WT (n = 8), 4-month-old LmnaG609G/G609G (n = 9) and 22-month-old WT (n = 8), showing data for individual mice. The low-abundance bacteria group includes all bacterial classes with less than 0.5% of total abundance. b, Results of LEfSe analysis showing bacteria that were significantly different in abundance in progeroid (blue, n = 5) versus WT (violet, n = 6) mice at 1 month of age. c, Results of LEfSe analysis showing bacteria that were significantly different in abundance between WT mice at 1 (violet, n = 6) and 4 (grey, n = 8) months of age. d, Results of LEfSe analysis showing bacteria that were significantly different in abundance between progeroid mice at 1 (blue, n = 5) and 4 (red, n = 9) months of age. e, Results of LEfSe analysis showing bacteria that were significantly different in abundance between WT mice at 4 (grey, n = 8) and 22 (green, n = 8) months of age. f, Results of LEfSe analysis of PICRUSt-HUMAnN2, showing the top five KEGG pathways (based on P value) significantly upregulated in WT and G609G at 4 months (Supplementary Table 3). p, phylum; c, class; o, order; f, family; g, genus. For a–f, analyses were performed with WT 1mo, n = 6; G609G 1mo, n = 5; WT 4mo, n = 8; G609G 4mo, n = 9; WT 22mo, n = 8.

Extended Data Fig. 3 Gut microbiome alteration in progeroid Zmpste24−/− mice.

a, Scheme representing the microbiome profiling experiment in Zmpste24−/− mice. b,c, Comparison of alpha-diversity of gut microbiota between WT and Zmpste24−/− mice using Shannon’s index (diversity) (b) and the Chao1 index (richness) (c). Analyses were performed using the Kruskal–Wallis test (Supplementary Table 1). In the box plots, upper and lower hinges correspond to the first and third quartiles, the center line represents the median, whiskers expand to the highest and lower values that are within 1.5 × IQR and data beyond the end of the whiskers are outliers and plotted as points. d, PCoA of beta-diversity using the Jaccard distance metric between WT and Zmpste24−/− mice (P < 0.002; PERMANOVA). e, Total relative abundance of prevalent microbiota at the class level in Zmpste24−/− and WT mice showing results from individual mice. The low abundance bacteria group includes all bacterial classes with less than 0.5% of total abundance. p, phylum; c, class. For a–e, analyses were performed with WT (n = 8) and Zmpste24−/− (n = 4) mice.

Extended Data Fig. 4 Gut microbiome alteration in human progeria samples.

a, Scheme representing the microbiome profiling experiment in Hutchinson–Gilford (HGPS) and Néstor–Guillermo (NGPS) progeria syndromes. b,c, Comparison of alpha-diversity of gut microbiota using the Shannon’s index (diversity) (b) and the Chao1 index (richness) (c) between progeria patients and healthy siblings. Analyses were performed using the Kruskal–Wallis test (Supplementary Table 1). In the box plots, upper and lower hinges correspond to the first and third quartiles, the center line represents the median, whiskers indicate the highest and lowest values that are within 1.5 × IQR and data beyond the end of the whiskers are outliers and plotted as points. d,e, PCoA of beta-diversity using the Jaccard distances metric among samples analyzed by health status (P = 0.75, PERMANOVA) (d) and by family (P = 0.001, PERMANOVA) (e) (Supplementary Table 1). f, Total relative abundance of prevalent microbiota at the class level in progeria patients and their corresponding healthy relatives, grouped by family and disease condition. The low-abundance bacteria group includes all bacterial classes with less than 0.5% of total abundance. p, phylum; c, class. For a–f, analyses were performed on family A, n = 3 individuals; family B, n = 3 individuals; family C, n = 3 individuals; family D, n = 2 individuals; family E, n = 3 individuals; control (C), n = 9 individuals, progeria (P), n = 5 individuals.

Extended Data Fig. 5 Gut microbiome alteration in human samples from centenarians and healthy controls (adults aged 30–50 years).

a, Scheme representing the microbiome profiling experiment of centenarians and healthy controls. b,c, Comparison of alpha-diversity using Shannon’s index (diversity; P = 0.08) (b) and the Chao1 index (richness; P = 0.00079) (c) in centenarians compared to healthy controls. Analyses were performed with the Kruskal–Wallis test (Supplementary Table 1). In the box plots, upper and lower hinges correspond to the first and third quartiles, the center line represents the median, whiskers indicate the highest and lowest values that are within 1.5 × IQR and data beyond the end of the whiskers are outliers and plotted as points. d, PCoA of beta-diversity using the Jaccard distance metric between centenarians (Ce) and healthy controls (HC) (P < 0.001; PERMANOVA). e, Total relative abundance of prevalent microbiota at the class level in healthy controls (HC) and centenarians (Ce) showing data for each individual. The low-abundance bacteria group includes all bacterial classes with less than 0.5% of total abundance. p, phylum; c, class. For a–e, analyses were performed on centenarians (Ce), n = 17 individuals, and healthy adult controls (HC), n = 14 individuals.

Extended Data Fig. 6 LmnaG609G/G609G mice transplanted with wild-type (WT) gut microbiota show an improved healthspan and lifespan.

a, Average relative abundance of prevalent microbiota at the class level in the FMT experiments, comparing age-matched controls and transplanted mice (n = 8, 4 males and 4 females, in all groups) and donor mice (n = 4, pooled samples from male and female mice). The low-abundance bacteria group includes all bacterial classes representing less than 0.1% of the total. Transplants began at ~6–8 weeks of age and metagenome profiling was analyzed after four rounds of FMT (2 weeks after microbiota depletion). Donor mice, both WT and LmnaG609G/G609G, were ~18–20 weeks old. p, phylum; c, class. b, UPGMA trees representing the beta-diversity analysis of control, transplanted and donor mice in each experimental condition, using Bray–Curtis dissimilarity (Supplementary Table 1). c, Comparison of spleen weight between control WT (n = 4), LmnaG609G/G609G (n = 4), LmnaG609G/G609G mice transplanted with WT microbiota (G609G-WTmic; n = 4) and LmnaG609G/G609G mice transplanted with microbiota from older LmnaG609G/G609G mice (G609G-oG609Gmic n = 4). Analyses were performed with one-way ANOVA with Sidak’s multiple comparison test. Exact adjusted P values are represented within the plot. d, mRNA relative levels (in log10 scale) of inflammatory interleukins in the ileum of WT (n = 9), G609G-control (n = 9), G609G-WTmic (n = 4) and G609G-oG609Gmic (n = 4) mice. Differences were analyzed with ANOVA with Sidak's multiple comparisons test relative to G609G-control mice. Exact adjusted P values are represented within the plot. For c and d, in the box plots, upper and lower hinges correspond to the first and third quartiles, the center line represents the median, whiskers indicate the highest and lowest values that are within 1.5 × IQR, and data beyond the end of the whiskers are outliers and plotted as points. e, Comparison of the number of mice dead and alive between LmnaG609G/G609G (Ctrl; n = 11) and LmnaG609G/G609G mice transplanted with WT microbiota (WTmic; n = 11) at the 80th percentile of the overall survival. Differences were analyzed by one-sized Fisher’s exact test (P = 0.04). f, Percentage survival of LmnaG609G/G609G (n = 11) and LmnaG609G/G609G mice transplanted with buffer (LmnaG609G-EmptyT; n = 8). Transplants started at ~8–10 weeks of age. Differences were analyzed with the log-rank Mantel–Cox test and BH correction was applied after pairwise comparisons between all experimental groups, including those described in Fig. 3f (P = 0.76). Hazard ratio (HR) was calculated using a Cox proportional model (HR of 1.7 [95% confidence interval (CI) 0.64–4.88], P = 0.27). Median and maximal survival are indicated in the Kaplan–Meier plot.

Extended Data Fig. 7 WT mice transplanted with progeroid microbiota show metabolic alterations.

a, Comparison of the body weight between female WT mice transplanted with progeroid microbiota (WT-oG609Gmic; n = 7) and female WT controls (n = 7) over the indicated time period. Differences of body weight over the time were assessed with a linear mixed model and analyzed with an ANOVA type II Wald χ2 test (χ2 = 15.98, d.f. = 1, P = 6.3 × 10−5). Data are presented as mean ± s.e.m. b–d, Metabolic parameters, measured with an Oxymax system, of WT (n = 14; 7 males and 7 females) and WT-oG609Gmic (n = 14; 7 males and 7 females) mice. Comparison of O2 consumption (b), CO2 production (c) and energy expenditure (d) over the indicated period of time. Smoothing curves represent the means of all individuals in each group and shaded areas represent 95% CI.

Extended Data Fig. 8 Zmpste24−/− female mice transplanted with wild-type (WT) gut microbiota show an improved phenotype and extended maximal survival.

a, Comparison of the body weight between control Zmpste24−/− mice (n = 7) and Zmpste24−/− mice transplanted with WT microbiota (Zmpste24−/−-WTmic, n = 7) over the indicated time period. Differences in body weight over the time were assessed with a linear mixed model and analyzed with an ANOVA type II Wald χ2 test (χ2 = 10.52, d.f. = 1, P = 0.0011). b, Comparison of the number of mice dead and alive between Zmpste24−/− (Ctrl; n = 7) and Zmpste24−/− transplanted with WT microbiota (WTmic; n = 7) at the 80th percentile of overall survival. Differences were analyzed by one-sized Fisher’s exact test (P = 0.09).

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Bárcena, C., Valdés-Mas, R., Mayoral, P. et al. Healthspan and lifespan extension by fecal microbiota transplantation into progeroid mice. Nat Med 25, 1234–1242 (2019). https://doi.org/10.1038/s41591-019-0504-5

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